diff --git a/3.22/core-functionality/canonical-transcripts/index.html b/3.22/core-functionality/canonical-transcripts/index.html index 15c9f747..f745714a 100644 --- a/3.22/core-functionality/canonical-transcripts/index.html +++ b/3.22/core-functionality/canonical-transcripts/index.html @@ -6,13 +6,13 @@ Canonical Transcripts | IlluminaConnectedAnnotations - - + +
-
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Version: 3.22

Canonical Transcripts

Overview

One of the more polarizing topics within annotation is the notion of canonical transcripts. Because of alternative splicing, we often have several transcripts for each gene. In the human genome, there are an average of 3.4 transcripts per gene (Tung, 2020). As scientists, we seem to have a need for identifying a representative example of a gene - even if there's no biological basis for the motivation.

Golden Helix Blog

A few years ago, the guys over at Golden Helix wrote an excellent post about the pitfalls and issues surrounding the identification of canonical transcripts: What’s in a Name: The Intricacies of Identifying Variants.

In Illumina Connected Annotations, we wanted to identify an algorithm for determining the canonical transcript and apply it consistently to all of our transcript data sources.

Known Algorithms

UCSC

UCSC publishes a list of canonical transcripts in its knownCanonical table which is available via the TableBrowser. Of the RefSeq data sources, it was the only one we could find that provided canonical transcripts:

The canonical transcript is defined as either the longest CDS, if the gene has translated transcripts, or the longest cDNA.

If you were to implement this and compare it with the knownCanonical table, you would see a lot of exceptions to the rule.

Ensembl

The Ensembl glossary states:

The canonical transcript is used in the gene tree analysis in Ensembl and does not necessarily reflect the most biologically relevant transcript of a gene. For human, the canonical transcript for a gene is set according to the following hierarchy:

  1. Longest CCDS translation with no stop codons.
  2. If no (1), choose the longest Ensembl/Havana merged translation with no stop codons.
  3. If no (2), choose the longest translation with no stop codons.
  4. If no translation, choose the longest non-protein-coding transcript.

ACMG

From the ACMG Guidelines for the Interpretation of Sequence Variants:

A reference transcript for each gene should be used and provided in the report when describing coding variants. The transcript should represent either the longest known transcript and/or the most clinically relevant transcript.

ClinVar

From the ClinVar paper:

When there are multiple transcripts for a gene, ClinVar selects one HGVS expression to construct a preferred name. By default, this selection is based on the first reference standard transcript identified by the RefSeqGene/LRG (Locus Reference Genomic) collaboration.

Unified Approach

Our approach is almost identical to the one Golden Helix discussed in their article:

  1. If we're looking at RefSeq, only consider NM & NR transcripts as candidates for canonical transcripts.
  2. Sort the transcripts in the following order:
    1. Locus Reference Genomic (LRG) entries occur before non-LRG entries
    2. Descending CDS length
    3. Descending transcript length
    4. Ascending accession number
  3. Grab the first entry
- - +
Skip to main content
Version: 3.22

Canonical Transcripts

Overview

One of the more polarizing topics within annotation is the notion of canonical transcripts. Because of alternative splicing, we often have several transcripts for each gene. In the human genome, there are an average of 3.4 transcripts per gene (Tung, 2020). As scientists, we seem to have a need for identifying a representative example of a gene - even if there's no biological basis for the motivation.

Golden Helix Blog

A few years ago, the guys over at Golden Helix wrote an excellent post about the pitfalls and issues surrounding the identification of canonical transcripts: What’s in a Name: The Intricacies of Identifying Variants.

In Illumina Connected Annotations, we wanted to identify an algorithm for determining the canonical transcript and apply it consistently to all of our transcript data sources.

Known Algorithms

UCSC

UCSC publishes a list of canonical transcripts in its knownCanonical table which is available via the TableBrowser. Of the RefSeq data sources, it was the only one we could find that provided canonical transcripts:

The canonical transcript is defined as either the longest CDS, if the gene has translated transcripts, or the longest cDNA.

If you were to implement this and compare it with the knownCanonical table, you would see a lot of exceptions to the rule.

Ensembl

The Ensembl glossary states:

The canonical transcript is used in the gene tree analysis in Ensembl and does not necessarily reflect the most biologically relevant transcript of a gene. For human, the canonical transcript for a gene is set according to the following hierarchy:

  1. Longest CCDS translation with no stop codons.
  2. If no (1), choose the longest Ensembl/Havana merged translation with no stop codons.
  3. If no (2), choose the longest translation with no stop codons.
  4. If no translation, choose the longest non-protein-coding transcript.

ACMG

From the ACMG Guidelines for the Interpretation of Sequence Variants:

A reference transcript for each gene should be used and provided in the report when describing coding variants. The transcript should represent either the longest known transcript and/or the most clinically relevant transcript.

ClinVar

From the ClinVar paper:

When there are multiple transcripts for a gene, ClinVar selects one HGVS expression to construct a preferred name. By default, this selection is based on the first reference standard transcript identified by the RefSeqGene/LRG (Locus Reference Genomic) collaboration.

Unified Approach

Our approach is almost identical to the one Golden Helix discussed in their article:

  1. If we're looking at RefSeq, only consider NM & NR transcripts as candidates for canonical transcripts.
  2. Sort the transcripts in the following order:
    1. Locus Reference Genomic (LRG) entries occur before non-LRG entries
    2. Descending CDS length
    3. Descending transcript length
    4. Ascending accession number
  3. Grab the first entry
+ + \ No newline at end of file diff --git a/3.22/core-functionality/gene-fusions/index.html b/3.22/core-functionality/gene-fusions/index.html index 1703fa18..916d85d0 100644 --- a/3.22/core-functionality/gene-fusions/index.html +++ b/3.22/core-functionality/gene-fusions/index.html @@ -6,14 +6,14 @@ Gene Fusion Detection | IlluminaConnectedAnnotations - - + +
Skip to main content
Version: 3.22

Gene Fusion Detection

Overview

Gene fusions often result from large genomic rearrangements such as structural variants. While WGS secondary analysis pipelines typically contain alignment and variant calling stages, very few of them contain dedicated gene fusion callers. When they are included, they are usually associated with RNA-Seq pipelines where gene fusions can be readily observed.

Since gene fusions are frequently observed in cancer and since many sequencing experiments do not include paired RNA-Seq data, we have added gene fusion detection and annotation to Illumina Connected Annotations.

The rich diversity in gene fusion architectures and their likely mechanisms can be seen below:

Publication

Kumar-Sinha, C., Kalyana-Sundaram, S. & Chinnaiyan, A.M. Landscape of gene fusions in epithelial cancers: seq and ye shall find. Genome Med 7, 129 (2015)

Approach

Illumina Connected Annotations uses structural variant calls to evaluate if they form either putative intra-chromosomal or inter-chromosomal gene fusions. Let's consider two transcripts, NM_014206.3 (TMEM258) and NM_013402.4 (FADS1). Both of these genes are on the reverse strand in the genome. The vertical bar indicates the breakpoint where these transcripts are fused:

TMEM258 & FADS1 transcripts

The above explains where the transcripts are fused together, but it doesn't explain in which orientation. By using the directionality encoded in the translocation breakend, we can rearrange these two transcripts in four ways:

TMEM258 & FADS1 gene fusions

Only two of the combinations yields a fusion containing both the transcription start site (TSS) and the stop codon. In one case, we can even detect an in-frame gene fusion. -If only unidirectional gene fusions are desired, only these two fusions can be detected. If enable-bidirectional-fusions is enabled, all four cases can be identified.

Interpreting translocation breakends

At first glance, translocation breakends are a bit daunting. However, once you understand how they work, they're actually quite simple. For more information, we recommend reading section 5.4 in the VCF 4.2 specification.

REFALTMeaning
st[p[piece extending to the right of p is joined after t
st]p]reverse comp piece extending left of p is joined after t
s]p]tpiece extending to the left of p is joined before t
s[p[treverse comp piece extending right of p is joined before t

Variant Types

Specifically we can identify gene fusions from the following structural variant types:

  • deletions (<DEL>)
  • tandem_duplications (<DUP:TANDEM>)
  • inversions (<INV>)
  • translocation breakpoints (AAAAAAAAAAAAAAAAAATTAGTCAGGCAC[chr3:153444911[)

Criteria

The following criteria must be met for Illumina Connected Annotations to identify a gene fusion:

  1. After accounting for gene orientation and genomic rearrangements, both transcripts must have the same orientation if enable-bidirectional-fusions is not enabled. They can have the same or different orientations if enable-bidirectional-fusions is set.
  2. Both transcripts must be from the same transcript source (i.e. we won't mix and match between RefSeq and Ensembl transcripts)
  3. Both transcripts must belong to different genes
  4. Both transcripts cannot have a coding region that already overlaps without the variant (i.e. in cases where two genes naturally overlap, we don't want to call a gene fusion)

ETV6/RUNX1 Example

ETV6/RUNX1 is the most common gene fusion in childhood B-cell precursor acute lymphoblastic leukemia (ALL). Samples with this translocation are associated with a good prognosis and excellent response to treatment.

Publication

Sun C., Chang L., Zhu X. Pathogenesis of ETV6/RUNX1-positive childhood acute lymphoblastic leukemia and mechanisms underlying its relapse. Oncotarget. 2017; 8: 35445-35459

VCF

Here's a simplified representation of the translocation breakends called by the Manta structural variant caller:

##fileformat=VCFv4.1
#CHROM  POS     ID      REF     ALT     QUAL    FILTER  INFO
chr12   12026270    .   C   [chr21:36420865[C   .   PASS    SVTYPE=BND
chr12   12026305    .   A   A]chr21:36420571]   .   PASS    SVTYPE=BND
chr21   36420571    .   C   C]chr12:12026305]   .   PASS    SVTYPE=BND
chr21   36420865    .   C   [chr12:12026270[C   .   PASS    SVTYPE=BND

When you put these calls together, the resulting genomic rearrangement looks something like this:

JSON Output

The annotation for the first variant in the VCF looks like this:

{
  "chromosome": "chr12",
  "position": 12026270,
  "refAllele": "C",
  "altAlleles": [
    "[chr21:36420865[C"
  ],
  "filters": [
    "PASS"
  ],
  "cytogeneticBand": "12p13.2",
  "clingen": [
    {
      "chromosome": "12",
      "begin": 173786,
      "end": 34835837,
      "variantType": "copy_number_gain",
      "id": "nsv995956",
      "clinicalInterpretation": "pathogenic",
      "phenotypes": [
        "Decreased calvarial ossification",
        "Delayed gross motor development",
        "Feeding difficulties",
        "Frontal bossing",
        "Morphological abnormality of the central nervous system",
        "Patchy alopecia"
      ],
      "phenotypeIds": [
        "HP:0002007",
        "HP:0002011",
        "HP:0002194",
        "HP:0002232",
        "HP:0005474",
        "HP:0011968",
        "MedGen:C0232466",
        "MedGen:C1862862",
        "MedGen:CN001816",
        "MedGen:CN001820",
        "MedGen:CN001989",
        "MedGen:CN004852"
      ],
      "observedGains": 1,
      "validated": true
    }
  ],
  "variants": [
    {
      "vid": "12-12026270-C-[chr21:36420865[C",
      "chromosome": "chr12",
      "begin": 12026270,
      "end": 12026270,
      "isStructuralVariant": true,
      "refAllele": "C",
      "altAllele": "[chr21:36420865[C",
      "variantType": "translocation_breakend",
      "cosmicGeneFusions": [
        {
          "id": "COSF2245",
          "numSamples": 249,
          "geneSymbols": [
            "ETV6",
            "RUNX1"
          ],
          "hgvsr": "ENST00000396373.4(ETV6):r.1_1283::ENST00000300305.3(RUNX1):r.504_6222",
          "histologies": [
            {
              "name": "acute lymphoblastic B cell leukaemia",
              "numSamples": 169
            },
            {
              "name": "acute lymphoblastic leukaemia",
              "numSamples": 80
            }
          ],
          "sites": [
            {
              "name": "haematopoietic and lymphoid tissue",
              "numSamples": 249
            }
          ],
          "pubMedIds": [
            7761424,
            7780150,
            8609706,
            8751464,
            8982044,
            9067587,
            9207408,
            9226156,
            9628428,
            10463610,
            10774753,
            11091202,
            12621238,
            12661004,
            12750722,
            15104290,
            15642392,
            24557455,
            26925663
          ]
        }
      ],
      "fusionCatcher": [
        {
          "genes": {
            "first": {
              "hgnc": "ETV6",
              "isOncogene": true
            },
            "second": {
              "hgnc": "RUNX1",
              "isOncogene": true
            }
          },
          "somaticSources": [
            "DepMap CCLE",
            "Cancer Genome Project",
            "ChimerKB 4.0",
            "ChimerPub 4.0",
            "ChimerSeq 4.0",
            "Known",
            "Mitelman DB",
            "OncoKB",
            "TICdb"
          ]
        }
      ],
      "transcripts": [
        {
          "transcript": "ENST00000396373.4",
          "source": "Ensembl",
          "bioType": "protein_coding",
          "introns": "5/7",
          "geneId": "ENSG00000139083",
          "hgnc": "ETV6",
          "consequence": [
            "transcript_variant",
            "unidirectional_gene_fusion"
          ],
          "geneFusions": [
            {
              "transcript": "ENST00000437180.1",
              "bioType": "protein_coding",
              "intron": 2,
              "geneId": "ENSG00000159216",
              "hgnc": "RUNX1",
              "hgvsr": "ENST00000437180.1(RUNX1):r.?_58+274::ENST00000396373.4(ETV6):r.1009+3367_?",
              "directionality":"uniDirectional"
            },
            {
              "transcript": "ENST00000300305.3",
              "bioType": "protein_coding",
              "intron": 1,
              "geneId": "ENSG00000159216",
              "hgnc": "RUNX1",
              "hgvsr": "ENST00000300305.3(RUNX1):r.?_58+274::ENST00000396373.4(ETV6):r.1009+3367_?",
              "directionality":"uniDirectional"
            },
            {
              "transcript": "ENST00000482318.1",
              "bioType": "nonsense_mediated_decay",
              "intron": 2,
              "geneId": "ENSG00000159216",
              "hgnc": "RUNX1",
              "hgvsr": "ENST00000482318.1(RUNX1):r.?_58+274::ENST00000396373.4(ETV6):r.1009+3367_?",
              "directionality":"uniDirectional"
            },
            {
              "transcript": "ENST00000486278.2",
              "bioType": "protein_coding",
              "intron": 2,
              "geneId": "ENSG00000159216",
              "hgnc": "RUNX1",
              "hgvsr": "ENST00000486278.2(RUNX1):r.?_-15+274::ENST00000396373.4(ETV6):r.1009+3367_?",
              "directionality":"uniDirectional"
            },
            {
              "transcript": "ENST00000455571.1",
              "bioType": "protein_coding",
              "intron": 2,
              "geneId": "ENSG00000159216",
              "hgnc": "RUNX1",
              "hgvsr": "ENST00000455571.1(RUNX1):r.?_58+274::ENST00000396373.4(ETV6):r.1009+3367_?",
              "directionality":"uniDirectional"
            },
            {
              "transcript": "ENST00000475045.2",
              "bioType": "protein_coding",
              "intron": 11,
              "geneId": "ENSG00000159216",
              "hgnc": "RUNX1",
              "hgvsr": "ENST00000475045.2(RUNX1):r.?_58+274::ENST00000396373.4(ETV6):r.1009+3367_?",
              "directionality":"uniDirectional"
            },
            {
              "transcript": "ENST00000416754.1",
              "bioType": "protein_coding",
              "intron": 2,
              "geneId": "ENSG00000159216",
              "hgnc": "RUNX1",
              "hgvsr": "ENST00000416754.1(RUNX1):r.?_58+274::ENST00000396373.4(ETV6):r.1009+3367_?",
              "directionality":"uniDirectional"
            }
          ],
          "isCanonical": true,
          "proteinId": "ENSP00000379658.3"
        },
        {
          "transcript": "NM_001987.4",
          "source": "RefSeq",
          "bioType": "protein_coding",
          "introns": "5/7",
          "geneId": "2120",
          "hgnc": "ETV6",
          "consequence": [
            "transcript_variant",
            "unidirectional_gene_fusion"
          ],
          "geneFusions": [
            {
              "transcript": "NM_001754.4",
              "bioType": "protein_coding",
              "intron": 2,
              "geneId": "861",
              "hgnc": "RUNX1",
              "hgvsr": "NM_001754.4(RUNX1):r.?_58+274::NM_001987.4(ETV6):r.1009+3367_?",
              "directionality":"uniDirectional"
            }
          ],
          "isCanonical": true,
          "proteinId": "NP_001978.1"
        }
      ]
    }
  ]
}
FieldTypeNotes
transcriptstringtranscript ID
bioTypestringdescriptions of the biotypes from Ensembl
exonintexon that contained fusion breakpoint
intronintintron that contained fusion breakpoint
geneIdstringgene ID. e.g. ENSG00000116062
hgncstringgene symbol. e.g. MSH6
hgvsrstringHGVS RNA nomenclature

Gene Fusion Data Sources

To provide more context to our gene fusions, we provide the following gene fusion data sources:

Consequences

When a gene fusion is identified, we add the following Sequence Ontology consequence:

              "consequence": [
                "transcript_variant",
                "gene_fusion"
              ],
  • If both transcripts have the same orientation, we label it as unidirectional_gene_fusion, if they have different orientations, we label it as bidirectional_gene_fusion
  • If both unidirectional and bidirectional ones are detected, we label it as gene_fusion.

Gene Fusions Section

The geneFusions section is contained within the object of the originating transcript. It will contain all the pairwise gene fusions that obey the criteria outline above. In the case of ENST00000396373.4, there 7 other Ensembl transcripts that would produce a gene fusion. For NM_001987.4, there was only one transcript (NM_001754.4) that produce a gene fusion.

For each originating transcript, we report the following for each partner transcript:

  • transcript ID
  • gene ID
  • HGNC gene symbol
  • transcript bio type (e.g. protein_coding)
  • intron or exon number containing the breakpoint
  • HGVS RNA notation
  • gene fusion directionality
tip

Before Illumina Connected Annotations 3.15, we provided HGVS coding notation. However, HGVS r. notation is more appropriate for these types fusion splicing events (see HGVS SVD-WG007).

          "geneFusions": [
            {
              "transcript": "NM_001754.4",
              "bioType": "protein_coding",
              "intron": 2,
              "geneId": "861",
              "hgnc": "RUNX1",
              "hgvsr": "NM_001754.4(RUNX1):r.?_58+274::NM_001987.4(ETV6):r.1009+3367_?",
              "directionality":"uniDirectional"
            }
          ],

The HGVS RNA notation above indicates that the gene fusion starts with NM_001754.4 (RUNX1) until CDS position 58 and continues with NM_001987.4 (ETV6). 1009+3367 indicates that the fusion occurred 3367 bp within intron 2.

- - +If only unidirectional gene fusions are desired, only these two fusions can be detected. If enable-bidirectional-fusions is enabled, all four cases can be identified.

Interpreting translocation breakends

At first glance, translocation breakends are a bit daunting. However, once you understand how they work, they're actually quite simple. For more information, we recommend reading section 5.4 in the VCF 4.2 specification.

REFALTMeaning
st[p[piece extending to the right of p is joined after t
st]p]reverse comp piece extending left of p is joined after t
s]p]tpiece extending to the left of p is joined before t
s[p[treverse comp piece extending right of p is joined before t

Variant Types

Specifically we can identify gene fusions from the following structural variant types:

Criteria

The following criteria must be met for Illumina Connected Annotations to identify a gene fusion:

  1. After accounting for gene orientation and genomic rearrangements, both transcripts must have the same orientation if enable-bidirectional-fusions is not enabled. They can have the same or different orientations if enable-bidirectional-fusions is set.
  2. Both transcripts must be from the same transcript source (i.e. we won't mix and match between RefSeq and Ensembl transcripts)
  3. Both transcripts must belong to different genes
  4. Both transcripts cannot have a coding region that already overlaps without the variant (i.e. in cases where two genes naturally overlap, we don't want to call a gene fusion)

ETV6/RUNX1 Example

ETV6/RUNX1 is the most common gene fusion in childhood B-cell precursor acute lymphoblastic leukemia (ALL). Samples with this translocation are associated with a good prognosis and excellent response to treatment.

Publication

Sun C., Chang L., Zhu X. Pathogenesis of ETV6/RUNX1-positive childhood acute lymphoblastic leukemia and mechanisms underlying its relapse. Oncotarget. 2017; 8: 35445-35459

VCF

Here's a simplified representation of the translocation breakends called by the Manta structural variant caller:

##fileformat=VCFv4.1
#CHROM  POS     ID      REF     ALT     QUAL    FILTER  INFO
chr12   12026270    .   C   [chr21:36420865[C   .   PASS    SVTYPE=BND
chr12   12026305    .   A   A]chr21:36420571]   .   PASS    SVTYPE=BND
chr21   36420571    .   C   C]chr12:12026305]   .   PASS    SVTYPE=BND
chr21   36420865    .   C   [chr12:12026270[C   .   PASS    SVTYPE=BND

When you put these calls together, the resulting genomic rearrangement looks something like this:

JSON Output

The annotation for the first variant in the VCF looks like this:

{
  "chromosome": "chr12",
  "position": 12026270,
  "refAllele": "C",
  "altAlleles": [
    "[chr21:36420865[C"
  ],
  "filters": [
    "PASS"
  ],
  "cytogeneticBand": "12p13.2",
  "clingen": [
    {
      "chromosome": "12",
      "begin": 173786,
      "end": 34835837,
      "variantType": "copy_number_gain",
      "id": "nsv995956",
      "clinicalInterpretation": "pathogenic",
      "phenotypes": [
        "Decreased calvarial ossification",
        "Delayed gross motor development",
        "Feeding difficulties",
        "Frontal bossing",
        "Morphological abnormality of the central nervous system",
        "Patchy alopecia"
      ],
      "phenotypeIds": [
        "HP:0002007",
        "HP:0002011",
        "HP:0002194",
        "HP:0002232",
        "HP:0005474",
        "HP:0011968",
        "MedGen:C0232466",
        "MedGen:C1862862",
        "MedGen:CN001816",
        "MedGen:CN001820",
        "MedGen:CN001989",
        "MedGen:CN004852"
      ],
      "observedGains": 1,
      "validated": true
    }
  ],
  "variants": [
    {
      "vid": "12-12026270-C-[chr21:36420865[C",
      "chromosome": "chr12",
      "begin": 12026270,
      "end": 12026270,
      "isStructuralVariant": true,
      "refAllele": "C",
      "altAllele": "[chr21:36420865[C",
      "variantType": "translocation_breakend",
      "cosmicGeneFusions": [
        {
          "id": "COSF2245",
          "numSamples": 249,
          "geneSymbols": [
            "ETV6",
            "RUNX1"
          ],
          "hgvsr": "ENST00000396373.4(ETV6):r.1_1283::ENST00000300305.3(RUNX1):r.504_6222",
          "histologies": [
            {
              "name": "acute lymphoblastic B cell leukaemia",
              "numSamples": 169
            },
            {
              "name": "acute lymphoblastic leukaemia",
              "numSamples": 80
            }
          ],
          "sites": [
            {
              "name": "haematopoietic and lymphoid tissue",
              "numSamples": 249
            }
          ],
          "pubMedIds": [
            7761424,
            7780150,
            8609706,
            8751464,
            8982044,
            9067587,
            9207408,
            9226156,
            9628428,
            10463610,
            10774753,
            11091202,
            12621238,
            12661004,
            12750722,
            15104290,
            15642392,
            24557455,
            26925663
          ]
        }
      ],
      "fusionCatcher": [
        {
          "genes": {
            "first": {
              "hgnc": "ETV6",
              "isOncogene": true
            },
            "second": {
              "hgnc": "RUNX1",
              "isOncogene": true
            }
          },
          "somaticSources": [
            "DepMap CCLE",
            "Cancer Genome Project",
            "ChimerKB 4.0",
            "ChimerPub 4.0",
            "ChimerSeq 4.0",
            "Known",
            "Mitelman DB",
            "OncoKB",
            "TICdb"
          ]
        }
      ],
      "transcripts": [
        {
          "transcript": "ENST00000396373.4",
          "source": "Ensembl",
          "bioType": "protein_coding",
          "introns": "5/7",
          "geneId": "ENSG00000139083",
          "hgnc": "ETV6",
          "consequence": [
            "transcript_variant",
            "unidirectional_gene_fusion"
          ],
          "geneFusions": [
            {
              "transcript": "ENST00000437180.1",
              "bioType": "protein_coding",
              "intron": 2,
              "geneId": "ENSG00000159216",
              "hgnc": "RUNX1",
              "hgvsr": "ENST00000437180.1(RUNX1):r.?_58+274::ENST00000396373.4(ETV6):r.1009+3367_?",
              "directionality":"uniDirectional"
            },
            {
              "transcript": "ENST00000300305.3",
              "bioType": "protein_coding",
              "intron": 1,
              "geneId": "ENSG00000159216",
              "hgnc": "RUNX1",
              "hgvsr": "ENST00000300305.3(RUNX1):r.?_58+274::ENST00000396373.4(ETV6):r.1009+3367_?",
              "directionality":"uniDirectional"
            },
            {
              "transcript": "ENST00000482318.1",
              "bioType": "nonsense_mediated_decay",
              "intron": 2,
              "geneId": "ENSG00000159216",
              "hgnc": "RUNX1",
              "hgvsr": "ENST00000482318.1(RUNX1):r.?_58+274::ENST00000396373.4(ETV6):r.1009+3367_?",
              "directionality":"uniDirectional"
            },
            {
              "transcript": "ENST00000486278.2",
              "bioType": "protein_coding",
              "intron": 2,
              "geneId": "ENSG00000159216",
              "hgnc": "RUNX1",
              "hgvsr": "ENST00000486278.2(RUNX1):r.?_-15+274::ENST00000396373.4(ETV6):r.1009+3367_?",
              "directionality":"uniDirectional"
            },
            {
              "transcript": "ENST00000455571.1",
              "bioType": "protein_coding",
              "intron": 2,
              "geneId": "ENSG00000159216",
              "hgnc": "RUNX1",
              "hgvsr": "ENST00000455571.1(RUNX1):r.?_58+274::ENST00000396373.4(ETV6):r.1009+3367_?",
              "directionality":"uniDirectional"
            },
            {
              "transcript": "ENST00000475045.2",
              "bioType": "protein_coding",
              "intron": 11,
              "geneId": "ENSG00000159216",
              "hgnc": "RUNX1",
              "hgvsr": "ENST00000475045.2(RUNX1):r.?_58+274::ENST00000396373.4(ETV6):r.1009+3367_?",
              "directionality":"uniDirectional"
            },
            {
              "transcript": "ENST00000416754.1",
              "bioType": "protein_coding",
              "intron": 2,
              "geneId": "ENSG00000159216",
              "hgnc": "RUNX1",
              "hgvsr": "ENST00000416754.1(RUNX1):r.?_58+274::ENST00000396373.4(ETV6):r.1009+3367_?",
              "directionality":"uniDirectional"
            }
          ],
          "isCanonical": true,
          "proteinId": "ENSP00000379658.3"
        },
        {
          "transcript": "NM_001987.4",
          "source": "RefSeq",
          "bioType": "protein_coding",
          "introns": "5/7",
          "geneId": "2120",
          "hgnc": "ETV6",
          "consequence": [
            "transcript_variant",
            "unidirectional_gene_fusion"
          ],
          "geneFusions": [
            {
              "transcript": "NM_001754.4",
              "bioType": "protein_coding",
              "intron": 2,
              "geneId": "861",
              "hgnc": "RUNX1",
              "hgvsr": "NM_001754.4(RUNX1):r.?_58+274::NM_001987.4(ETV6):r.1009+3367_?",
              "directionality":"uniDirectional"
            }
          ],
          "isCanonical": true,
          "proteinId": "NP_001978.1"
        }
      ]
    }
  ]
}
FieldTypeNotes
transcriptstringtranscript ID
bioTypestringdescriptions of the biotypes from Ensembl
exonintexon that contained fusion breakpoint
intronintintron that contained fusion breakpoint
geneIdstringgene ID. e.g. ENSG00000116062
hgncstringgene symbol. e.g. MSH6
hgvsrstringHGVS RNA nomenclature

Gene Fusion Data Sources

To provide more context to our gene fusions, we provide the following gene fusion data sources:

Consequences

When a gene fusion is identified, we add the following Sequence Ontology consequence:

              "consequence": [
                "transcript_variant",
                "gene_fusion"
              ],

Gene Fusions Section

The geneFusions section is contained within the object of the originating transcript. It will contain all the pairwise gene fusions that obey the criteria outline above. In the case of ENST00000396373.4, there 7 other Ensembl transcripts that would produce a gene fusion. For NM_001987.4, there was only one transcript (NM_001754.4) that produce a gene fusion.

For each originating transcript, we report the following for each partner transcript:

tip

Before Illumina Connected Annotations 3.15, we provided HGVS coding notation. However, HGVS r. notation is more appropriate for these types fusion splicing events (see HGVS SVD-WG007).

          "geneFusions": [
            {
              "transcript": "NM_001754.4",
              "bioType": "protein_coding",
              "intron": 2,
              "geneId": "861",
              "hgnc": "RUNX1",
              "hgvsr": "NM_001754.4(RUNX1):r.?_58+274::NM_001987.4(ETV6):r.1009+3367_?",
              "directionality":"uniDirectional"
            }
          ],

The HGVS RNA notation above indicates that the gene fusion starts with NM_001754.4 (RUNX1) until CDS position 58 and continues with NM_001987.4 (ETV6). 1009+3367 indicates that the fusion occurred 3367 bp within intron 2.

+ + \ No newline at end of file diff --git a/3.22/core-functionality/transcript-consequence-impacts/index.html b/3.22/core-functionality/transcript-consequence-impacts/index.html index 96f105bd..a98864b3 100644 --- a/3.22/core-functionality/transcript-consequence-impacts/index.html +++ b/3.22/core-functionality/transcript-consequence-impacts/index.html @@ -6,14 +6,14 @@ Transcript Consequence Impact | IlluminaConnectedAnnotations - - + +
Skip to main content
Version: 3.22

Transcript Consequence Impact

Overview

Illumina Connected Annotations provides transcript consequence impacts from SnpEff.

Following definitions are used for the impact ratings as obtained from SnpEff.

ImpactDefinition
highThe variant is assumed to have high (disruptive) impact in the protein, probably causing protein truncation, loss of function or triggering nonsense mediated decay.
moderateA non-disruptive variant that might change protein effectiveness.
lowAssumed to be mostly harmless or unlikely to change protein behavior.
modifierUsually non-coding variants or variants affecting non-coding genes, where predictions are difficult or there is no evidence of impact.

Sources

Not all consequences are rated by SnpEff, therefore Illumina Connected Annotations combines the ratings from SnpEff with those from VEP.

  1. SnpEff Documentation and Codebase
  2. VEP Documentation

Consequence Impacts

Following table gives the combined rating for all consequences recognized by Illumina Connected Annotations.

ConsequenceSnpEff ImpactVEP ImpactIllumina Connected Annotations ImpactComment
bidirectional_gene_fusionhighhighSnpEff
coding_sequence_variantlow, modifiermodifiermodifierBased on CDS
copy_number_changemodifier
copy_number_decreasemodifier
copy_number_increasemodifier
downstream_gene_variantmodifiermodifiermodifierSnpEff + VEP
feature_elongationmodifierhighhighVEP
feature_truncationhighhighVEP
five_prime_duplicated_transcriptmodifier
five_prime_UTR_variantmodifiermodifiermodifierSnpEff + VEP
frameshift_varianthighhighhighSnpEff + VEP
gene_fusionhighhighSnpEff
incomplete_terminal_codon_variantlowlowVEP
inframe_deletionmoderatemoderatemoderateSnpEff + VEP
inframe_insertionmoderatemoderatemoderateSnpEff + VEP
intron_variantmodifiermodifiermodifierSnpEff + VEP
mature_miRNA_variantmodifiermodifierVEP
missense_variantmoderatemoderatemoderateSnpEff + VEP
NMD_transcript_variantmodifiermodifierVEP
non_coding_transcript_exon_variantmodifiermodifiermodifierSnpEff + VEP
non_coding_transcript_variantmodifiermodifiermodifierSnpEff + VEP
protein_altering_variantmoderatemoderateVEP
regulatory_region_ablationmodifiermodifierVEP
regulatory_region_amplificationmodifiermodifierVEP
regulatory_region_variantmodifiermodifiermodifierSnpEff + VEP
short_tandem_repeat_changemodifier
short_tandem_repeat_contractionmodifier
short_tandem_repeat_expansionmodifier
splice_acceptor_varianthighhighhighSnpEff + VEP
splice_donor_varianthighhighhighSnpEff + VEP
splice_region_variantmoderate, lowlowlowBased on SPLICE_SITE_REGION in SnpEff
start_losthighhighhighSnpEff + VEP
start_retained_variantlowlowlowSnpEff + VEP
stop_gainedhighhighhighSnpEff + VEP
stop_losthighhighhighSnpEff + VEP
stop_retained_variantlowlowlowSnpEff + VEP
synonymous_variantlowlowlowSnpEff + VEP
three_prime_duplicated_transcriptmodifier
three_prime_UTR_variantmodifiermodifiermodifierSnpEff + VEP
transcript_ablationhighhighhighSnpEff + VEP
transcript_amplificationhighhighVEP
transcript_variantmodifiermodifierSnpEff
unidirectional_gene_fusionhighhighSnpEff
upstream_gene_variantmodifiermodifiermodifierSnpEff + VEP
Note:
  1. For transcripts with multiple consequences, the most severe impact rating is chosen.
  2. In case of consequences that do not have any impact rating from SnpEff or VEP, Illumina Connected Annotations provides modifier.

Known Issues

Known Issues

The consequence splice_polypyrimidine_tract_variant, is rated as low by VEP. -However, this consequence is not annotated by Illumina Connected Annotations, therefore the impact will also not be provided.

Example Transcript

The key impact for each transcript gives the impact rating for the consequence.

{
  "variants": [
    {
      "vid": "1-1623412-T-C",
      "chromosome": "1",
      "begin": 1623412,
      "end": 1623412,
      "refAllele": "T",
      "altAllele": "C",
      "variantType": "SNV",
      "hgvsg": "NC_000001.11:g.1623412T>C",
      "transcripts": [
        {
          "transcript": "ENST00000479659.5",
          "source": "Ensembl",
          "bioType": "lncRNA",
          "introns": "2/18",
          "geneId": "ENSG00000197530",
          "hgnc": "MIB2",
          "consequence": [
            "intron_variant",
            "non_coding_transcript_variant"
          ],
          "impact": "modifier",
          "hgvsc": "ENST00000479659.5:n.288-19T>C"
        },
        {
          "transcript": "ENST00000489635.5",
          "source": "VEP",
          "bioType": "mRNA",
          "codons": "aTg/aCg",
          "aminoAcids": "M/T",
          "cdnaPos": "269",
          "cdsPos": "134",
          "exons": "3/20",
          "proteinPos": "45",
          "geneId": "ENSG00000197530",
          "hgnc": "MIB2",
          "consequence": [
            "missense_variant"
          ],
          "impact": "moderate",
          "hgvsc": "ENST00000489635.5:c.134T>C",
          "hgvsp": "ENSP00000426007.1:p.(Met45Thr)",
          "proteinId": "ENSP00000426007.1"
        }
      ]
    }
  ]
}
- - +However, this consequence is not annotated by Illumina Connected Annotations, therefore the impact will also not be provided.

Example Transcript

The key impact for each transcript gives the impact rating for the consequence.

{
  "variants": [
    {
      "vid": "1-1623412-T-C",
      "chromosome": "1",
      "begin": 1623412,
      "end": 1623412,
      "refAllele": "T",
      "altAllele": "C",
      "variantType": "SNV",
      "hgvsg": "NC_000001.11:g.1623412T>C",
      "transcripts": [
        {
          "transcript": "ENST00000479659.5",
          "source": "Ensembl",
          "bioType": "lncRNA",
          "introns": "2/18",
          "geneId": "ENSG00000197530",
          "hgnc": "MIB2",
          "consequence": [
            "intron_variant",
            "non_coding_transcript_variant"
          ],
          "impact": "modifier",
          "hgvsc": "ENST00000479659.5:n.288-19T>C"
        },
        {
          "transcript": "ENST00000489635.5",
          "source": "VEP",
          "bioType": "mRNA",
          "codons": "aTg/aCg",
          "aminoAcids": "M/T",
          "cdnaPos": "269",
          "cdsPos": "134",
          "exons": "3/20",
          "proteinPos": "45",
          "geneId": "ENSG00000197530",
          "hgnc": "MIB2",
          "consequence": [
            "missense_variant"
          ],
          "impact": "moderate",
          "hgvsc": "ENST00000489635.5:c.134T>C",
          "hgvsp": "ENSP00000426007.1:p.(Met45Thr)",
          "proteinId": "ENSP00000426007.1"
        }
      ]
    }
  ]
}
+ + \ No newline at end of file diff --git a/3.22/core-functionality/variant-ids/index.html b/3.22/core-functionality/variant-ids/index.html index 244c5b62..88129c86 100644 --- a/3.22/core-functionality/variant-ids/index.html +++ b/3.22/core-functionality/variant-ids/index.html @@ -6,13 +6,13 @@ Variant IDs | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

Variant IDs

Overview

Many downstream tools use a variant identifier to store annotation results. We've standardized on using variant identifiers (VIDs) that originated from the notation used by the Broad Institute.

The Broad VID scheme is not only simple, but it has the advantage that a user could create a bare bones VCF entry from the information captured in the identifier. One of the limitations of the Broad VID scheme is that it does not define how to handle structural variants. Our VID scheme attempts to fill that gap.

Conventions
  • all chromosomes use Ensembl style notation (i.e. 22 instead of chr22)
  • for a reference variant (i.e. no alt allele), replace the period (.) with the reference base
  • padding bases are used, neither the reference nor alternate allele can be empty
  • some large variant callers lazily output N for the reference allele. If this is the case, replace it with the true reference base

Small Variants

VCF Examples

chr1    66507   .   T   A   184.45  PASS    .
chr1    66521   .   T   TATATA  144.53  PASS    .
chr1    66572   .   GTA G,GTACTATATATTATA   45.45   PASS    .

Format

chromosome—position—reference allele—alternate allele

VID Examples

  • 1-66507-T-A
  • 1-66521-T-TATATA
  • 1-66572-GTA-G
  • 1-66572-G-GTACTATATATTA

Translocation Breakends

VCF Example

chr1    2617277 .   A   AAAAAAAAAAAAAAAAAATTAGTCAGGCAC[chr3:153444911[  .   PASS    SVTYPE=BND

Format

chromosome—position—reference allele—alternate allele

VID Example

  • 1-2617277-A-AAAAAAAAAAAAAAAAAATTAGTCAGGCAC[chr3:153444911[

All Other Structural Variants

VCF Examples

chr1    1000    .   G   <ROH>   .   PASS    END=3001000;SVTYPE=ROH
chr1    1350082 .   G   <DEL>   .   PASS    END=1351320;SVTYPE=DEL
chr1    1477854 .   C   <DUP:TANDEM>    .   PASS    END=1477984;SVTYPE=DUP
chr1    1477968 .   T   <INS>   .   PASS    END=1477968;SVTYPE=INS
chr1    1715898 .   N   <DUP>   .   PASS    SVTYPE=CNV;END=1750149
chr1    2650426 .   N   <DEL>   .   PASS    SVTYPE=CNV;END=2653074
chr2    321682  .   T   <INV>   .   PASS    SVTYPE=INV;END=421681
chr20   2633403 .   G   <STR2>  .   PASS    END=2633421

Format

chromosome—position—end position—reference allele—alternate allele—SVTYPE

VID Examples

  • 1-1000-3001000-G-<ROH>-ROH
  • 1-1350082-1351320-G-<DEL>-DEL
  • 1-1477854-1477984-C-<DUP:TANDEM>-DUP
  • 1-1477968-1477968-T-<INS>-INS
  • 1-1715898-1750149-A-<DUP>-CNV (replace the N with A)
  • 1-2650426-2653074-N-<DEL>-CNV (keep the N)
  • 2-321682-421681-T-<INV>-INV
  • 20-2633403-2633421-G-<STR2>-STR
- - +
Skip to main content
Version: 3.22

Variant IDs

Overview

Many downstream tools use a variant identifier to store annotation results. We've standardized on using variant identifiers (VIDs) that originated from the notation used by the Broad Institute.

The Broad VID scheme is not only simple, but it has the advantage that a user could create a bare bones VCF entry from the information captured in the identifier. One of the limitations of the Broad VID scheme is that it does not define how to handle structural variants. Our VID scheme attempts to fill that gap.

Conventions
  • all chromosomes use Ensembl style notation (i.e. 22 instead of chr22)
  • for a reference variant (i.e. no alt allele), replace the period (.) with the reference base
  • padding bases are used, neither the reference nor alternate allele can be empty
  • some large variant callers lazily output N for the reference allele. If this is the case, replace it with the true reference base

Small Variants

VCF Examples

chr1    66507   .   T   A   184.45  PASS    .
chr1    66521   .   T   TATATA  144.53  PASS    .
chr1    66572   .   GTA G,GTACTATATATTATA   45.45   PASS    .

Format

chromosome—position—reference allele—alternate allele

VID Examples

  • 1-66507-T-A
  • 1-66521-T-TATATA
  • 1-66572-GTA-G
  • 1-66572-G-GTACTATATATTA

Translocation Breakends

VCF Example

chr1    2617277 .   A   AAAAAAAAAAAAAAAAAATTAGTCAGGCAC[chr3:153444911[  .   PASS    SVTYPE=BND

Format

chromosome—position—reference allele—alternate allele

VID Example

  • 1-2617277-A-AAAAAAAAAAAAAAAAAATTAGTCAGGCAC[chr3:153444911[

All Other Structural Variants

VCF Examples

chr1    1000    .   G   <ROH>   .   PASS    END=3001000;SVTYPE=ROH
chr1    1350082 .   G   <DEL>   .   PASS    END=1351320;SVTYPE=DEL
chr1    1477854 .   C   <DUP:TANDEM>    .   PASS    END=1477984;SVTYPE=DUP
chr1    1477968 .   T   <INS>   .   PASS    END=1477968;SVTYPE=INS
chr1    1715898 .   N   <DUP>   .   PASS    SVTYPE=CNV;END=1750149
chr1    2650426 .   N   <DEL>   .   PASS    SVTYPE=CNV;END=2653074
chr2    321682  .   T   <INV>   .   PASS    SVTYPE=INV;END=421681
chr20   2633403 .   G   <STR2>  .   PASS    END=2633421

Format

chromosome—position—end position—reference allele—alternate allele—SVTYPE

VID Examples

  • 1-1000-3001000-G-<ROH>-ROH
  • 1-1350082-1351320-G-<DEL>-DEL
  • 1-1477854-1477984-C-<DUP:TANDEM>-DUP
  • 1-1477968-1477968-T-<INS>-INS
  • 1-1715898-1750149-A-<DUP>-CNV (replace the N with A)
  • 1-2650426-2653074-N-<DEL>-CNV (keep the N)
  • 2-321682-421681-T-<INV>-INV
  • 20-2633403-2633421-G-<STR2>-STR
+ + \ No newline at end of file diff --git a/3.22/data-sources/1000Genomes-snv-json/index.html b/3.22/data-sources/1000Genomes-snv-json/index.html index d3985115..1cd3e07c 100644 --- a/3.22/data-sources/1000Genomes-snv-json/index.html +++ b/3.22/data-sources/1000Genomes-snv-json/index.html @@ -6,13 +6,13 @@ 1000Genomes-snv-json | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

1000Genomes-snv-json

"oneKg":{
   "allAf":0.200879,
   "afrAf":0.210287,
   "amrAf":0.139769,
   "easAf":0.275794,
   "eurAf":0.181909,
   "sasAf":0.173824,
   "allAn":5008,
   "afrAn":1322,
   "amrAn":694,
   "easAn":1008,
   "eurAn":1006,
   "sasAn":978,
   "allAc":1006,
   "afrAc":278,
   "amrAc":97,
   "easAc":278,
   "eurAc":183,
   "sasAc":170
}
FieldTypeNotes
allAffloatallele frequency for all populations. Range: 0 - 1.0
allAcintallele count for all populations. Integer.
allAnintallele number for all populations. Non-zero integer.
afrAffloatallele frequency for the African super population. Range: 0 - 1.0
afrAcintallele count for the African super population. Integer.
afrAnintallele number for the African super population. Non-zero integer.
amrAffloatallele frequency for the Ad Mixed American super population. Range: 0 - 1.0
amrAcintallele count for the Ad Mixed American super population. Integer.
amrAnintallele number for the Ad Mixed American super population. Non-zero integer.
easAffloatallele frequency for the East Asian super population. Range: 0 - 1.0
easAcintallele count for the East Asian super population. Integer.
easAnintallele number for the East Asian super population. Non-zero integer.
eurAffloatallele frequency for the European super population. Range: 0 - 1.0
eurAcintallele count for the European super population. Integer.
eurAnintallele number for the European super population. Non-zero integer.
sasAffloatallele frequency for the South Asian super population. Range: 0 - 1.0
sasAcintallele count for the South Asian super population. Integer.
sasAnintallele number for the South Asian super population. Non-zero integer.
- - +
Skip to main content
Version: 3.22

1000Genomes-snv-json

"oneKg":{
   "allAf":0.200879,
   "afrAf":0.210287,
   "amrAf":0.139769,
   "easAf":0.275794,
   "eurAf":0.181909,
   "sasAf":0.173824,
   "allAn":5008,
   "afrAn":1322,
   "amrAn":694,
   "easAn":1008,
   "eurAn":1006,
   "sasAn":978,
   "allAc":1006,
   "afrAc":278,
   "amrAc":97,
   "easAc":278,
   "eurAc":183,
   "sasAc":170
}
FieldTypeNotes
allAffloatallele frequency for all populations. Range: 0 - 1.0
allAcintallele count for all populations. Integer.
allAnintallele number for all populations. Non-zero integer.
afrAffloatallele frequency for the African super population. Range: 0 - 1.0
afrAcintallele count for the African super population. Integer.
afrAnintallele number for the African super population. Non-zero integer.
amrAffloatallele frequency for the Ad Mixed American super population. Range: 0 - 1.0
amrAcintallele count for the Ad Mixed American super population. Integer.
amrAnintallele number for the Ad Mixed American super population. Non-zero integer.
easAffloatallele frequency for the East Asian super population. Range: 0 - 1.0
easAcintallele count for the East Asian super population. Integer.
easAnintallele number for the East Asian super population. Non-zero integer.
eurAffloatallele frequency for the European super population. Range: 0 - 1.0
eurAcintallele count for the European super population. Integer.
eurAnintallele number for the European super population. Non-zero integer.
sasAffloatallele frequency for the South Asian super population. Range: 0 - 1.0
sasAcintallele count for the South Asian super population. Integer.
sasAnintallele number for the South Asian super population. Non-zero integer.
+ + \ No newline at end of file diff --git a/3.22/data-sources/1000Genomes-sv-json/index.html b/3.22/data-sources/1000Genomes-sv-json/index.html index 2a651d7b..92e9948d 100644 --- a/3.22/data-sources/1000Genomes-sv-json/index.html +++ b/3.22/data-sources/1000Genomes-sv-json/index.html @@ -6,13 +6,13 @@ 1000Genomes-sv-json | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

1000Genomes-sv-json

"oneKg":[
   {
      "chromosome":"1",
      "begin":1595369,
      "end":1612441,
      "variantType": "copy_number_variation",
      "id": "esv3635753;esv3635754;esv3635755;esv3635756;esv3635757",
      "allAn": 5008,
      "allAc": 2702,
      "allAf": 0.539537,
      "afrAf": 0.6052,
      "amrAf": 0.3675,
      "eurAf": 0.5357,
      "easAf": 0.5368,
      "sasAf": 0.5797,
      "reciprocalOverlap": 0.07555
   }
],
FieldTypeNotes
chromosomestring
begininteger
endinteger
variantTypestring
idstring
allAnintegerallele number for all populations. Non-zero integer.
allAcintegerallele count for all populations. Integer.
allAffloating pointallele frequency for all populations. Range: 0 - 1.0
afrAffloating pointallele frequency for the African super population. Range: 0 - 1.0
amrAffloating pointallele frequency for the Ad Mixed American super population. Range: 0 - 1.0
eurAffloating pointallele frequency for the European super population. Range: 0 - 1.0
easAffloating pointallele frequency for the East Asian super population. Range: 0 - 1.0
sasAffloating pointallele frequency for the South Asian super population. Range: 0 - 1.0
reciprocalOverlapfloating pointrange: 0 - 1.
- - +
Skip to main content
Version: 3.22

1000Genomes-sv-json

"oneKg":[
   {
      "chromosome":"1",
      "begin":1595369,
      "end":1612441,
      "variantType": "copy_number_variation",
      "id": "esv3635753;esv3635754;esv3635755;esv3635756;esv3635757",
      "allAn": 5008,
      "allAc": 2702,
      "allAf": 0.539537,
      "afrAf": 0.6052,
      "amrAf": 0.3675,
      "eurAf": 0.5357,
      "easAf": 0.5368,
      "sasAf": 0.5797,
      "reciprocalOverlap": 0.07555
   }
],
FieldTypeNotes
chromosomestring
begininteger
endinteger
variantTypestring
idstring
allAnintegerallele number for all populations. Non-zero integer.
allAcintegerallele count for all populations. Integer.
allAffloating pointallele frequency for all populations. Range: 0 - 1.0
afrAffloating pointallele frequency for the African super population. Range: 0 - 1.0
amrAffloating pointallele frequency for the Ad Mixed American super population. Range: 0 - 1.0
eurAffloating pointallele frequency for the European super population. Range: 0 - 1.0
easAffloating pointallele frequency for the East Asian super population. Range: 0 - 1.0
sasAffloating pointallele frequency for the South Asian super population. Range: 0 - 1.0
reciprocalOverlapfloating pointrange: 0 - 1.
+ + \ No newline at end of file diff --git a/3.22/data-sources/1000Genomes/index.html b/3.22/data-sources/1000Genomes/index.html index 3cbf41ec..53609be1 100644 --- a/3.22/data-sources/1000Genomes/index.html +++ b/3.22/data-sources/1000Genomes/index.html @@ -6,15 +6,15 @@ 1000 Genomes | IlluminaConnectedAnnotations - - + +
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Version: 3.22

1000 Genomes

Overview

The goal of the 1000 Genomes Project was to find most genetic variants with frequencies of at least 1% in the populations studied. It was the first project to sequence the genomes of a large number of people, to provide a comprehensive resource on human genetic variation. Data from the 1000 Genomes Project was quickly made available to the worldwide scientific community through freely accessible public databases.

Publication

Sudmant, P., Rausch, T., Gardner, E. et al. An integrated map of structural variation in 2,504 human genomes. Nature 526, 75–81 (2015). https://doi.org/10.1038/nature15394

Populations

Small Variants

VCF File Parsing

The original VCF files come with allele frequency fields (e.g. ALL_AF, AMR_AF) but we recompute them using allele counts and allele numbers in order to get 6 digit precision. The allele counts and allele numbers (e.g. AMR_AC, AMR_AN) are not expressed in the INFO field. Instead the genotypes need to be parsed to compute that information. Our team converted the original data to VCF entries with allele counts and allele numbers like the following.

#CHROM  POS ID  REF ALT QUAL    FILTER  INFO
1       15274   rs62636497      A       G,T     100     PASS    AC=1739,3210;AF=0.347244,0.640974;AN=5008;NS=2504;DP=23255;EAS_AF=0.4812,0.5188;AMR_AF=0.2752,0.7205;AFR_AF=0.323,0.6369;EUR_AF=0.2922,0.7078;SAS_AF=0.3497,0.6472;AA=g|||;VT=SNP;MULTI_ALLELIC;EAS_AN=1008;EAS_AC=485,523;EUR_AN=1006;EUR_AC=294,712;AFR_AN=1322;AFR_AC=427,842;AMR_AN=694;AMR_AC=191,500;SAS_AN=978;SAS_AC=342,633

The ancestral allele, if it exists, is the first value in the pipe separated AA fields (the Indel specific REF, ALT, IndelType fields are ignored).

We parse the VCF file and extract the following fields from INFO:

  • AA
  • AC
  • AN
  • EAS_AN
  • AMR_AN
  • AFR_AN
  • EUR_AN
  • SAS_AN
  • EAS_AC
  • AMR_AC
  • AFR_AC
  • EUR_AC
  • SAS_AC

Conflict Resolution

We have observed conflicting allele frequency information in the source. Take the following example:

#CHROM  POS ID  REF ALT QUAL    FILTER  INFO
1 20505705 rs35377696 C CTCTG,CTG,CTGTG 100 PASS AC=46,1513,152;AF=0.0091853,0.302117,0.0303514;
1 20505705 rs35377696 C CTG 100 PASS AC=4;AF=0.000798722;

That is, the variant 1-20505705-C-CTG has conflicting entries. To get an idea of how frequently we observe this, here is a table summarizing ChrX and all chromosomes. Note that almost all such entries are found in ChrX.

Chromosome# of alleles# of conflicting allelespercentage
chrX83480027330.33%
Total2141309827430.013%

Currently, we removed the allele frequency of the conflicting allele (i.e., insertion TG in the example) but keep allele frequencies of all other alleles in the VCF line.

Potential Alternate Solutions

  • Remove all alleles that are contained in the vcf lines which have conflicting allele. (Recommended by 1000 genome group Holly Zheng-Bradley, 7/29/2015)
  • Recalculate the allele frequency for the conflicting allele.
  • Pick the allele frequency that has the highest data support.

Download URL

GRCh37 GRCh38

JSON Output

"oneKg":{
   "allAf":0.200879,
   "afrAf":0.210287,
   "amrAf":0.139769,
   "easAf":0.275794,
   "eurAf":0.181909,
   "sasAf":0.173824,
   "allAn":5008,
   "afrAn":1322,
   "amrAn":694,
   "easAn":1008,
   "eurAn":1006,
   "sasAn":978,
   "allAc":1006,
   "afrAc":278,
   "amrAc":97,
   "easAc":278,
   "eurAc":183,
   "sasAc":170
}
FieldTypeNotes
allAffloatallele frequency for all populations. Range: 0 - 1.0
allAcintallele count for all populations. Integer.
allAnintallele number for all populations. Non-zero integer.
afrAffloatallele frequency for the African super population. Range: 0 - 1.0
afrAcintallele count for the African super population. Integer.
afrAnintallele number for the African super population. Non-zero integer.
amrAffloatallele frequency for the Ad Mixed American super population. Range: 0 - 1.0
amrAcintallele count for the Ad Mixed American super population. Integer.
amrAnintallele number for the Ad Mixed American super population. Non-zero integer.
easAffloatallele frequency for the East Asian super population. Range: 0 - 1.0
easAcintallele count for the East Asian super population. Integer.
easAnintallele number for the East Asian super population. Non-zero integer.
eurAffloatallele frequency for the European super population. Range: 0 - 1.0
eurAcintallele count for the European super population. Integer.
eurAnintallele number for the European super population. Non-zero integer.
sasAffloatallele frequency for the South Asian super population. Range: 0 - 1.0
sasAcintallele count for the South Asian super population. Integer.
sasAnintallele number for the South Asian super population. Non-zero integer.

Structural Variants

VCF File Parsing

The VCF files contain entries like the following:

#CHROM  POS     ID      REF     ALT     QUAL    FILTER  INFO    FORMAT  HG00096 HG00097 HG00099 HG00100 HG00101 HG00102 HG00103
22      16050654        esv3647175;esv3647176;esv3647177;esv3647178     A       <CN0>,<CN2>,<CN3>,<CN4> 100     PASS  AC=9,87,599,20;AF=0.00179712,0.0173722,0.119609,0.00399361;AN=5008;CS=DUP_gs;END=16063474;NS=2504;SVTYPE=CNV;DP=22545;EAS_AF=0.001,0.0169,0.2361,0.0099;AMR_AF=0,0.0101,0.219,0.0072;AFR_AF=0.0061,0.0363,0.0053,0;EUR_AF=0,0.007,0.0944,0.003;SAS_AF=0,0.0082,0.1094,0.002;VT=SV       GT      3|0     0|0     0|0     0|0     0|0     0|0     0|4

Please note that, CNVs are allele-specific. For example, HG00096 is effectively copy number 4, which would be a net gain on chr22.

1000 Genomes contains 5 types of structural variants:

  • CNV
  • DEL
  • DUP
  • INS
  • INV

Since data of 1000 genomes is provided in VCF format, we assume that the coordinates follow the vcf format, i.e., there is a padding base for symbolic alleles. So all the interval can be interpreted as [BEGIN+1, END]. -Similarly, for all other variant types except insertion, END is far larger than BEGIN. The distribution of BEGIN and END for insertions is summarized below.

Insertion issues

  • END = BEGIN for 6/165
  • END = BEGIN+2 for 93/165
  • END = BEGIN+3 for 11/165
  • END = BEGIN+4 for 11/165
  • END – BEGIN range from 5 to 1156 for others.

Converting VCF svTypes to SO sequence alterations

The svType will be captured in our JSON file under the sequenceAlteration key. Here's the translation we'll use according to svType in 1000 Genomes.

svTypeAlternative Alleles contain <CN*>sequenceAlteration
ALUFALSEmobile_element_insertion
DUPTRUEcopy_number_gain
CNVTRUEcopy_number_gain (observed_gains >0 and observed_losses =0)
copy_number_loss (observed_gains = 0 and observed_losses > 0)
copy_number_variation (otherwise)
DELTRUEcopy_number_loss
LINE1FALSEmobile_element_insertion
SVAFALSEmobile_element_insertion
INVFALSEinversion
INSFALSEinsertion

Exceptions

We discard structural variants without END

#CHROM  POS     ID      REF     ALT     QUAL    FILTER  INFO    FORMAT  HG00096 HG00097 HG00099 HG00100 HG00101 HG00102 HG00103
21      9495848 esv3646347      A       <INS:ME:LINE1>  100     PASS   AC=1543;AF=0.308107;AN=5008;CS=L1_umary;MEINFO=LINE1,5669,6005,+;NS=2504;SVLEN=336;SVTYPE=LINE1;TSD=null;DP=20015;EAS_AF=0.3125;AMR_AF=0.2911;AFR_AF=0.3026;EUR_AF=0.2922;SAS_AF=0.3395;VT=SV   GT      0|0     1|1     1|0     0|1     1|0     1|0     0|0

CNVs in chrY

  • No other types of structural variants exist in chrY
  • Since copy number is provided in genotype field, we directly parse the copy number from "CN" field.
  • For most CNVs in chrY, the reference copy number is 1, but the refence number for CNVs in segmental duplication sites is 2 (<CN2> in the 2nd example). All segmental duplication calls have identifiers starting with GS_SD_M2.
#CHROM  POS     ID      REF     ALT     QUAL    FILTER  INFO    FORMAT  HG00096 HG00101 HG00103 HG00105 HG00107 HG00108
Y       2888555 CNV_Y_2888555_3014661   T       <CN2>   100     PASS    AC=1;AF=0.000817661;AN=1223;END=3014661;NS=1233;SVTYPE=CNV;AMR_AF=0.0000;AFR_AF=0.0000;EUR_AF=0.0000;SAS_AF=0.0019;EAS_AF=0.0000;VT=SV  GT:CN:CNL:CNP:CNQ:GP:GQ:PL      0:1:-1000,0,-58.45:-1000,0,-61.55:99:0,-61.55:99:0,585  0:1:-296.36,0,-16.6:-300.46,0,-19.7:99:0,-19.7:99:0,166 0:1:-1000,0,-39.44:-1000,0,-42.54:99:0,-42.54:99:0,394
Y       6128381 GS_SD_M2_Y_6128381_6230094_Y_9650284_9752225    C       <CN1>,<CN3>     100     PASS    AC=4,2;AF=0.00327065,0.00163532;AN=1223;END=6230094;NS=1233;SVTYPE=CNV;AMR_AF=0.0029,0.0029;AFR_AF=0.0016,0.0016;EUR_AF=0.0000,0.0000;SAS_AF=0.0038,0.0000;EAS_AF=0.0000,0.0000;VT=SV;EX_TARGET GT:CN:CNL:CNP:CNQ:GP:GQ 0:2:-1000,-138.78,0,-38.53:-1000,-141.27,0,-41.33:99:0,-141.27,-41.33:99        0:2:-1000,-53.32,0,-17.85:-1000,-55.81,0,-20.64:99:0,-55.81,-20.64:99   0:2:-1000,-71.83,0,-32.5:-1000,-74.32,0,-35.29:99:0,-74.32,-35.29:99    0:2:-1000,-60.96,0,-20.29:-1000,-63.45,0,-23.08:99:0,-63.45,-23.08:99   0:2:-1000,-77.6,0,-31.45:-1000,-80.09,0,-34.24:99:0,-80.09,-34.24:99

JSON Output

"oneKg":[
   {
      "chromosome":"1",
      "begin":1595369,
      "end":1612441,
      "variantType": "copy_number_variation",
      "id": "esv3635753;esv3635754;esv3635755;esv3635756;esv3635757",
      "allAn": 5008,
      "allAc": 2702,
      "allAf": 0.539537,
      "afrAf": 0.6052,
      "amrAf": 0.3675,
      "eurAf": 0.5357,
      "easAf": 0.5368,
      "sasAf": 0.5797,
      "reciprocalOverlap": 0.07555
   }
],
FieldTypeNotes
chromosomestring
begininteger
endinteger
variantTypestring
idstring
allAnintegerallele number for all populations. Non-zero integer.
allAcintegerallele count for all populations. Integer.
allAffloating pointallele frequency for all populations. Range: 0 - 1.0
afrAffloating pointallele frequency for the African super population. Range: 0 - 1.0
amrAffloating pointallele frequency for the Ad Mixed American super population. Range: 0 - 1.0
eurAffloating pointallele frequency for the European super population. Range: 0 - 1.0
easAffloating pointallele frequency for the East Asian super population. Range: 0 - 1.0
sasAffloating pointallele frequency for the South Asian super population. Range: 0 - 1.0
reciprocalOverlapfloating pointrange: 0 - 1.
- - +Similarly, for all other variant types except insertion, END is far larger than BEGIN. The distribution of BEGIN and END for insertions is summarized below.

Insertion issues

Converting VCF svTypes to SO sequence alterations

The svType will be captured in our JSON file under the sequenceAlteration key. Here's the translation we'll use according to svType in 1000 Genomes.

svTypeAlternative Alleles contain <CN*>sequenceAlteration
ALUFALSEmobile_element_insertion
DUPTRUEcopy_number_gain
CNVTRUEcopy_number_gain (observed_gains >0 and observed_losses =0)
copy_number_loss (observed_gains = 0 and observed_losses > 0)
copy_number_variation (otherwise)
DELTRUEcopy_number_loss
LINE1FALSEmobile_element_insertion
SVAFALSEmobile_element_insertion
INVFALSEinversion
INSFALSEinsertion

Exceptions

We discard structural variants without END

#CHROM  POS     ID      REF     ALT     QUAL    FILTER  INFO    FORMAT  HG00096 HG00097 HG00099 HG00100 HG00101 HG00102 HG00103
21      9495848 esv3646347      A       <INS:ME:LINE1>  100     PASS   AC=1543;AF=0.308107;AN=5008;CS=L1_umary;MEINFO=LINE1,5669,6005,+;NS=2504;SVLEN=336;SVTYPE=LINE1;TSD=null;DP=20015;EAS_AF=0.3125;AMR_AF=0.2911;AFR_AF=0.3026;EUR_AF=0.2922;SAS_AF=0.3395;VT=SV   GT      0|0     1|1     1|0     0|1     1|0     1|0     0|0

CNVs in chrY

#CHROM  POS     ID      REF     ALT     QUAL    FILTER  INFO    FORMAT  HG00096 HG00101 HG00103 HG00105 HG00107 HG00108
Y       2888555 CNV_Y_2888555_3014661   T       <CN2>   100     PASS    AC=1;AF=0.000817661;AN=1223;END=3014661;NS=1233;SVTYPE=CNV;AMR_AF=0.0000;AFR_AF=0.0000;EUR_AF=0.0000;SAS_AF=0.0019;EAS_AF=0.0000;VT=SV  GT:CN:CNL:CNP:CNQ:GP:GQ:PL      0:1:-1000,0,-58.45:-1000,0,-61.55:99:0,-61.55:99:0,585  0:1:-296.36,0,-16.6:-300.46,0,-19.7:99:0,-19.7:99:0,166 0:1:-1000,0,-39.44:-1000,0,-42.54:99:0,-42.54:99:0,394
Y       6128381 GS_SD_M2_Y_6128381_6230094_Y_9650284_9752225    C       <CN1>,<CN3>     100     PASS    AC=4,2;AF=0.00327065,0.00163532;AN=1223;END=6230094;NS=1233;SVTYPE=CNV;AMR_AF=0.0029,0.0029;AFR_AF=0.0016,0.0016;EUR_AF=0.0000,0.0000;SAS_AF=0.0038,0.0000;EAS_AF=0.0000,0.0000;VT=SV;EX_TARGET GT:CN:CNL:CNP:CNQ:GP:GQ 0:2:-1000,-138.78,0,-38.53:-1000,-141.27,0,-41.33:99:0,-141.27,-41.33:99        0:2:-1000,-53.32,0,-17.85:-1000,-55.81,0,-20.64:99:0,-55.81,-20.64:99   0:2:-1000,-71.83,0,-32.5:-1000,-74.32,0,-35.29:99:0,-74.32,-35.29:99    0:2:-1000,-60.96,0,-20.29:-1000,-63.45,0,-23.08:99:0,-63.45,-23.08:99   0:2:-1000,-77.6,0,-31.45:-1000,-80.09,0,-34.24:99:0,-80.09,-34.24:99

JSON Output

"oneKg":[
   {
      "chromosome":"1",
      "begin":1595369,
      "end":1612441,
      "variantType": "copy_number_variation",
      "id": "esv3635753;esv3635754;esv3635755;esv3635756;esv3635757",
      "allAn": 5008,
      "allAc": 2702,
      "allAf": 0.539537,
      "afrAf": 0.6052,
      "amrAf": 0.3675,
      "eurAf": 0.5357,
      "easAf": 0.5368,
      "sasAf": 0.5797,
      "reciprocalOverlap": 0.07555
   }
],
FieldTypeNotes
chromosomestring
begininteger
endinteger
variantTypestring
idstring
allAnintegerallele number for all populations. Non-zero integer.
allAcintegerallele count for all populations. Integer.
allAffloating pointallele frequency for all populations. Range: 0 - 1.0
afrAffloating pointallele frequency for the African super population. Range: 0 - 1.0
amrAffloating pointallele frequency for the Ad Mixed American super population. Range: 0 - 1.0
eurAffloating pointallele frequency for the European super population. Range: 0 - 1.0
easAffloating pointallele frequency for the East Asian super population. Range: 0 - 1.0
sasAffloating pointallele frequency for the South Asian super population. Range: 0 - 1.0
reciprocalOverlapfloating pointrange: 0 - 1.
+ + \ No newline at end of file diff --git a/3.22/data-sources/amino-acid-conservation-json/index.html b/3.22/data-sources/amino-acid-conservation-json/index.html index 3ae61ace..df128f0a 100644 --- a/3.22/data-sources/amino-acid-conservation-json/index.html +++ b/3.22/data-sources/amino-acid-conservation-json/index.html @@ -6,13 +6,13 @@ amino-acid-conservation-json | IlluminaConnectedAnnotations - - + +
-
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Version: 3.22

amino-acid-conservation-json

"aminoAcidConservation": {
    "scores": [0.34]
}
FieldTypeNotes
aminoAcidConservationobject
scoresobject array of doublespercent conserved with respect to human amino acid residue. Range: 0.01 - 1.00
- - +
Skip to main content
Version: 3.22

amino-acid-conservation-json

"aminoAcidConservation": {
    "scores": [0.34]
}
FieldTypeNotes
aminoAcidConservationobject
scoresobject array of doublespercent conserved with respect to human amino acid residue. Range: 0.01 - 1.00
+ + \ No newline at end of file diff --git a/3.22/data-sources/amino-acid-conservation/index.html b/3.22/data-sources/amino-acid-conservation/index.html index e6985187..eccb7511 100644 --- a/3.22/data-sources/amino-acid-conservation/index.html +++ b/3.22/data-sources/amino-acid-conservation/index.html @@ -6,14 +6,14 @@ Amino Acid Conservation | IlluminaConnectedAnnotations - - + +
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Version: 3.22

Amino Acid Conservation

Overview

Amino acid conservation scores are obtained from multiple alignments of vertebrate exomes to the human ones. The score indicate the frequency with which a particular AA is observed in Humans.

Publication

Siepel A, Bejerano G, Pedersen JS, Hinrichs AS, Hou M, Rosenbloom K, Clawson H, Spieth J, Hillier LW, Richards S, et al. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res. 2005 Aug;15(8):1034-50. (http://www.genome.org/cgi/doi/10.1101/gr.3715005)

FASTA File

The exon alignments are provided in FASTA files as follows:

>ENST00000641515.2_hg38_1_2 3 0 0 chr1:65565-65573+
MKK
>ENST00000641515.2_panTro4_1_2 3 0 0 chrUn_GL393541:146907-146915+
MKK
>ENST00000641515.2_gorGor3_1_2 3 0 0
---
>ENST00000641515.2_ponAbe2_1_2 3 0 0 chr15:99141417-99141425-
MKK
>ENST00000641515.2_hg38_2_2 324 0 0 chr1:69037-70008+
VTAEAISWNESTSETNNSMVTEFIFLGLSDSQELQTFLFMLFFVFYGGIVFGNLLIVITVVSDSHLHSPMYFLLANLSLIDLSLSSVTAPKMITDFFSQRKVISFKGCLVQIFLLHFFGGSEMVILIAMGFDRYIAICKPLHYTTIMCGNACVGIMAVTWGIGFLHSVSQLAFAVHLLFCGPNEVDSFYCDLPRVIKLACTDTYRLDIMVIANSGVLTVCSFVLLIISYTIILMTIQHRPLDKSSKALSTLTAHITVVLLFFGPCVFIYAWPFPIKSLDKFLAVFYSVITPLLNPIIYTLRNKDMKTAIRQLRKWDAHSSVKFZ
>ENST00000641515.2_panTro4_2_2 324 0 0 chrUn_GL393541:151333-152303+

Parsing FASTA

For each Ensembl transcript, we will need to aggregate all the exons together for each of the 100 species. From there, we should get a full alignment that can be used to determine conservation. For example, for ENST00000641515.2 we have:

Human (hg38) MKKVTAEAISWNESTSETNNSMVTEFIFLGLSDSQELQTFLFMLFFVFYGGIVFGNLLIVITVVSDSHLHSPMYFLLANLSLIDLSLSSVTAPKMITDFFSQRKVISFKGCLVQIFLL
Chimp        MKKVTAEAISWNESTSETNNSMVTEFIFLGLSDSQELQTFL-MLFFVFYGGIVFGNLLIVRIVVSDSHLHSPMYFLLANLSLIDLSLCSVTAPKMITDFFSQRKVISFKGCLVQIFLL
Gorilla      ----------------------------------------------------------------------------------------------------------------------
Orangutan    MKKVTAEAISWNESTSKTNNSVVTEFIFLGLSDSQELQTFLFMLFFVFYGGIVFGNLLIVIIVVSDSHLHSPMYFLLANLSLIDLSLSSVTAPKMITDFFSQRKVISFKGCLVQIFLL
Gibbon       ----------------------------------------------------------------------------------------------------------------------
Rhesus       MKKVTEAAISWNESTSETNNSIVTEFIFLGLSDSQELQIFLFVLFLVFYGGIVFGNLLIVITVVSDSHLHSPMYLLLANLSVVDLSLSSVTAPKMITDFFSQRKAISFKGCLVQIFLL
Macaque      MKKVTEAAISWNESTSETNNSIVTEFIFLGLSDSQELQIFLFVLFLVFYGGIVFGNLLIVITVVSDSHLHSPMYLLLANLSVIDLSLSSVTAPKMITDFFSQRKAISFKGCLVQIFLL

If we look at position 6, we see that humans have an Alanine (A) residue. This residue is shared by Chimp and Orangutan. However, Rhesus and Macaque have a Glutamic acid (E) residue at that position. Moreover, Gorilla and Gibbon don't even have data for that transcript. -For position 6, we would say that we have 43% conservation (3/7) since three organisms share the same residue as humans.

Assigning scores to Illumina Connected Annotations transcripts

The source FASTA file comes with Ensembl/UCSC transcript ids of the transcripts used for alignments. The Illumina Connected Annotations cache has RefSeq and Ensembl transcripts and our first attempt was to map the given Ensembl/UCSC ids to their equivalent RefSeq/Ensembl ids. This attempt was unsuccessful since UCSC Table Browser provided mapping without version numbers. So we proceeded as follows:

  • Take proteins which have a unique mapping (and hence one set of conservation scores). For ones that mapped to both ChrX and ChrY, we accepted the one from ChrX.
  • A Illumina Connected Annotations transcript having an exact peptide sequence match with a uniquely aligned protein is assigned the corresponding conservation scores.

Unfortunately this left us with a very small number of transcripts having conservation scores.

GRCh37

  • Source FASTA contained 41957 protein alignments.
  • 38165 proteins had unique scores.
  • 88 aligned proteins existed in Illumina Connected Annotations cache.
  • 118 transcripts had conservation scores.

GRCh38

  • Source FASTA contained 110024 protein alignments.
  • 88961 proteins had unique scores.
  • 11688 aligned proteins existed in Illumina Connected Annotations cache.
  • 12098 transcripts had conservation scores.

Download URL

GRCh37: http://hgdownload.soe.ucsc.edu/goldenPath/hg19/multiz100way/alignments/knownGene.exonAA.fa.gz

GRCh38: http://hgdownload.soe.ucsc.edu/goldenPath/hg38/multiz100way/alignments/knownGene.exonAA.fa.gz

JSON Output

Conservation scores are reported in the transcript section. One score is reported for each alt allele

"aminoAcidConservation": {
    "scores": [0.34]
}
FieldTypeNotes
aminoAcidConservationobject
scoresobject array of doublespercent conserved with respect to human amino acid residue. Range: 0.01 - 1.00
- - +For position 6, we would say that we have 43% conservation (3/7) since three organisms share the same residue as humans.

Assigning scores to Illumina Connected Annotations transcripts

The source FASTA file comes with Ensembl/UCSC transcript ids of the transcripts used for alignments. The Illumina Connected Annotations cache has RefSeq and Ensembl transcripts and our first attempt was to map the given Ensembl/UCSC ids to their equivalent RefSeq/Ensembl ids. This attempt was unsuccessful since UCSC Table Browser provided mapping without version numbers. So we proceeded as follows:

Unfortunately this left us with a very small number of transcripts having conservation scores.

GRCh37

GRCh38

Download URL

GRCh37: http://hgdownload.soe.ucsc.edu/goldenPath/hg19/multiz100way/alignments/knownGene.exonAA.fa.gz

GRCh38: http://hgdownload.soe.ucsc.edu/goldenPath/hg38/multiz100way/alignments/knownGene.exonAA.fa.gz

JSON Output

Conservation scores are reported in the transcript section. One score is reported for each alt allele

"aminoAcidConservation": {
    "scores": [0.34]
}
FieldTypeNotes
aminoAcidConservationobject
scoresobject array of doublespercent conserved with respect to human amino acid residue. Range: 0.01 - 1.00
+ + \ No newline at end of file diff --git a/3.22/data-sources/cancer-hotspots/index.html b/3.22/data-sources/cancer-hotspots/index.html index 1359e10d..a260ea01 100644 --- a/3.22/data-sources/cancer-hotspots/index.html +++ b/3.22/data-sources/cancer-hotspots/index.html @@ -6,14 +6,14 @@ Cancer Hotspots | IlluminaConnectedAnnotations - - + +
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Version: 3.22

Cancer Hotspots

Overview

Cancer Hotspots, a resource for statistically significant mutations in cancer. It provides information about statistically significantly recurrent mutations identified in large scale cancer genomics data.

Publication

Chang MT, Bhattarai TS, Schram AM, Bielski CM, Donoghue MTA, Jonsson P, Chakravarty D, Phillips S, Kandoth C, Penson A, Gorelick A, Shamu T, Patel S, Harris C, Gao J, Sumer SO, Kundra R, Razavi P, Li BT, Reales DN, Socci ND, Jayakumaran G, Zehir A, Benayed R, Arcila ME, Chandarlapaty S, Ladanyi M, Schultz N, Baselga J, Berger MF, Rosen N, Solit DB, Hyman DM, Taylor BS. Accelerating Discovery of Functional Mutant Alleles in Cancer. Cancer Discov. 2018 Feb;8(2):174-183. doi: 10.1158/2159-8290.CD-17-0321. Epub 2017 Dec 15. PMID: 29247016; PMCID: PMC5809279.

Chang MT, Asthana S, Gao SP, Lee BH, Chapman JS, Kandoth C, Gao J, Socci ND, Solit DB, Olshen AB, Schultz N, Taylor BS. Identifying recurrent mutations in cancer reveals widespread lineage diversity and mutational specificity. Nat Biotechnol. 2016 Feb;34(2):155-63. doi: 10.1038/nbt.3391. Epub 2015 Nov 30. PMID: 26619011; PMCID: PMC4744099.

Data extraction

Illumina Connected Annotations currently parses SNV and indel tabs from hotspots_v2.xls file to extract the relevant content.

Example

SNV

Hugo_Symbol     Amino_Acid_Position     log10_pvalue    Mutation_Count  Reference_Amino_Acid    Total_Mutations_in_Gene Median_Allele_Freq_Rank Allele_Freq_Rank        Variant_Amino_Acid   Codon_Change     Genomic_Position        Detailed_Cancer_Types   Organ_Types     Tri-nucleotides Mutability      mu_protein      Total_Samples   Analysis_Type   qvalue  tm      qvalue_pancanIs_repeat        seq     length  align100        pad12entropy    pad24entropy    pad36entropy    TP      reason  n_MSK   n_Retro judgement       inNBT   inOncokb        ref     qvaluect     ct       Samples
NRAS    61      -1237.69143477067       422     Q:422   620     0.333333333333333       295|0.692307692307692:0.733333333333333:0.2:0.933333333333333:1:0.25:0.666666666666667:1:0.25:0.571428571428571:1:1:0.5:0.363636363636364:0.428571428571429:0.0833333333333333:1:1:1:1:0.5:1:0.125:0.363636363636364:0.173913043478261:0.25:1:0.8:0.153846153846154:0.857142857142857:0.5:0.5:0.5:1:0.272727272727273:0.214285714285714:1:0.5:1:1:0.2:0.333333333333333:0.6875:0.708333333333333:0.25:0.266666666666667:0.111111111111111:1:1:0.333333333333333:0.428571428571429:0.666666666666667:0.25:0.5:0.833333333333333:0.5:0.735294117647059:0.0476190476190476:0.1:0.133333333333333:0.230769230769231:0.25:1:0.5:0.294117647058824:0.217391304347826:0.46875:0.5:1:0.2:0.166666666666667:0.666666666666667:1:0.8:0.407407407407407:1:0.0212765957446809:0.285714285714286:0.0909090909090909:0.333333333333333:0.2:0.333333333333333:0.5:0.5:1:0.111111111111111:0.5:0.903846153846154:0.5:0.2:1:1:0.0909090909090909:0.4:0.428571428571429:0.0625:0.25:0.833333333333333:1:0.956521739130435:0.111111111111111:0.6:0.212765957446809:0.5:0.207547169811321:1:0.75:0.294117647058824:0.666666666666667:1:0.333333333333333:0.714285714285714:0.142857142857143:1:0.3:0.416666666666667:0.272727272727273:0.25:0.333333333333333:0.345454545454545:0.0952380952380952:0.166666666666667:0.111111111111111:0.454545454545455:0.0666666666666667:1:0.636363636363636:0.636363636363636:0.25:0.272727272727273:0.824324324324324:1:0.75:0.545454545454545:1:1:0.0769230769230769:0.363636363636364:0.290322580645161:0.333333333333333:0.179487179487179:1:0.0666666666666667:0.333333333333333:1:0.478260869565217:0.166666666666667:1:1:0.0276497695852535:0.0716845878136201:0.0263736263736264:0.933333333333333:1:0.5:1:1:0.8125:0.361788617886179:0.113761467889908:0.113761467889908:0.157894736842105:0.333333333333333:0.0555555555555556:0.0357142857142857:0.375:0.111111111111111:0.584415584415584:0.0350877192982456:0.751111111111111:0.761245674740484:0.164989939637827:0.196652719665272:0.135549872122762:0.172113289760349:0.0240963855421687:0.0620767494356659:0.142268041237113:0.147441457068517:0.147959183673469:0.038961038961039:0.686274509803922:0.0929054054054054:0.364787111622555:0.331306990881459:0.691449814126394:0.691449814126394:0.0769230769230769:0.347826086956522:0.117647058823529:0.148148148148148:0.05:0.290030211480363:0.680272108843537:0.188679245283019:0.0701754385964912:0.801526717557252:0.236842105263158:0.1953125:0.0539906103286385:0.015625:0.0390492359932088:0.00790513833992095:0.0597826086956522:0.136783733826248:0.362359550561798:0.0713719270420301:0.328621908127208:0.0657672849915683:0.320099255583127:0.075:0.433021806853583:0.524818401937046:0.524818401937046:0.259259259259259:0.483695652173913:0.0269360269360269:0.100486223662885:0.785507246376812:0.137870855148342:0.472340425531915:0.194331983805668:0.0830769230769231:0.418055555555556:0.546296296296296:0.247596153846154:0.52:0.39832285115304:0.601866251944012:0.234016887816647:0.214007782101167:0.153153153153153:0.137180700094607:0.0666666666666667:0.037037037037037:0.1:0.2:0.458333333333333:0.0588235294117647:0.111111111111111:0.333333333333333:0.181818181818182:0.473684210526316:0.5:0.2:0.136363636363636:0.0769230769230769:0.142857142857143:0.285714285714286:0.25:0.445714285714286:0.149377593360996:0.0227790432801822:0.182278481012658:0.540123456790123:0.021505376344086:0.541666666666667:0.00429184549356223:0.473684210526316:0.103508771929825:0.0930232558139535:0.391304347826087:0.072:0.0113636363636364:0.148837209302326:0.448051948051948:0.761038961038961:0.530373831775701:0.222857142857143:0.433862433862434:0.0810810810810811:0.0723327305605787:0.410714285714286:0.247910863509749:0.384615384615385:0.125:0.24:0.783582089552239:0.0646651270207852:0.445569620253165:0.754777070063694:0.165137614678899:0.10732538330494:0.0375:0.538461538461538:0.0981387478849408:0.029126213592233:0.0833333333333333:0.443514644351464:0.0917431192660551:0.03125:0.674418604651163:0.3125:0.375:0.314285714285714     R:204   cAa/cGa:203|Caa/Aaa:140|cAa/cTa:46|caA/caT:14|caA/caC:13|ggACaa/ggCAaa:2|cAa/cCa:2|Caa/Taa:1|CAa/AGa:1  1:115256529_252|1:115256530_143|1:115256528_27        skcm:787:186|thpa:486:43|mm:275:27|thpd:58:18|coadread:683:16|luad:2057:15|coad:712:13|mup:42:7|aml:198:6|blca:852:5|thap:33:5|read:149:5|rms:50:5|uec:339:5|nsgct:152:5|cll:283:4|ihch:104:4|lgsoc:17:3|sem:59:3|thhc:21:3|erms:8:3|lggnos:544:3|utuc:76:2|cup:135:2|thfo:5:2|sarcl:13:2|mfh:53:2|gbm:688:2|soc:468:2|stad:748:2|thym:125:2|es:229:1|npc:66:1|unk:146:1|panet:86:1|hnsc:643:1|armm:21:1|tmt:3:1|acrm:23:1|thyc:9:1|odg:36:1|paasc:8:1|hnmucm:11:1|blad:7:1|esca:556:1|mixed:3:1|chol:152:1|hcc:620:1|sarc:280:1|chrcc:88:1|aca:93:1     skin:974:187|thyroid:618:71|blood:890:37|bowel:1782:35|lung:2761:17|unk:357:11|softtissue:739:11|testis:217:9|bladder:958:8|cnsbrain:2270:6|ovaryfallopiantube:699:5|biliarytract:358:5|uterus:618:5|headandneck:988:3|thymus:162:3|esophagusstomach:1407:3|pancreas:1059:2|bone:297:1|liver:636:1|kidney:1304:1|adrenalgland:291:1    TTG|ACA|CTT|TCG|CCC|CCA 0.0120300464273379      0.0267810594223141    24592   "pancan,skin,thyroid,bowel,blood,lung,softtissue,testis,bladder,cnsbrain,biliarytract,ovaryfallopiantube,uterus,thymus,headandneck,esophagusstomach"    0       NRAS 61       0       FALSE           NA      1       1.16795714944678        1.26187131041539        1.29838371117394        TRUE            165     257     RETAIN  TRUE    TRUE    Q       0    skin     skin:88|thyroid:54|blood:15|bowel:8|testis:5|biliarytract:4|bladder:4|lung:4|ovaryfallopiantube:4|softtissue:3|unk:3|uterus:3|cnsbrain:2|esophagusstomach:2|headandneck:2|bone:1|pancreas:1|thymus:1
NRAS    61      -1237.69143477067       422     Q:422   620     0.333333333333333       295|0.692307692307692:0.733333333333333:0.2:0.933333333333333:1:0.25:0.666666666666667:1:0.25:0.571428571428571:1:1:0.5:0.363636363636364:0.428571428571429:0.0833333333333333:1:1:1:1:0.5:1:0.125:0.363636363636364:0.173913043478261:0.25:1:0.8:0.153846153846154:0.857142857142857:0.5:0.5:0.5:1:0.272727272727273:0.214285714285714:1:0.5:1:1:0.2:0.333333333333333:0.6875:0.708333333333333:0.25:0.266666666666667:0.111111111111111:1:1:0.333333333333333:0.428571428571429:0.666666666666667:0.25:0.5:0.833333333333333:0.5:0.735294117647059:0.0476190476190476:0.1:0.133333333333333:0.230769230769231:0.25:1:0.5:0.294117647058824:0.217391304347826:0.46875:0.5:1:0.2:0.166666666666667:0.666666666666667:1:0.8:0.407407407407407:1:0.0212765957446809:0.285714285714286:0.0909090909090909:0.333333333333333:0.2:0.333333333333333:0.5:0.5:1:0.111111111111111:0.5:0.903846153846154:0.5:0.2:1:1:0.0909090909090909:0.4:0.428571428571429:0.0625:0.25:0.833333333333333:1:0.956521739130435:0.111111111111111:0.6:0.212765957446809:0.5:0.207547169811321:1:0.75:0.294117647058824:0.666666666666667:1:0.333333333333333:0.714285714285714:0.142857142857143:1:0.3:0.416666666666667:0.272727272727273:0.25:0.333333333333333:0.345454545454545:0.0952380952380952:0.166666666666667:0.111111111111111:0.454545454545455:0.0666666666666667:1:0.636363636363636:0.636363636363636:0.25:0.272727272727273:0.824324324324324:1:0.75:0.545454545454545:1:1:0.0769230769230769:0.363636363636364:0.290322580645161:0.333333333333333:0.179487179487179:1:0.0666666666666667:0.333333333333333:1:0.478260869565217:0.166666666666667:1:1:0.0276497695852535:0.0716845878136201:0.0263736263736264:0.933333333333333:1:0.5:1:1:0.8125:0.361788617886179:0.113761467889908:0.113761467889908:0.157894736842105:0.333333333333333:0.0555555555555556:0.0357142857142857:0.375:0.111111111111111:0.584415584415584:0.0350877192982456:0.751111111111111:0.761245674740484:0.164989939637827:0.196652719665272:0.135549872122762:0.172113289760349:0.0240963855421687:0.0620767494356659:0.142268041237113:0.147441457068517:0.147959183673469:0.038961038961039:0.686274509803922:0.0929054054054054:0.364787111622555:0.331306990881459:0.691449814126394:0.691449814126394:0.0769230769230769:0.347826086956522:0.117647058823529:0.148148148148148:0.05:0.290030211480363:0.680272108843537:0.188679245283019:0.0701754385964912:0.801526717557252:0.236842105263158:0.1953125:0.0539906103286385:0.015625:0.0390492359932088:0.00790513833992095:0.0597826086956522:0.136783733826248:0.362359550561798:0.0713719270420301:0.328621908127208:0.0657672849915683:0.320099255583127:0.075:0.433021806853583:0.524818401937046:0.524818401937046:0.259259259259259:0.483695652173913:0.0269360269360269:0.100486223662885:0.785507246376812:0.137870855148342:0.472340425531915:0.194331983805668:0.0830769230769231:0.418055555555556:0.546296296296296:0.247596153846154:0.52:0.39832285115304:0.601866251944012:0.234016887816647:0.214007782101167:0.153153153153153:0.137180700094607:0.0666666666666667:0.037037037037037:0.1:0.2:0.458333333333333:0.0588235294117647:0.111111111111111:0.333333333333333:0.181818181818182:0.473684210526316:0.5:0.2:0.136363636363636:0.0769230769230769:0.142857142857143:0.285714285714286:0.25:0.445714285714286:0.149377593360996:0.0227790432801822:0.182278481012658:0.540123456790123:0.021505376344086:0.541666666666667:0.00429184549356223:0.473684210526316:0.103508771929825:0.0930232558139535:0.391304347826087:0.072:0.0113636363636364:0.148837209302326:0.448051948051948:0.761038961038961:0.530373831775701:0.222857142857143:0.433862433862434:0.0810810810810811:0.0723327305605787:0.410714285714286:0.247910863509749:0.384615384615385:0.125:0.24:0.783582089552239:0.0646651270207852:0.445569620253165:0.754777070063694:0.165137614678899:0.10732538330494:0.0375:0.538461538461538:0.0981387478849408:0.029126213592233:0.0833333333333333:0.443514644351464:0.0917431192660551:0.03125:0.674418604651163:0.3125:0.375:0.314285714285714     K:142   cAa/cGa:203|Caa/Aaa:140|cAa/cTa:46|caA/caT:14|caA/caC:13|ggACaa/ggCAaa:2|cAa/cCa:2|Caa/Taa:1|CAa/AGa:1  1:115256529_252|1:115256530_143|1:115256528_27        skcm:787:186|thpa:486:43|mm:275:27|thpd:58:18|coadread:683:16|luad:2057:15|coad:712:13|mup:42:7|aml:198:6|blca:852:5|thap:33:5|read:149:5|rms:50:5|uec:339:5|nsgct:152:5|cll:283:4|ihch:104:4|lgsoc:17:3|sem:59:3|thhc:21:3|erms:8:3|lggnos:544:3|utuc:76:2|cup:135:2|thfo:5:2|sarcl:13:2|mfh:53:2|gbm:688:2|soc:468:2|stad:748:2|thym:125:2|es:229:1|npc:66:1|unk:146:1|panet:86:1|hnsc:643:1|armm:21:1|tmt:3:1|acrm:23:1|thyc:9:1|odg:36:1|paasc:8:1|hnmucm:11:1|blad:7:1|esca:556:1|mixed:3:1|chol:152:1|hcc:620:1|sarc:280:1|chrcc:88:1|aca:93:1     skin:974:187|thyroid:618:71|blood:890:37|bowel:1782:35|lung:2761:17|unk:357:11|softtissue:739:11|testis:217:9|bladder:958:8|cnsbrain:2270:6|ovaryfallopiantube:699:5|biliarytract:358:5|uterus:618:5|headandneck:988:3|thymus:162:3|esophagusstomach:1407:3|pancreas:1059:2|bone:297:1|liver:636:1|kidney:1304:1|adrenalgland:291:1    TTG|ACA|CTT|TCG|CCC|CCA 0.0120300464273379      0.0267810594223141    24592   "pancan,skin,thyroid,bowel,blood,lung,softtissue,testis,bladder,cnsbrain,biliarytract,ovaryfallopiantube,uterus,thymus,headandneck,esophagusstomach"    0       NRAS 61       0       FALSE           NA      1       1.16795714944678        1.26187131041539        1.29838371117394        TRUE            165     257     RETAIN  TRUE    TRUE    Q       0    skin     skin:62|bowel:18|thyroid:17|blood:12|softtissue:6|lung:5|unk:5|bladder:3|cnsbrain:2|thymus:2|adrenalgland:1|biliarytract:1|esophagusstomach:1|headandneck:1|kidney:1|liver:1|ovaryfallopiantube:1|pancreas:1|testis:1|uterus:1
NRAS    61      -1237.69143477067       422     Q:422   620     0.333333333333333       295|0.692307692307692:0.733333333333333:0.2:0.933333333333333:1:0.25:0.666666666666667:1:0.25:0.571428571428571:1:1:0.5:0.363636363636364:0.428571428571429:0.0833333333333333:1:1:1:1:0.5:1:0.125:0.363636363636364:0.173913043478261:0.25:1:0.8:0.153846153846154:0.857142857142857:0.5:0.5:0.5:1:0.272727272727273:0.214285714285714:1:0.5:1:1:0.2:0.333333333333333:0.6875:0.708333333333333:0.25:0.266666666666667:0.111111111111111:1:1:0.333333333333333:0.428571428571429:0.666666666666667:0.25:0.5:0.833333333333333:0.5:0.735294117647059:0.0476190476190476:0.1:0.133333333333333:0.230769230769231:0.25:1:0.5:0.294117647058824:0.217391304347826:0.46875:0.5:1:0.2:0.166666666666667:0.666666666666667:1:0.8:0.407407407407407:1:0.0212765957446809:0.285714285714286:0.0909090909090909:0.333333333333333:0.2:0.333333333333333:0.5:0.5:1:0.111111111111111:0.5:0.903846153846154:0.5:0.2:1:1:0.0909090909090909:0.4:0.428571428571429:0.0625:0.25:0.833333333333333:1:0.956521739130435:0.111111111111111:0.6:0.212765957446809:0.5:0.207547169811321:1:0.75:0.294117647058824:0.666666666666667:1:0.333333333333333:0.714285714285714:0.142857142857143:1:0.3:0.416666666666667:0.272727272727273:0.25:0.333333333333333:0.345454545454545:0.0952380952380952:0.166666666666667:0.111111111111111:0.454545454545455:0.0666666666666667:1:0.636363636363636:0.636363636363636:0.25:0.272727272727273:0.824324324324324:1:0.75:0.545454545454545:1:1:0.0769230769230769:0.363636363636364:0.290322580645161:0.333333333333333:0.179487179487179:1:0.0666666666666667:0.333333333333333:1:0.478260869565217:0.166666666666667:1:1:0.0276497695852535:0.0716845878136201:0.0263736263736264:0.933333333333333:1:0.5:1:1:0.8125:0.361788617886179:0.113761467889908:0.113761467889908:0.157894736842105:0.333333333333333:0.0555555555555556:0.0357142857142857:0.375:0.111111111111111:0.584415584415584:0.0350877192982456:0.751111111111111:0.761245674740484:0.164989939637827:0.196652719665272:0.135549872122762:0.172113289760349:0.0240963855421687:0.0620767494356659:0.142268041237113:0.147441457068517:0.147959183673469:0.038961038961039:0.686274509803922:0.0929054054054054:0.364787111622555:0.331306990881459:0.691449814126394:0.691449814126394:0.0769230769230769:0.347826086956522:0.117647058823529:0.148148148148148:0.05:0.290030211480363:0.680272108843537:0.188679245283019:0.0701754385964912:0.801526717557252:0.236842105263158:0.1953125:0.0539906103286385:0.015625:0.0390492359932088:0.00790513833992095:0.0597826086956522:0.136783733826248:0.362359550561798:0.0713719270420301:0.328621908127208:0.0657672849915683:0.320099255583127:0.075:0.433021806853583:0.524818401937046:0.524818401937046:0.259259259259259:0.483695652173913:0.0269360269360269:0.100486223662885:0.785507246376812:0.137870855148342:0.472340425531915:0.194331983805668:0.0830769230769231:0.418055555555556:0.546296296296296:0.247596153846154:0.52:0.39832285115304:0.601866251944012:0.234016887816647:0.214007782101167:0.153153153153153:0.137180700094607:0.0666666666666667:0.037037037037037:0.1:0.2:0.458333333333333:0.0588235294117647:0.111111111111111:0.333333333333333:0.181818181818182:0.473684210526316:0.5:0.2:0.136363636363636:0.0769230769230769:0.142857142857143:0.285714285714286:0.25:0.445714285714286:0.149377593360996:0.0227790432801822:0.182278481012658:0.540123456790123:0.021505376344086:0.541666666666667:0.00429184549356223:0.473684210526316:0.103508771929825:0.0930232558139535:0.391304347826087:0.072:0.0113636363636364:0.148837209302326:0.448051948051948:0.761038961038961:0.530373831775701:0.222857142857143:0.433862433862434:0.0810810810810811:0.0723327305605787:0.410714285714286:0.247910863509749:0.384615384615385:0.125:0.24:0.783582089552239:0.0646651270207852:0.445569620253165:0.754777070063694:0.165137614678899:0.10732538330494:0.0375:0.538461538461538:0.0981387478849408:0.029126213592233:0.0833333333333333:0.443514644351464:0.0917431192660551:0.03125:0.674418604651163:0.3125:0.375:0.314285714285714     L:46    cAa/cGa:203|Caa/Aaa:140|cAa/cTa:46|caA/caT:14|caA/caC:13|ggACaa/ggCAaa:2|cAa/cCa:2|Caa/Taa:1|CAa/AGa:1  1:115256529_252|1:115256530_143|1:115256528_27        skcm:787:186|thpa:486:43|mm:275:27|thpd:58:18|coadread:683:16|luad:2057:15|coad:712:13|mup:42:7|aml:198:6|blca:852:5|thap:33:5|read:149:5|rms:50:5|uec:339:5|nsgct:152:5|cll:283:4|ihch:104:4|lgsoc:17:3|sem:59:3|thhc:21:3|erms:8:3|lggnos:544:3|utuc:76:2|cup:135:2|thfo:5:2|sarcl:13:2|mfh:53:2|gbm:688:2|soc:468:2|stad:748:2|thym:125:2|es:229:1|npc:66:1|unk:146:1|panet:86:1|hnsc:643:1|armm:21:1|tmt:3:1|acrm:23:1|thyc:9:1|odg:36:1|paasc:8:1|hnmucm:11:1|blad:7:1|esca:556:1|mixed:3:1|chol:152:1|hcc:620:1|sarc:280:1|chrcc:88:1|aca:93:1     skin:974:187|thyroid:618:71|blood:890:37|bowel:1782:35|lung:2761:17|unk:357:11|softtissue:739:11|testis:217:9|bladder:958:8|cnsbrain:2270:6|ovaryfallopiantube:699:5|biliarytract:358:5|uterus:618:5|headandneck:988:3|thymus:162:3|esophagusstomach:1407:3|pancreas:1059:2|bone:297:1|liver:636:1|kidney:1304:1|adrenalgland:291:1    TTG|ACA|CTT|TCG|CCC|CCA 0.0120300464273379      0.0267810594223141    24592   "pancan,skin,thyroid,bowel,blood,lung,softtissue,testis,bladder,cnsbrain,biliarytract,ovaryfallopiantube,uterus,thymus,headandneck,esophagusstomach"    0       NRAS 61       0       FALSE           NA      1       1.16795714944678        1.26187131041539        1.29838371117394        TRUE            165     257     RETAIN  TRUE    TRUE    Q       0    skin     skin:24|bowel:7|lung:6|blood:2|cnsbrain:2|unk:2|bladder:1|softtissue:1|uterus:1
NRAS    61      -1237.69143477067       422     Q:422   620     0.333333333333333       295|0.692307692307692:0.733333333333333:0.2:0.933333333333333:1:0.25:0.666666666666667:1:0.25:0.571428571428571:1:1:0.5:0.363636363636364:0.428571428571429:0.0833333333333333:1:1:1:1:0.5:1:0.125:0.363636363636364:0.173913043478261:0.25:1:0.8:0.153846153846154:0.857142857142857:0.5:0.5:0.5:1:0.272727272727273:0.214285714285714:1:0.5:1:1:0.2:0.333333333333333:0.6875:0.708333333333333:0.25:0.266666666666667:0.111111111111111:1:1:0.333333333333333:0.428571428571429:0.666666666666667:0.25:0.5:0.833333333333333:0.5:0.735294117647059:0.0476190476190476:0.1:0.133333333333333:0.230769230769231:0.25:1:0.5:0.294117647058824:0.217391304347826:0.46875:0.5:1:0.2:0.166666666666667:0.666666666666667:1:0.8:0.407407407407407:1:0.0212765957446809:0.285714285714286:0.0909090909090909:0.333333333333333:0.2:0.333333333333333:0.5:0.5:1:0.111111111111111:0.5:0.903846153846154:0.5:0.2:1:1:0.0909090909090909:0.4:0.428571428571429:0.0625:0.25:0.833333333333333:1:0.956521739130435:0.111111111111111:0.6:0.212765957446809:0.5:0.207547169811321:1:0.75:0.294117647058824:0.666666666666667:1:0.333333333333333:0.714285714285714:0.142857142857143:1:0.3:0.416666666666667:0.272727272727273:0.25:0.333333333333333:0.345454545454545:0.0952380952380952:0.166666666666667:0.111111111111111:0.454545454545455:0.0666666666666667:1:0.636363636363636:0.636363636363636:0.25:0.272727272727273:0.824324324324324:1:0.75:0.545454545454545:1:1:0.0769230769230769:0.363636363636364:0.290322580645161:0.333333333333333:0.179487179487179:1:0.0666666666666667:0.333333333333333:1:0.478260869565217:0.166666666666667:1:1:0.0276497695852535:0.0716845878136201:0.0263736263736264:0.933333333333333:1:0.5:1:1:0.8125:0.361788617886179:0.113761467889908:0.113761467889908:0.157894736842105:0.333333333333333:0.0555555555555556:0.0357142857142857:0.375:0.111111111111111:0.584415584415584:0.0350877192982456:0.751111111111111:0.761245674740484:0.164989939637827:0.196652719665272:0.135549872122762:0.172113289760349:0.0240963855421687:0.0620767494356659:0.142268041237113:0.147441457068517:0.147959183673469:0.038961038961039:0.686274509803922:0.0929054054054054:0.364787111622555:0.331306990881459:0.691449814126394:0.691449814126394:0.0769230769230769:0.347826086956522:0.117647058823529:0.148148148148148:0.05:0.290030211480363:0.680272108843537:0.188679245283019:0.0701754385964912:0.801526717557252:0.236842105263158:0.1953125:0.0539906103286385:0.015625:0.0390492359932088:0.00790513833992095:0.0597826086956522:0.136783733826248:0.362359550561798:0.0713719270420301:0.328621908127208:0.0657672849915683:0.320099255583127:0.075:0.433021806853583:0.524818401937046:0.524818401937046:0.259259259259259:0.483695652173913:0.0269360269360269:0.100486223662885:0.785507246376812:0.137870855148342:0.472340425531915:0.194331983805668:0.0830769230769231:0.418055555555556:0.546296296296296:0.247596153846154:0.52:0.39832285115304:0.601866251944012:0.234016887816647:0.214007782101167:0.153153153153153:0.137180700094607:0.0666666666666667:0.037037037037037:0.1:0.2:0.458333333333333:0.0588235294117647:0.111111111111111:0.333333333333333:0.181818181818182:0.473684210526316:0.5:0.2:0.136363636363636:0.0769230769230769:0.142857142857143:0.285714285714286:0.25:0.445714285714286:0.149377593360996:0.0227790432801822:0.182278481012658:0.540123456790123:0.021505376344086:0.541666666666667:0.00429184549356223:0.473684210526316:0.103508771929825:0.0930232558139535:0.391304347826087:0.072:0.0113636363636364:0.148837209302326:0.448051948051948:0.761038961038961:0.530373831775701:0.222857142857143:0.433862433862434:0.0810810810810811:0.0723327305605787:0.410714285714286:0.247910863509749:0.384615384615385:0.125:0.24:0.783582089552239:0.0646651270207852:0.445569620253165:0.754777070063694:0.165137614678899:0.10732538330494:0.0375:0.538461538461538:0.0981387478849408:0.029126213592233:0.0833333333333333:0.443514644351464:0.0917431192660551:0.03125:0.674418604651163:0.3125:0.375:0.314285714285714     H:27    cAa/cGa:203|Caa/Aaa:140|cAa/cTa:46|caA/caT:14|caA/caC:13|ggACaa/ggCAaa:2|cAa/cCa:2|Caa/Taa:1|CAa/AGa:1  1:115256529_252|1:115256530_143|1:115256528_27        skcm:787:186|thpa:486:43|mm:275:27|thpd:58:18|coadread:683:16|luad:2057:15|coad:712:13|mup:42:7|aml:198:6|blca:852:5|thap:33:5|read:149:5|rms:50:5|uec:339:5|nsgct:152:5|cll:283:4|ihch:104:4|lgsoc:17:3|sem:59:3|thhc:21:3|erms:8:3|lggnos:544:3|utuc:76:2|cup:135:2|thfo:5:2|sarcl:13:2|mfh:53:2|gbm:688:2|soc:468:2|stad:748:2|thym:125:2|es:229:1|npc:66:1|unk:146:1|panet:86:1|hnsc:643:1|armm:21:1|tmt:3:1|acrm:23:1|thyc:9:1|odg:36:1|paasc:8:1|hnmucm:11:1|blad:7:1|esca:556:1|mixed:3:1|chol:152:1|hcc:620:1|sarc:280:1|chrcc:88:1|aca:93:1     skin:974:187|thyroid:618:71|blood:890:37|bowel:1782:35|lung:2761:17|unk:357:11|softtissue:739:11|testis:217:9|bladder:958:8|cnsbrain:2270:6|ovaryfallopiantube:699:5|biliarytract:358:5|uterus:618:5|headandneck:988:3|thymus:162:3|esophagusstomach:1407:3|pancreas:1059:2|bone:297:1|liver:636:1|kidney:1304:1|adrenalgland:291:1    TTG|ACA|CTT|TCG|CCC|CCA 0.0120300464273379      0.0267810594223141    24592   "pancan,skin,thyroid,bowel,blood,lung,softtissue,testis,bladder,cnsbrain,biliarytract,ovaryfallopiantube,uterus,thymus,headandneck,esophagusstomach"    0       NRAS 61       0       FALSE           NA      1       1.16795714944678        1.26187131041539        1.29838371117394        TRUE            165     257     RETAIN  TRUE    TRUE    Q       0    skin     skin:12|blood:7|bowel:2|lung:2|testis:2|softtissue:1|unk:1

Indel

Hugo_Symbol     Amino_Acid_Position     log10_pvalue    Mutation_Count  Reference_Amino_Acid    Total_Mutations_in_Gene Median_Allele_Freq_Rank Allele_Freq_Rank        SNP_ID  Variant_Amino_Acid    Codon_Change    Genomic_Position        Detailed_Cancer_Types   Organ_Types     Tri-nucleotides Mutability      mu_protein      ccf     Total_Samples   indel_size      qvalue  tm   Is_repeat        seq     length  align100        pad12entropy    pad24entropy    pad36entropy    TP      reason  n_MSK   n_Retro judgement       inNBT   inOncokb        Samples
SMARCA4 546     -7.75235638169585       5       QK:5    101     NA      NA      :NA     K546del:5       cAGAag/cag:5    19:11106926_5   lgg:536:4|dlbcl:246:1   cnsbrain:2283:4|lymph:366:1  NA       0.0573226243518208      0.0473351872460284      NA      24592   1       0.000230672905611517    SMARCA4 546     FALSE   NA      NA      1       0.91489630957268        1.2950060272429       1.33965330506364        FALSE   LOCAL_ENTROPY   1       4       RETAIN  FALSE   FALSE   cnsbrain:4|lymph:1
CDKN2A  27-42   -6.82111516846557       12      VRALLEA:4|LEAGALP:3|ALPN:1|EV:1|GA:1|PNAPN:1|RALLEA:1   219     NA      NA      :NA     V28_E33del:4    gTGCGGGCGCTGCTGGAGGcg/gcg:4|cTGGAGGCGGGGGCGCTGCcc/ccc:3|GGGGCG/-:1|gCGCTGCCCAac/gac:1|gAGGtg/gtg:1|CGGGCGCTGCTGGAGGCG/-:1|ccCAACGCACCGAAt/cct:1       9:21974727_4|9:21974715_3|9:21974745_1|9:21974725_1|9:21974719_1|9:21974712_1|9:21974702_1    luad:2071:3|esca:556:2|blca:852:1|skcm:192:1|icemu:1:1|paad:932:1|mel:595:1|stad:748:1|hnsc:650:1       esophagusstomach:1413:3|lung:2767:3|skin:974:2|bladder:955:1|cervix:234:1|pancreas:1059:1|headandneck:988:1   NA      0.0573226243518208      0.0473351872460284      NA      24592   15      8.77193090544841e-05    CDKN2A 27-42    FALSE   NA      NA      1    0.857780912379927        1.13008762297022        1.1577633500238 FALSE   LOCAL_ENTROPY   6       6       RETAIN  FALSE   FALSE   cervix:1|esophagusstomach:1|lung:1|pancreas:1
CDKN2A  27-42   -6.82111516846557       12      VRALLEA:4|LEAGALP:3|ALPN:1|EV:1|GA:1|PNAPN:1|RALLEA:1   219     NA      NA      :NA     L32_L37del:3    gTGCGGGCGCTGCTGGAGGcg/gcg:4|cTGGAGGCGGGGGCGCTGCcc/ccc:3|GGGGCG/-:1|gCGCTGCCCAac/gac:1|gAGGtg/gtg:1|CGGGCGCTGCTGGAGGCG/-:1|ccCAACGCACCGAAt/cct:1       9:21974727_4|9:21974715_3|9:21974745_1|9:21974725_1|9:21974719_1|9:21974712_1|9:21974702_1    luad:2071:3|esca:556:2|blca:852:1|skcm:192:1|icemu:1:1|paad:932:1|mel:595:1|stad:748:1|hnsc:650:1       esophagusstomach:1413:3|lung:2767:3|skin:974:2|bladder:955:1|cervix:234:1|pancreas:1059:1|headandneck:988:1   NA      0.0573226243518208      0.0473351872460284      NA      24592   15      8.77193090544841e-05    CDKN2A 27-42    FALSE   NA      NA      1    0.857780912379927        1.13008762297022        1.1577633500238 FALSE   LOCAL_ENTROPY   6       6       RETAIN  FALSE   FALSE   skin:2|esophagusstomach:1
CDKN2A  27-42   -6.82111516846557       12      VRALLEA:4|LEAGALP:3|ALPN:1|EV:1|GA:1|PNAPN:1|RALLEA:1   219     NA      NA      :NA     A36_N39delinsD:1        gTGCGGGCGCTGCTGGAGGcg/gcg:4|cTGGAGGCGGGGGCGCTGCcc/ccc:3|GGGGCG/-:1|gCGCTGCCCAac/gac:1|gAGGtg/gtg:1|CGGGCGCTGCTGGAGGCG/-:1|ccCAACGCACCGAAt/cct:1       9:21974727_4|9:21974715_3|9:21974745_1|9:21974725_1|9:21974719_1|9:21974712_1|9:21974702_1    luad:2071:3|esca:556:2|blca:852:1|skcm:192:1|icemu:1:1|paad:932:1|mel:595:1|stad:748:1|hnsc:650:1       esophagusstomach:1413:3|lung:2767:3|skin:974:2|bladder:955:1|cervix:234:1|pancreas:1059:1|headandneck:988:1   NA      0.0573226243518208      0.0473351872460284      NA      24592   15      8.77193090544841e-05    CDKN2A 27-42    FALSE   NA      NA   0.857780912379927        1.13008762297022        1.1577633500238 FALSE   LOCAL_ENTROPY   6       6       RETAIN  FALSE   FALSE   lung:1

Parsing

From the file, we're mainly interested in the following columns:

  • Hugo_Symbol
  • Amino_Acid_Position
  • Mutation_Count
  • Reference_Amino_Acid
  • Variant_Amino_Acid
  • qvalue

We map the gene symbol onto the canonical transcripts (RefSeq & Ensembl) for that gene. For SNVs, we obtain position, ref and alt amino acid from source file and generate substitution notation. For indels, we get protein change notation from Reference_Amino_Acid column. -Then we match each entry using these notations.

caution

We currently skip all variants labeled as splice from the source

JSON Output

The data source will be captured under the cancerHotspots key in the transcript section.

{
   "transcript":"NM_002524.5",
   "source":"RefSeq",
   "bioType":"mRNA",
   "aminoAcids":"Q/K",
   "proteinPos":"61",
   "geneId":"4893",
   "hgnc":"NRAS",
   "hgvsc":"NM_002524.5:c.181C>A",
   "hgvsp":"NP_002515.1:p.(Gln61Lys)",
   "isCanonical":true,
   "proteinId":"NP_002515.1",
   "cancerHotspots":{
      "residue":"Q61",
      "numSamples":422,
      "numAltAminoAcidSamples":142,
      "qValue":0
   }
}
FieldTypeNotes
residuestring
numSamplesinthow many samples are associated with a variant at the same amino acid position
numAltAminoAcidSamplesinthow many samples are associated with a variant with the same position and alternate amino acid position
qValuedouble
- - +Then we match each entry using these notations.

caution

We currently skip all variants labeled as splice from the source

JSON Output

The data source will be captured under the cancerHotspots key in the transcript section.

{
   "transcript":"NM_002524.5",
   "source":"RefSeq",
   "bioType":"mRNA",
   "aminoAcids":"Q/K",
   "proteinPos":"61",
   "geneId":"4893",
   "hgnc":"NRAS",
   "hgvsc":"NM_002524.5:c.181C>A",
   "hgvsp":"NP_002515.1:p.(Gln61Lys)",
   "isCanonical":true,
   "proteinId":"NP_002515.1",
   "cancerHotspots":{
      "residue":"Q61",
      "numSamples":422,
      "numAltAminoAcidSamples":142,
      "qValue":0
   }
}
FieldTypeNotes
residuestring
numSamplesinthow many samples are associated with a variant at the same amino acid position
numAltAminoAcidSamplesinthow many samples are associated with a variant with the same position and alternate amino acid position
qValuedouble
+ + \ No newline at end of file diff --git a/3.22/data-sources/clingen-dosage-json/index.html b/3.22/data-sources/clingen-dosage-json/index.html index 6d89334c..fc1a34fe 100644 --- a/3.22/data-sources/clingen-dosage-json/index.html +++ b/3.22/data-sources/clingen-dosage-json/index.html @@ -6,13 +6,13 @@ clingen-dosage-json | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

clingen-dosage-json

"clingenDosageSensitivityMap": [{
    "chromosome": "15",
    "begin": 30900686,
    "end": 32153204,
    "haploinsufficiency": "sufficient evidence suggesting dosage sensitivity is associated with clinical phenotype",
    "triplosensitivity": "little evidence suggesting dosage sensitivity is associated with clinical phenotype",
    "reciprocalOverlap": 0.00147,
    "annotationOverlap": 0.33994
},
{
    "chromosome": "15",
    "begin": 31727418,
    "end": 32153204,
    "haploinsufficiency": "sufficient evidence suggesting dosage sensitivity is associated with clinical phenotype",
    "triplosensitivity": "dosage sensitivity unlikely",
    "reciprocalOverlap": 0.00147,
    "annotationOverlap": 1
}]
FieldTypeNotes
clingenDosageSensitivityMapobject array
chromosomestringEnsembl-style chromosome names
begininteger1-based position
endinteger1-based position
haploinsufficiencystringsee possible values below
triplosensitivitystring(same as haploinsufficiency) 
reciprocalOverlapfloating pointRange: 0 - 1. E.g. 0.57 would indicate a 57% reciprocal overlap. Specified up to 5 decimal places (Not reported for Insertions).
annotationOverlapfloating pointRange: 0 - 1. E.g. 0.57 would indicate a 57% reciprocal overlap. Specified up to 5 decimal places (Not reported for Insertions).

haploinsufficiency and triplosensitivity

  • no evidence to suggest that dosage sensitivity is associated with clinical phenotype
  • little evidence suggesting dosage sensitivity is associated with clinical phenotype
  • emerging evidence suggesting dosage sensitivity is associated with clinical phenotype
  • sufficient evidence suggesting dosage sensitivity is associated with clinical phenotype
  • gene associated with autosomal recessive phenotype
  • dosage sensitivity unlikely
- - +
Skip to main content
Version: 3.22

clingen-dosage-json

"clingenDosageSensitivityMap": [{
    "chromosome": "15",
    "begin": 30900686,
    "end": 32153204,
    "haploinsufficiency": "sufficient evidence suggesting dosage sensitivity is associated with clinical phenotype",
    "triplosensitivity": "little evidence suggesting dosage sensitivity is associated with clinical phenotype",
    "reciprocalOverlap": 0.00147,
    "annotationOverlap": 0.33994
},
{
    "chromosome": "15",
    "begin": 31727418,
    "end": 32153204,
    "haploinsufficiency": "sufficient evidence suggesting dosage sensitivity is associated with clinical phenotype",
    "triplosensitivity": "dosage sensitivity unlikely",
    "reciprocalOverlap": 0.00147,
    "annotationOverlap": 1
}]
FieldTypeNotes
clingenDosageSensitivityMapobject array
chromosomestringEnsembl-style chromosome names
begininteger1-based position
endinteger1-based position
haploinsufficiencystringsee possible values below
triplosensitivitystring(same as haploinsufficiency) 
reciprocalOverlapfloating pointRange: 0 - 1. E.g. 0.57 would indicate a 57% reciprocal overlap. Specified up to 5 decimal places (Not reported for Insertions).
annotationOverlapfloating pointRange: 0 - 1. E.g. 0.57 would indicate a 57% reciprocal overlap. Specified up to 5 decimal places (Not reported for Insertions).

haploinsufficiency and triplosensitivity

  • no evidence to suggest that dosage sensitivity is associated with clinical phenotype
  • little evidence suggesting dosage sensitivity is associated with clinical phenotype
  • emerging evidence suggesting dosage sensitivity is associated with clinical phenotype
  • sufficient evidence suggesting dosage sensitivity is associated with clinical phenotype
  • gene associated with autosomal recessive phenotype
  • dosage sensitivity unlikely
+ + \ No newline at end of file diff --git a/3.22/data-sources/clingen-gene-validity-json/index.html b/3.22/data-sources/clingen-gene-validity-json/index.html index d4e11db2..403f1ced 100644 --- a/3.22/data-sources/clingen-gene-validity-json/index.html +++ b/3.22/data-sources/clingen-gene-validity-json/index.html @@ -6,13 +6,13 @@ clingen-gene-validity-json | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

clingen-gene-validity-json

"clingenGeneValidity":[
   {
      "diseaseId":"MONDO_0007893",
      "disease":"Noonan syndrome with multiple lentigines",
      "classification":"no reported evidence",
      "classificationDate":"2018-06-07"
   },
   {
      "diseaseId":"MONDO_0015280",
      "disease":"cardiofaciocutaneous syndrome",
      "classification":"no reported evidence",
      "classificationDate":"2018-06-07"
   }
]
FieldTypeNotes
clingenGeneValidityobject
diseaseIdstringMonarch Disease Ontology ID (MONDO)
diseasestringdisease label
classificationstringsee below for possible values
classificationDatestringyyyy-MM-dd

classification

  • no reported evidence
  • disputed
  • limited
  • moderate
  • definitive
  • strong
  • refuted
  • no known disease relationship
- - +
Skip to main content
Version: 3.22

clingen-gene-validity-json

"clingenGeneValidity":[
   {
      "diseaseId":"MONDO_0007893",
      "disease":"Noonan syndrome with multiple lentigines",
      "classification":"no reported evidence",
      "classificationDate":"2018-06-07"
   },
   {
      "diseaseId":"MONDO_0015280",
      "disease":"cardiofaciocutaneous syndrome",
      "classification":"no reported evidence",
      "classificationDate":"2018-06-07"
   }
]
FieldTypeNotes
clingenGeneValidityobject
diseaseIdstringMonarch Disease Ontology ID (MONDO)
diseasestringdisease label
classificationstringsee below for possible values
classificationDatestringyyyy-MM-dd

classification

  • no reported evidence
  • disputed
  • limited
  • moderate
  • definitive
  • strong
  • refuted
  • no known disease relationship
+ + \ No newline at end of file diff --git a/3.22/data-sources/clingen-json/index.html b/3.22/data-sources/clingen-json/index.html index ab1f02e4..0382caba 100644 --- a/3.22/data-sources/clingen-json/index.html +++ b/3.22/data-sources/clingen-json/index.html @@ -6,13 +6,13 @@ clingen-json | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

clingen-json

"clingen":[
   {
      "chromosome":"17",
      "begin":525,
      "end":14667519,
      "variantType":"copy_number_gain",
      "id":"nsv996083",
      "clinicalInterpretation":"pathogenic",
      "observedGains":1,
      "validated":true,
      "phenotypes":[
         "Intrauterine growth retardation"
      ],
      "phenotypeIds":[
         "HP:0001511",
         "MedGen:C1853481"
      ],
      "reciprocalOverlap":0.00131
   },
   {
      "chromosome":"17",
      "begin":45835,
      "end":7600330,
      "variantType":"copy_number_loss",
      "id":"nsv869419",
      "clinicalInterpretation":"pathogenic",
      "observedLosses":1,
      "validated":true,
      "phenotypes":[
         "Developmental delay AND/OR other significant developmental or morphological phenotypes"
      ],
      "reciprocalOverlap":0.00254
   }
]
FieldTypeNotes
clingenobject array
chromosomestringEnsembl-style chromosome names
begininteger1-based position
endinteger1-based position
variantTypestringAny of the sequence alterations defined here.
idstringIdentifier from the data source. Alternatively a VID
clinicalInterpretationstringsee possible values below
observedGainsintegerRange: 0 - (231 - 1). Only used if copy_number_variation, copy_number_loss, or copy_number_gain.
observedLossesintegerRange: 0 - (231 - 1). Only used if copy_number_variation, copy_number_loss, or copy_number_gain.
validatedboolean
phenotypesstring arrayDescription of the phenotype.
phenotypeIdsstring arrayDescription of the phenotype IDs.
reciprocalOverlapfloating pointRange: 0 - 1. E.g. 0.57 would indicate a 57% reciprocal overlap. Specified up to 5 decimal places (Not reported for Insertions).

clinicalInterpretation

  • benign
  • curated benign
  • curated pathogenic
  • likely benign
  • likely pathogenic
  • path gain
  • path loss
  • pathogenic
  • uncertain
- - +
Skip to main content
Version: 3.22

clingen-json

"clingen":[
   {
      "chromosome":"17",
      "begin":525,
      "end":14667519,
      "variantType":"copy_number_gain",
      "id":"nsv996083",
      "clinicalInterpretation":"pathogenic",
      "observedGains":1,
      "validated":true,
      "phenotypes":[
         "Intrauterine growth retardation"
      ],
      "phenotypeIds":[
         "HP:0001511",
         "MedGen:C1853481"
      ],
      "reciprocalOverlap":0.00131
   },
   {
      "chromosome":"17",
      "begin":45835,
      "end":7600330,
      "variantType":"copy_number_loss",
      "id":"nsv869419",
      "clinicalInterpretation":"pathogenic",
      "observedLosses":1,
      "validated":true,
      "phenotypes":[
         "Developmental delay AND/OR other significant developmental or morphological phenotypes"
      ],
      "reciprocalOverlap":0.00254
   }
]
FieldTypeNotes
clingenobject array
chromosomestringEnsembl-style chromosome names
begininteger1-based position
endinteger1-based position
variantTypestringAny of the sequence alterations defined here.
idstringIdentifier from the data source. Alternatively a VID
clinicalInterpretationstringsee possible values below
observedGainsintegerRange: 0 - (231 - 1). Only used if copy_number_variation, copy_number_loss, or copy_number_gain.
observedLossesintegerRange: 0 - (231 - 1). Only used if copy_number_variation, copy_number_loss, or copy_number_gain.
validatedboolean
phenotypesstring arrayDescription of the phenotype.
phenotypeIdsstring arrayDescription of the phenotype IDs.
reciprocalOverlapfloating pointRange: 0 - 1. E.g. 0.57 would indicate a 57% reciprocal overlap. Specified up to 5 decimal places (Not reported for Insertions).

clinicalInterpretation

  • benign
  • curated benign
  • curated pathogenic
  • likely benign
  • likely pathogenic
  • path gain
  • path loss
  • pathogenic
  • uncertain
+ + \ No newline at end of file diff --git a/3.22/data-sources/clingen/index.html b/3.22/data-sources/clingen/index.html index 9a9fc0b0..b8af222f 100644 --- a/3.22/data-sources/clingen/index.html +++ b/3.22/data-sources/clingen/index.html @@ -6,13 +6,13 @@ ClinGen | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

ClinGen

Overview

ClinGen is a National Institutes of Health (NIH)-funded resource dedicated to building a central resource that defines the clinical relevance of genes and variants for use in precision medicine and research.

Publication

Heidi L. Rehm, Ph.D., Jonathan S. Berg, M.D., Ph.D., Lisa D. Brooks, Ph.D., Carlos D. Bustamante, Ph.D., James P. Evans, M.D., Ph.D., Melissa J. Landrum, Ph.D., David H. Ledbetter, Ph.D., Donna R. Maglott, Ph.D., Christa Lese Martin, Ph.D., Robert L. Nussbaum, M.D., Sharon E. Plon, M.D., Ph.D., Erin M. Ramos, Ph.D., Stephen T. Sherry, Ph.D., and Michael S. Watson, Ph.D., for ClinGen. ClinGen The Clinical Genome Resource. N Engl J Med 2015; 372:2235-2242 June 4, 2015 DOI: 10.1056/NEJMsr1406261.

ISCA Regions

TSV Extraction

ClinGen contains only copy number variation variants, since the coordinates in ClinGen original file follow the same rule as BED format, the coordinates had to be adjusted to [BEGIN+1, END].

#bin    chrom   chromStart      chromEnd        name    score   strand  thickStart      thickEnd        attrCount       attrTags        attrVals
nsv530705       1       564405  8597804 0       1       copy_number_loss        pathogenic      False   Developmental delay AND/OR other significant developmental or morphological phenotypes
nsv530706       1       564424  3262790 0       1       copy_number_loss        pathogenic      False   Abnormal facial shape,Abnormality of cardiac morphology,Global developmental delay,Muscular hypotonia   HP:0001252,HP:0001263,HP:0001627,HP:0001999,MedGen:CN001147,MedGen:CN001157,MedGen:CN001482,MedGen:CN001810
nsv530707       1       564424  7068738 0       1       copy_number_loss        pathogenic      False   Abnormality of cardiac morphology,Cleft upper lip,Failure to thrive,Global developmental delay,Intrauterine growth retardation,Microcephaly,Short stature       HP:0000204,HP:0000252,HP:0001263,HP:0001508,HP:0001511,HP:0001627,HP:0004322,MedGen:C0349588,MedGen:C1845868,MedGen:C1853481,MedGen:C2364119,MedGen:CN000197,MedGen:CN001157,MedGen:CN001482
nsv533512       1       564435  649748  0       1       copy_number_loss        benign  False   Developmental delay AND/OR other significant developmental or morphological phenotypes
nsv931338       1       714078  4958499 0       1       copy_number_loss        pathogenic      False   Developmental delay AND/OR other significant developmental or morphological phenotypes
nsv530300       1       728138  5066371 1       0       copy_number_gain        pathogenic      False   Abnormality of cardiac morphology,Cleft palate,Global developmental delay       HP:0000175,HP:0001263,HP:0001627,MedGen:C2240378,MedGen:CN001157,MedGen:CN001482

Status levels

  • benign
  • curated benign
  • curated pathogenic
  • likely benign
  • likely pathogenic
  • path gain
  • path loss
  • pathogenic
  • uncertain

Parsing

We parse the ClinGen tsv file and extract the following:

  • chrom
  • chromStart (note this a 0-based coordinate)
  • chromEnd
  • attrTags
  • attrVals

attrTags and attrVals are comma separated lists. attrTags contains the field keys and attrVals contains the field values. We will parse the following keys from the two fields:

  • parent (this will be used as the ID in our JSON output)
  • clinical_int
  • validated
  • phenotype (this should be a string array)
  • phenotype_id (this should be a string array)

Observed losses and observed gains will be calculated from entries that share a common parent ID.

  • variants with a common parent ID and same coordinates are grouped
    • calculated observed losses, observed gains for each group
    • Clinical significance and validation status are collapsed using the priority strategy described below
  • Variants with the same parent ID can have different coordinates (mapped to hg38)
    • nsv491508 : chr14:105583663-106881350 and chr14:105605043-106766076 (only one example)
    • we kept both variants

Conflict Resolution

Clinical significance priority

When there are a mixture of variants belonging to the same parent ID, we will choose the most pathogenic clinical significance from the available values. i.e. if 3 samples were deemed pathogenic and 2 samples were likely pathogenic, we would list the variant as pathogenic.

Priority (high to low)

  • Priority
  • Pathogenic
  • Likely pathogenic
  • Benign
  • Likely benign
  • Uncertain significance

Validation Priority

When there are a mixture of variants belonging to same parent ID, we will set the validation status to true if any of the variants were validated.

Download URL

https://cirm.ucsc.edu/cgi-bin/hgTrackUi?db=hg19&g=iscaComposite

JSON Output

"clingen":[
   {
      "chromosome":"17",
      "begin":525,
      "end":14667519,
      "variantType":"copy_number_gain",
      "id":"nsv996083",
      "clinicalInterpretation":"pathogenic",
      "observedGains":1,
      "validated":true,
      "phenotypes":[
         "Intrauterine growth retardation"
      ],
      "phenotypeIds":[
         "HP:0001511",
         "MedGen:C1853481"
      ],
      "reciprocalOverlap":0.00131
   },
   {
      "chromosome":"17",
      "begin":45835,
      "end":7600330,
      "variantType":"copy_number_loss",
      "id":"nsv869419",
      "clinicalInterpretation":"pathogenic",
      "observedLosses":1,
      "validated":true,
      "phenotypes":[
         "Developmental delay AND/OR other significant developmental or morphological phenotypes"
      ],
      "reciprocalOverlap":0.00254
   }
]
FieldTypeNotes
clingenobject array
chromosomestringEnsembl-style chromosome names
begininteger1-based position
endinteger1-based position
variantTypestringAny of the sequence alterations defined here.
idstringIdentifier from the data source. Alternatively a VID
clinicalInterpretationstringsee possible values below
observedGainsintegerRange: 0 - (231 - 1). Only used if copy_number_variation, copy_number_loss, or copy_number_gain.
observedLossesintegerRange: 0 - (231 - 1). Only used if copy_number_variation, copy_number_loss, or copy_number_gain.
validatedboolean
phenotypesstring arrayDescription of the phenotype.
phenotypeIdsstring arrayDescription of the phenotype IDs.
reciprocalOverlapfloating pointRange: 0 - 1. E.g. 0.57 would indicate a 57% reciprocal overlap. Specified up to 5 decimal places (Not reported for Insertions).

clinicalInterpretation

  • benign
  • curated benign
  • curated pathogenic
  • likely benign
  • likely pathogenic
  • path gain
  • path loss
  • pathogenic
  • uncertain

Dosage Sensitivity Map

The Clinical Genome Resource (ClinGen) consortium is curating genes and regions of the genome to assess whether there is evidence to support that these genes/regions are dosage sensitive and should be targeted on a cytogenomic array. Illumina Connected Annotations reports these annotations for overlapping SVs.

Publication

Riggs ER, Nelson T, Merz A, Ackley T, Bunke B, Collins CD, Collinson MN, Fan YS, Goodenberger ML, Golden DM, Haglund-Hazy L, Krgovic D, Lamb AN, Lewis Z, Li G, Liu Y, Meck J, Neufeld-Kaiser W, Runke CK, Sanmann JN, Stavropoulos DJ, Strong E, Su M, Tayeh MK, Kokalj Vokac N, Thorland EC, Andersen E, Martin CL. Copy number variant discrepancy resolution using the ClinGen dosage sensitivity map results in updated clinical interpretations in ClinVar. Hum Mutat. 2018 Nov;39(11):1650-1659. doi: 10.1002/humu.23610. PMID: 30095202; PMCID: PMC7374944.

TSV Source files

Regions

#ClinGen Region Curation Results
#07 May,2019
#Genomic Locations are reported on GRCh38 (hg38): GCF_000001405.36
#https://www.ncbi.nlm.nih.gov/projects/dbvar/clingen
#to create link: https://www.ncbi.nlm.nih.gov/projects/dbvar/clingen/clingen_region.cgi?id=key
#ISCA ID    ISCA Region Name    cytoBand    Genomic Location    Haploinsufficiency Score    Haploinsufficiency Description  Haploinsufficiency PMID1    Haploinsufficiency PMID2    Haploinsufficiency PMID3    Triplosensitivity Score Triplosensitivity Description   Triplosensitivity PMID1 Triplosensitivity PMID2 Triplosensitivity PMID3 Date Last Evaluated Loss phenotype OMIM ID  Triplosensitive phenotype OMIM ID
ISCA-46299  Xp11.22 region (includes HUWE1) Xp11.22 tbd 0   No evidence available               3   Sufficient evidence for dosage pathogenicity    22840365    20655035    26692240    2018-11-19
ISCA-46295  15q13.3 recurrent region (D-CHRNA7 to BP5) (includes CHRNA7 and OTUD7A) 15q13.3 chr15:31727418-32153204 3   Sufficient evidence for dosage pathogenicity    19898479    20236110    22775350    40  Dosage sensitivity unlikely 26968334    22420048        2018-05-10
ISCA-46291  7q11.23 recurrent distal region (includes HIP1, YWHAG)  7q11.23 chr7:75528718-76433859  2   Some evidence for dosage pathogenicity  21109226    16971481        1   Little evidence for dosage pathogenicity    21109226    27867344        2018-12-31
ISCA-46290  Xp11.22p11.23 recurrent region (includes SHROOM4)   Xp11.22-p11.23  chrX: 48447780-52444264 0   No evidence available               3   Sufficient evidence for dosage pathogenicity    19716111    21418194    25425167    2017-12-14      300801

Genes

#ClinGen Gene Curation Results
#24 May,2019
#Genomic Locations are reported on GRCh37 (hg19): GCF_000001405.13
#https://www.ncbi.nlm.nih.gov/projects/dbvar/clingen
#to create link: https://www.ncbi.nlm.nih.gov/projects/dbvar/clingen/clingen_gene.cgi?sym=Gene Symbol
#Gene Symbol    Gene ID cytoBand    Genomic Location    Haploinsufficiency Score    Haploinsufficiency Description  Haploinsufficiency PMID1    Haploinsufficiency PMID2    Haploinsufficiency PMID3    Triplosensitivity Score Triplosensitivity Description   Triplosensitivity PMID1 Triplosensitivity PMID2 Triplosensitivity PMID3 Date Last Evaluated Loss phenotype OMIM ID  Triplosensitive phenotype OMIM ID
A4GALT  53947   22q13.2 chr22:43088121-43117307 30  Gene associated with autosomal recessive phenotype              0   No evidence available               2014-12-11  111400
AAGAB   79719   15q23   chr15:67493013-67547536 3   Sufficient evidence for dosage pathogenicity    23064416    23000146        0   No evidence available               2013-02-28  148600

Dosage Rating System

RatingPossible Clinical Interpretation
0No evidence to suggest that dosage sensitivity is associated with clinical phenotype
1Little evidence suggesting dosage sensitivity is associated with clinical phenotype
2Emerging evidence suggesting dosage sensitivity is associated with clinical phenotype
3Sufficient evidence suggesting dosage sensitivity is associated with clinical phenotype
30Gene associated with autosomal recessive phenotype
40Dosage sensitivity unlikely

Reference: https://www.ncbi.nlm.nih.gov/projects/dbvar/clingen/help.shtml

Download URL

ftp://ftp.clinicalgenome.org/

JSON Output

"clingenDosageSensitivityMap": [{
    "chromosome": "15",
    "begin": 30900686,
    "end": 32153204,
    "haploinsufficiency": "sufficient evidence suggesting dosage sensitivity is associated with clinical phenotype",
    "triplosensitivity": "little evidence suggesting dosage sensitivity is associated with clinical phenotype",
    "reciprocalOverlap": 0.00147,
    "annotationOverlap": 0.33994
},
{
    "chromosome": "15",
    "begin": 31727418,
    "end": 32153204,
    "haploinsufficiency": "sufficient evidence suggesting dosage sensitivity is associated with clinical phenotype",
    "triplosensitivity": "dosage sensitivity unlikely",
    "reciprocalOverlap": 0.00147,
    "annotationOverlap": 1
}]
FieldTypeNotes
clingenDosageSensitivityMapobject array
chromosomestringEnsembl-style chromosome names
begininteger1-based position
endinteger1-based position
haploinsufficiencystringsee possible values below
triplosensitivitystring(same as haploinsufficiency) 
reciprocalOverlapfloating pointRange: 0 - 1. E.g. 0.57 would indicate a 57% reciprocal overlap. Specified up to 5 decimal places (Not reported for Insertions).
annotationOverlapfloating pointRange: 0 - 1. E.g. 0.57 would indicate a 57% reciprocal overlap. Specified up to 5 decimal places (Not reported for Insertions).

haploinsufficiency and triplosensitivity

  • no evidence to suggest that dosage sensitivity is associated with clinical phenotype
  • little evidence suggesting dosage sensitivity is associated with clinical phenotype
  • emerging evidence suggesting dosage sensitivity is associated with clinical phenotype
  • sufficient evidence suggesting dosage sensitivity is associated with clinical phenotype
  • gene associated with autosomal recessive phenotype
  • dosage sensitivity unlikely

Building the supplementary files

The gene dosage sensitivity .nga for Illumina Connected Annotations can be built using the SAUtils command's DosageSensitivity subcommand. The required data file is ClinGen_gene_curation_list_{ASSEMBLY}.tsv (url provided above) and its associated .version file.

NAME=ClinGen Dosage Sensitivity Map
VERSION=20211201
DATE=2021-12-01
DESCRIPTION=Dosage sensitivity map from ClinGen (dbVar)

Here is a sample run:

dotnet SAUtils.dll DosageSensitivity
---------------------------------------------------------------------------
SAUtils                                             (c) 2023 Illumina, Inc.
Stromberg, Roy, Platzer, Siddiqui, Ouyang, et al         3.21.0-0-gd2a0e953
---------------------------------------------------------------------------

USAGE: dotnet SAUtils.dll dosagesensitivity [options]
Creates a gene annotation database from dbVar data

OPTIONS:
      --tsv, -t <VALUE>      input tsv file
      --out, -o <VALUE>      output directory
      --help, -h             displays the help menu
      --version, -v          displays the version

dotnet SAUtils.dll DosageSensitivity --out SupplementaryDatabase/64/GRCh37 --tsv ClinGen_gene_curation_list_GRCh37.tsv
---------------------------------------------------------------------------
SAUtils                                             (c) 2023 Illumina, Inc.
Stromberg, Roy, Platzer, Siddiqui, Ouyang, et al         3.21.0-0-gd2a0e953
---------------------------------------------------------------------------


Time: 00:00:00.1

For building the .nsi files, we use the SAUtils command's DosageMapRegions subcommand. The required data file is ClinGen_region_curation_list_{ASSEMBLY}.tsv (url provided above) and its associated .version file.

NAME=ClinGen Dosage Sensitivity Map
VERSION=20211201
DATE=2021-12-01
DESCRIPTION=Dosage sensitivity map from ClinGen (dbVar)

Here is a sample run:

dotnet SAUtils.dll DosageMapRegions
---------------------------------------------------------------------------
SAUtils                                             (c) 2023 Illumina, Inc.
Stromberg, Roy, Platzer, Siddiqui, Ouyang, et al         3.21.0-0-gd2a0e953
---------------------------------------------------------------------------

USAGE: dotnet SAUtils.dll dosagemapregions [options]
Creates an interval annotation database from dbVar data

OPTIONS:
      --tsv, -t <VALUE>      input tsv file
      --ref, -r <filename>   input reference filename
      --out, -o <VALUE>      output directory
      --help, -h             displays the help menu
      --version, -v          displays the version

dotnet SAUtils.dll DosageMapRegions --out SupplementaryDatabase/64/GRCh37 --ref References/7/Homo_sapiens.GRCh37.Nirvana.dat --tsv ClinGen_region_curation_list_GRCh37.tsv
---------------------------------------------------------------------------
SAUtils                                             (c) 2023 Illumina, Inc.
Stromberg, Roy, Platzer, Siddiqui, Ouyang, et al         3.21.0-0-gd2a0e953
---------------------------------------------------------------------------

Writing 505 intervals to database...

Time: 00:00:00.1

Gene-Disease Validity

The ClinGen Gene-Disease Clinical Validity curation process involves evaluating the strength of evidence supporting or refuting a claim that variation in a particular gene causes a particular disease. Illumina Connected Annotations reports these annotations for genes in the genes section of the JSON.

Publication

Strande NT, Riggs ER, Buchanan AH, et al. Evaluating the Clinical Validity of Gene-Disease Associations: An Evidence-Based Framework Developed by the Clinical Genome Resource. Am J Hum Genet. 2017;100(6):895-906. doi:10.1016/j.ajhg.2017.04.015

Source TSV

The source data comes in a CSV file that we convert to a TSV.

CLINGEN GENE VALIDITY CURATIONS
FILE CREATED: 2019-05-28
WEBPAGE: https://search.clinicalgenome.org/kb/gene-validity
+++++++++++,++++++++++++++,+++++++++++++,++++++++++++++++++,+++++++++,++++++++++++++,+++++++++++++,+++++++++++++++++++
GENE SYMBOL,GENE ID (HGNC),DISEASE LABEL,DISEASE ID (MONDO),SOP,CLASSIFICATION,ONLINE REPORT,CLASSIFICATION DATE
+++++++++++,++++++++++++++,+++++++++++++,++++++++++++++++++,+++++++++,++++++++++++++,+++++++++++++,+++++++++++++++++++
A2ML1,HGNC:23336,Noonan syndrome with multiple lentigines,MONDO_0007893,SOP5,No Reported Evidence,https://search.clinicalgenome.org/kb/gene-validity/59b87033-dd91-4f1e-aec1-c9b1f5124b16--2018-06-07T14:37:47,2018-06-07T14:37:47.175Z
A2ML1,HGNC:23336,cardiofaciocutaneous syndrome,MONDO_0015280,SOP5,No Reported Evidence,https://search.clinicalgenome.org/kb/gene-validity/fc3c41d8-8497-489b-a350-c9e30016bc6a--2018-06-07T14:31:03,2018-06-07T14:31:03.696Z
A2ML1,HGNC:23336,Costello syndrome,MONDO_0009026,SOP5,No Reported Evidence,https://search.clinicalgenome.org/kb/gene-validity/ea72ba8d-cf62-44bc-86be-da64e3848eba--2018-06-07T14:34:05,2018-06-07T14:34:05.324Z

Download URL

https://search.clinicalgenome.org/kb/downloads#section_gene-disease-validity

Conflict Resolution

Multiple Classifications

Here is an example of multiple classifications.

$ grep MONDO_0010192 ClinGen-Gene-Disease-Summary-2019-12-02.csv  | grep EDNRB
EDNRB,HGNC:3180,Waardenburg syndrome type 4A,MONDO_0010192,SOP6,Moderate,https://search.clinicalgenome.org/kb/gene-validity/d7abbd45-7915-437b-849b-dea876bfc2f5--2018-05-08T04:00:00,2018-05-08T04:00:00.000Z
EDNRB,HGNC:3180,Waardenburg syndrome type 4A,MONDO_0010192,SOP6,Limited,https://search.clinicalgenome.org/kb/gene-validity/73ee9727-60c1-40fd-830f-08c2b513d2ee--2018-05-08T04:00:00,2018-05-08T04:00:00.000Z

In such cases, we select the more severe classification.

Multiple Dates

$ grep MONDO_0016419 ClinGen-Gene-Disease-Summary-2019-12-02.csv  | grep MUTYH
MUTYH,HGNC:7527,hereditary breast carcinoma,MONDO_0016419,SOP4,No Reported Evidence,https://search.clinicalgenome.org/kb/gene-validity/9904,2017-05-24T00:00:00
MUTYH,HGNC:7527,hereditary breast carcinoma,MONDO_0016419,SOP4,No Reported Evidence,https://search.clinicalgenome.org/kb/gene-validity/9902,2017-05-25T00:00:00

If the classifications are the same, we should select the latest classification date.

JSON Output

"clingenGeneValidity":[
   {
      "diseaseId":"MONDO_0007893",
      "disease":"Noonan syndrome with multiple lentigines",
      "classification":"no reported evidence",
      "classificationDate":"2018-06-07"
   },
   {
      "diseaseId":"MONDO_0015280",
      "disease":"cardiofaciocutaneous syndrome",
      "classification":"no reported evidence",
      "classificationDate":"2018-06-07"
   }
]
FieldTypeNotes
clingenGeneValidityobject
diseaseIdstringMonarch Disease Ontology ID (MONDO)
diseasestringdisease label
classificationstringsee below for possible values
classificationDatestringyyyy-MM-dd

classification

  • no reported evidence
  • disputed
  • limited
  • moderate
  • definitive
  • strong
  • refuted
  • no known disease relationship

Building the supplementary files

The gene disease validity .nga for Illumina Connected Annotations can be built using the SAUtils command's DiseaseValidity subcommand. The only required data file is Clingen-Gene-Disease-Summary-2021-12-01.tsv (url provided above) and its associated .version file.

NAME=ClinGen disease validity curations
VERSION=20211201
DATE=2021-12-01
DESCRIPTION=Disease validity curations from ClinGen (dbVar)

Here is a sample run:

 dotnet SAUtils.dll DiseaseValidity
---------------------------------------------------------------------------
SAUtils                                             (c) 2023 Illumina, Inc.
Stromberg, Roy, Platzer, Siddiqui, Ouyang, et al         3.21.0-0-gd2a0e953
---------------------------------------------------------------------------

USAGE: dotnet SAUtils.dll diseasevalidity [options]
Creates a gene annotation database from ClinGen gene validity data

OPTIONS:
      --csv, -i <VALUE>      ClinGen gene validity file path
      --cache, -c <directory>
                             input cache directory
      --ref, -r <filename>   input reference filename
      --out, -o <VALUE>      output directory
      --help, -h             displays the help menu
      --version, -v          displays the version

dotnet SAUtils.dll DiseaseValidity --tsv Clingen-Gene-Disease-Summary-2021-12-01.tsv \\
--uga Cache --out SupplementaryDatabase
---------------------------------------------------------------------------
SAUtils                                             (c) 2023 Illumina, Inc.
Stromberg, Roy, Platzer, Siddiqui, Ouyang, et al         3.21.0-0-gd2a0e953
---------------------------------------------------------------------------

Number of geneIds missing from the cache:0 (0%)

Time: 00:00:00.2
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Version: 3.22

ClinGen

Overview

ClinGen is a National Institutes of Health (NIH)-funded resource dedicated to building a central resource that defines the clinical relevance of genes and variants for use in precision medicine and research.

Publication

Heidi L. Rehm, Ph.D., Jonathan S. Berg, M.D., Ph.D., Lisa D. Brooks, Ph.D., Carlos D. Bustamante, Ph.D., James P. Evans, M.D., Ph.D., Melissa J. Landrum, Ph.D., David H. Ledbetter, Ph.D., Donna R. Maglott, Ph.D., Christa Lese Martin, Ph.D., Robert L. Nussbaum, M.D., Sharon E. Plon, M.D., Ph.D., Erin M. Ramos, Ph.D., Stephen T. Sherry, Ph.D., and Michael S. Watson, Ph.D., for ClinGen. ClinGen The Clinical Genome Resource. N Engl J Med 2015; 372:2235-2242 June 4, 2015 DOI: 10.1056/NEJMsr1406261.

ISCA Regions

TSV Extraction

ClinGen contains only copy number variation variants, since the coordinates in ClinGen original file follow the same rule as BED format, the coordinates had to be adjusted to [BEGIN+1, END].

#bin    chrom   chromStart      chromEnd        name    score   strand  thickStart      thickEnd        attrCount       attrTags        attrVals
nsv530705       1       564405  8597804 0       1       copy_number_loss        pathogenic      False   Developmental delay AND/OR other significant developmental or morphological phenotypes
nsv530706       1       564424  3262790 0       1       copy_number_loss        pathogenic      False   Abnormal facial shape,Abnormality of cardiac morphology,Global developmental delay,Muscular hypotonia   HP:0001252,HP:0001263,HP:0001627,HP:0001999,MedGen:CN001147,MedGen:CN001157,MedGen:CN001482,MedGen:CN001810
nsv530707       1       564424  7068738 0       1       copy_number_loss        pathogenic      False   Abnormality of cardiac morphology,Cleft upper lip,Failure to thrive,Global developmental delay,Intrauterine growth retardation,Microcephaly,Short stature       HP:0000204,HP:0000252,HP:0001263,HP:0001508,HP:0001511,HP:0001627,HP:0004322,MedGen:C0349588,MedGen:C1845868,MedGen:C1853481,MedGen:C2364119,MedGen:CN000197,MedGen:CN001157,MedGen:CN001482
nsv533512       1       564435  649748  0       1       copy_number_loss        benign  False   Developmental delay AND/OR other significant developmental or morphological phenotypes
nsv931338       1       714078  4958499 0       1       copy_number_loss        pathogenic      False   Developmental delay AND/OR other significant developmental or morphological phenotypes
nsv530300       1       728138  5066371 1       0       copy_number_gain        pathogenic      False   Abnormality of cardiac morphology,Cleft palate,Global developmental delay       HP:0000175,HP:0001263,HP:0001627,MedGen:C2240378,MedGen:CN001157,MedGen:CN001482

Status levels

  • benign
  • curated benign
  • curated pathogenic
  • likely benign
  • likely pathogenic
  • path gain
  • path loss
  • pathogenic
  • uncertain

Parsing

We parse the ClinGen tsv file and extract the following:

  • chrom
  • chromStart (note this a 0-based coordinate)
  • chromEnd
  • attrTags
  • attrVals

attrTags and attrVals are comma separated lists. attrTags contains the field keys and attrVals contains the field values. We will parse the following keys from the two fields:

  • parent (this will be used as the ID in our JSON output)
  • clinical_int
  • validated
  • phenotype (this should be a string array)
  • phenotype_id (this should be a string array)

Observed losses and observed gains will be calculated from entries that share a common parent ID.

  • variants with a common parent ID and same coordinates are grouped
    • calculated observed losses, observed gains for each group
    • Clinical significance and validation status are collapsed using the priority strategy described below
  • Variants with the same parent ID can have different coordinates (mapped to hg38)
    • nsv491508 : chr14:105583663-106881350 and chr14:105605043-106766076 (only one example)
    • we kept both variants

Conflict Resolution

Clinical significance priority

When there are a mixture of variants belonging to the same parent ID, we will choose the most pathogenic clinical significance from the available values. i.e. if 3 samples were deemed pathogenic and 2 samples were likely pathogenic, we would list the variant as pathogenic.

Priority (high to low)

  • Priority
  • Pathogenic
  • Likely pathogenic
  • Benign
  • Likely benign
  • Uncertain significance

Validation Priority

When there are a mixture of variants belonging to same parent ID, we will set the validation status to true if any of the variants were validated.

Download URL

https://cirm.ucsc.edu/cgi-bin/hgTrackUi?db=hg19&g=iscaComposite

JSON Output

"clingen":[
   {
      "chromosome":"17",
      "begin":525,
      "end":14667519,
      "variantType":"copy_number_gain",
      "id":"nsv996083",
      "clinicalInterpretation":"pathogenic",
      "observedGains":1,
      "validated":true,
      "phenotypes":[
         "Intrauterine growth retardation"
      ],
      "phenotypeIds":[
         "HP:0001511",
         "MedGen:C1853481"
      ],
      "reciprocalOverlap":0.00131
   },
   {
      "chromosome":"17",
      "begin":45835,
      "end":7600330,
      "variantType":"copy_number_loss",
      "id":"nsv869419",
      "clinicalInterpretation":"pathogenic",
      "observedLosses":1,
      "validated":true,
      "phenotypes":[
         "Developmental delay AND/OR other significant developmental or morphological phenotypes"
      ],
      "reciprocalOverlap":0.00254
   }
]
FieldTypeNotes
clingenobject array
chromosomestringEnsembl-style chromosome names
begininteger1-based position
endinteger1-based position
variantTypestringAny of the sequence alterations defined here.
idstringIdentifier from the data source. Alternatively a VID
clinicalInterpretationstringsee possible values below
observedGainsintegerRange: 0 - (231 - 1). Only used if copy_number_variation, copy_number_loss, or copy_number_gain.
observedLossesintegerRange: 0 - (231 - 1). Only used if copy_number_variation, copy_number_loss, or copy_number_gain.
validatedboolean
phenotypesstring arrayDescription of the phenotype.
phenotypeIdsstring arrayDescription of the phenotype IDs.
reciprocalOverlapfloating pointRange: 0 - 1. E.g. 0.57 would indicate a 57% reciprocal overlap. Specified up to 5 decimal places (Not reported for Insertions).

clinicalInterpretation

  • benign
  • curated benign
  • curated pathogenic
  • likely benign
  • likely pathogenic
  • path gain
  • path loss
  • pathogenic
  • uncertain

Dosage Sensitivity Map

The Clinical Genome Resource (ClinGen) consortium is curating genes and regions of the genome to assess whether there is evidence to support that these genes/regions are dosage sensitive and should be targeted on a cytogenomic array. Illumina Connected Annotations reports these annotations for overlapping SVs.

Publication

Riggs ER, Nelson T, Merz A, Ackley T, Bunke B, Collins CD, Collinson MN, Fan YS, Goodenberger ML, Golden DM, Haglund-Hazy L, Krgovic D, Lamb AN, Lewis Z, Li G, Liu Y, Meck J, Neufeld-Kaiser W, Runke CK, Sanmann JN, Stavropoulos DJ, Strong E, Su M, Tayeh MK, Kokalj Vokac N, Thorland EC, Andersen E, Martin CL. Copy number variant discrepancy resolution using the ClinGen dosage sensitivity map results in updated clinical interpretations in ClinVar. Hum Mutat. 2018 Nov;39(11):1650-1659. doi: 10.1002/humu.23610. PMID: 30095202; PMCID: PMC7374944.

TSV Source files

Regions

#ClinGen Region Curation Results
#07 May,2019
#Genomic Locations are reported on GRCh38 (hg38): GCF_000001405.36
#https://www.ncbi.nlm.nih.gov/projects/dbvar/clingen
#to create link: https://www.ncbi.nlm.nih.gov/projects/dbvar/clingen/clingen_region.cgi?id=key
#ISCA ID    ISCA Region Name    cytoBand    Genomic Location    Haploinsufficiency Score    Haploinsufficiency Description  Haploinsufficiency PMID1    Haploinsufficiency PMID2    Haploinsufficiency PMID3    Triplosensitivity Score Triplosensitivity Description   Triplosensitivity PMID1 Triplosensitivity PMID2 Triplosensitivity PMID3 Date Last Evaluated Loss phenotype OMIM ID  Triplosensitive phenotype OMIM ID
ISCA-46299  Xp11.22 region (includes HUWE1) Xp11.22 tbd 0   No evidence available               3   Sufficient evidence for dosage pathogenicity    22840365    20655035    26692240    2018-11-19
ISCA-46295  15q13.3 recurrent region (D-CHRNA7 to BP5) (includes CHRNA7 and OTUD7A) 15q13.3 chr15:31727418-32153204 3   Sufficient evidence for dosage pathogenicity    19898479    20236110    22775350    40  Dosage sensitivity unlikely 26968334    22420048        2018-05-10
ISCA-46291  7q11.23 recurrent distal region (includes HIP1, YWHAG)  7q11.23 chr7:75528718-76433859  2   Some evidence for dosage pathogenicity  21109226    16971481        1   Little evidence for dosage pathogenicity    21109226    27867344        2018-12-31
ISCA-46290  Xp11.22p11.23 recurrent region (includes SHROOM4)   Xp11.22-p11.23  chrX: 48447780-52444264 0   No evidence available               3   Sufficient evidence for dosage pathogenicity    19716111    21418194    25425167    2017-12-14      300801

Genes

#ClinGen Gene Curation Results
#24 May,2019
#Genomic Locations are reported on GRCh37 (hg19): GCF_000001405.13
#https://www.ncbi.nlm.nih.gov/projects/dbvar/clingen
#to create link: https://www.ncbi.nlm.nih.gov/projects/dbvar/clingen/clingen_gene.cgi?sym=Gene Symbol
#Gene Symbol    Gene ID cytoBand    Genomic Location    Haploinsufficiency Score    Haploinsufficiency Description  Haploinsufficiency PMID1    Haploinsufficiency PMID2    Haploinsufficiency PMID3    Triplosensitivity Score Triplosensitivity Description   Triplosensitivity PMID1 Triplosensitivity PMID2 Triplosensitivity PMID3 Date Last Evaluated Loss phenotype OMIM ID  Triplosensitive phenotype OMIM ID
A4GALT  53947   22q13.2 chr22:43088121-43117307 30  Gene associated with autosomal recessive phenotype              0   No evidence available               2014-12-11  111400
AAGAB   79719   15q23   chr15:67493013-67547536 3   Sufficient evidence for dosage pathogenicity    23064416    23000146        0   No evidence available               2013-02-28  148600

Dosage Rating System

RatingPossible Clinical Interpretation
0No evidence to suggest that dosage sensitivity is associated with clinical phenotype
1Little evidence suggesting dosage sensitivity is associated with clinical phenotype
2Emerging evidence suggesting dosage sensitivity is associated with clinical phenotype
3Sufficient evidence suggesting dosage sensitivity is associated with clinical phenotype
30Gene associated with autosomal recessive phenotype
40Dosage sensitivity unlikely

Reference: https://www.ncbi.nlm.nih.gov/projects/dbvar/clingen/help.shtml

Download URL

ftp://ftp.clinicalgenome.org/

JSON Output

"clingenDosageSensitivityMap": [{
    "chromosome": "15",
    "begin": 30900686,
    "end": 32153204,
    "haploinsufficiency": "sufficient evidence suggesting dosage sensitivity is associated with clinical phenotype",
    "triplosensitivity": "little evidence suggesting dosage sensitivity is associated with clinical phenotype",
    "reciprocalOverlap": 0.00147,
    "annotationOverlap": 0.33994
},
{
    "chromosome": "15",
    "begin": 31727418,
    "end": 32153204,
    "haploinsufficiency": "sufficient evidence suggesting dosage sensitivity is associated with clinical phenotype",
    "triplosensitivity": "dosage sensitivity unlikely",
    "reciprocalOverlap": 0.00147,
    "annotationOverlap": 1
}]
FieldTypeNotes
clingenDosageSensitivityMapobject array
chromosomestringEnsembl-style chromosome names
begininteger1-based position
endinteger1-based position
haploinsufficiencystringsee possible values below
triplosensitivitystring(same as haploinsufficiency) 
reciprocalOverlapfloating pointRange: 0 - 1. E.g. 0.57 would indicate a 57% reciprocal overlap. Specified up to 5 decimal places (Not reported for Insertions).
annotationOverlapfloating pointRange: 0 - 1. E.g. 0.57 would indicate a 57% reciprocal overlap. Specified up to 5 decimal places (Not reported for Insertions).

haploinsufficiency and triplosensitivity

  • no evidence to suggest that dosage sensitivity is associated with clinical phenotype
  • little evidence suggesting dosage sensitivity is associated with clinical phenotype
  • emerging evidence suggesting dosage sensitivity is associated with clinical phenotype
  • sufficient evidence suggesting dosage sensitivity is associated with clinical phenotype
  • gene associated with autosomal recessive phenotype
  • dosage sensitivity unlikely

Building the supplementary files

The gene dosage sensitivity .nga for Illumina Connected Annotations can be built using the SAUtils command's DosageSensitivity subcommand. The required data file is ClinGen_gene_curation_list_{ASSEMBLY}.tsv (url provided above) and its associated .version file.

NAME=ClinGen Dosage Sensitivity Map
VERSION=20211201
DATE=2021-12-01
DESCRIPTION=Dosage sensitivity map from ClinGen (dbVar)

Here is a sample run:

dotnet SAUtils.dll DosageSensitivity
---------------------------------------------------------------------------
SAUtils                                             (c) 2023 Illumina, Inc.
Stromberg, Roy, Platzer, Siddiqui, Ouyang, et al         3.21.0-0-gd2a0e953
---------------------------------------------------------------------------

USAGE: dotnet SAUtils.dll dosagesensitivity [options]
Creates a gene annotation database from dbVar data

OPTIONS:
      --tsv, -t <VALUE>      input tsv file
      --out, -o <VALUE>      output directory
      --help, -h             displays the help menu
      --version, -v          displays the version

dotnet SAUtils.dll DosageSensitivity --out SupplementaryDatabase/64/GRCh37 --tsv ClinGen_gene_curation_list_GRCh37.tsv
---------------------------------------------------------------------------
SAUtils                                             (c) 2023 Illumina, Inc.
Stromberg, Roy, Platzer, Siddiqui, Ouyang, et al         3.21.0-0-gd2a0e953
---------------------------------------------------------------------------


Time: 00:00:00.1

For building the .nsi files, we use the SAUtils command's DosageMapRegions subcommand. The required data file is ClinGen_region_curation_list_{ASSEMBLY}.tsv (url provided above) and its associated .version file.

NAME=ClinGen Dosage Sensitivity Map
VERSION=20211201
DATE=2021-12-01
DESCRIPTION=Dosage sensitivity map from ClinGen (dbVar)

Here is a sample run:

dotnet SAUtils.dll DosageMapRegions
---------------------------------------------------------------------------
SAUtils                                             (c) 2023 Illumina, Inc.
Stromberg, Roy, Platzer, Siddiqui, Ouyang, et al         3.21.0-0-gd2a0e953
---------------------------------------------------------------------------

USAGE: dotnet SAUtils.dll dosagemapregions [options]
Creates an interval annotation database from dbVar data

OPTIONS:
      --tsv, -t <VALUE>      input tsv file
      --ref, -r <filename>   input reference filename
      --out, -o <VALUE>      output directory
      --help, -h             displays the help menu
      --version, -v          displays the version

dotnet SAUtils.dll DosageMapRegions --out SupplementaryDatabase/64/GRCh37 --ref References/7/Homo_sapiens.GRCh37.Nirvana.dat --tsv ClinGen_region_curation_list_GRCh37.tsv
---------------------------------------------------------------------------
SAUtils                                             (c) 2023 Illumina, Inc.
Stromberg, Roy, Platzer, Siddiqui, Ouyang, et al         3.21.0-0-gd2a0e953
---------------------------------------------------------------------------

Writing 505 intervals to database...

Time: 00:00:00.1

Gene-Disease Validity

The ClinGen Gene-Disease Clinical Validity curation process involves evaluating the strength of evidence supporting or refuting a claim that variation in a particular gene causes a particular disease. Illumina Connected Annotations reports these annotations for genes in the genes section of the JSON.

Publication

Strande NT, Riggs ER, Buchanan AH, et al. Evaluating the Clinical Validity of Gene-Disease Associations: An Evidence-Based Framework Developed by the Clinical Genome Resource. Am J Hum Genet. 2017;100(6):895-906. doi:10.1016/j.ajhg.2017.04.015

Source TSV

The source data comes in a CSV file that we convert to a TSV.

CLINGEN GENE VALIDITY CURATIONS
FILE CREATED: 2019-05-28
WEBPAGE: https://search.clinicalgenome.org/kb/gene-validity
+++++++++++,++++++++++++++,+++++++++++++,++++++++++++++++++,+++++++++,++++++++++++++,+++++++++++++,+++++++++++++++++++
GENE SYMBOL,GENE ID (HGNC),DISEASE LABEL,DISEASE ID (MONDO),SOP,CLASSIFICATION,ONLINE REPORT,CLASSIFICATION DATE
+++++++++++,++++++++++++++,+++++++++++++,++++++++++++++++++,+++++++++,++++++++++++++,+++++++++++++,+++++++++++++++++++
A2ML1,HGNC:23336,Noonan syndrome with multiple lentigines,MONDO_0007893,SOP5,No Reported Evidence,https://search.clinicalgenome.org/kb/gene-validity/59b87033-dd91-4f1e-aec1-c9b1f5124b16--2018-06-07T14:37:47,2018-06-07T14:37:47.175Z
A2ML1,HGNC:23336,cardiofaciocutaneous syndrome,MONDO_0015280,SOP5,No Reported Evidence,https://search.clinicalgenome.org/kb/gene-validity/fc3c41d8-8497-489b-a350-c9e30016bc6a--2018-06-07T14:31:03,2018-06-07T14:31:03.696Z
A2ML1,HGNC:23336,Costello syndrome,MONDO_0009026,SOP5,No Reported Evidence,https://search.clinicalgenome.org/kb/gene-validity/ea72ba8d-cf62-44bc-86be-da64e3848eba--2018-06-07T14:34:05,2018-06-07T14:34:05.324Z

Download URL

https://search.clinicalgenome.org/kb/downloads#section_gene-disease-validity

Conflict Resolution

Multiple Classifications

Here is an example of multiple classifications.

$ grep MONDO_0010192 ClinGen-Gene-Disease-Summary-2019-12-02.csv  | grep EDNRB
EDNRB,HGNC:3180,Waardenburg syndrome type 4A,MONDO_0010192,SOP6,Moderate,https://search.clinicalgenome.org/kb/gene-validity/d7abbd45-7915-437b-849b-dea876bfc2f5--2018-05-08T04:00:00,2018-05-08T04:00:00.000Z
EDNRB,HGNC:3180,Waardenburg syndrome type 4A,MONDO_0010192,SOP6,Limited,https://search.clinicalgenome.org/kb/gene-validity/73ee9727-60c1-40fd-830f-08c2b513d2ee--2018-05-08T04:00:00,2018-05-08T04:00:00.000Z

In such cases, we select the more severe classification.

Multiple Dates

$ grep MONDO_0016419 ClinGen-Gene-Disease-Summary-2019-12-02.csv  | grep MUTYH
MUTYH,HGNC:7527,hereditary breast carcinoma,MONDO_0016419,SOP4,No Reported Evidence,https://search.clinicalgenome.org/kb/gene-validity/9904,2017-05-24T00:00:00
MUTYH,HGNC:7527,hereditary breast carcinoma,MONDO_0016419,SOP4,No Reported Evidence,https://search.clinicalgenome.org/kb/gene-validity/9902,2017-05-25T00:00:00

If the classifications are the same, we should select the latest classification date.

JSON Output

"clingenGeneValidity":[
   {
      "diseaseId":"MONDO_0007893",
      "disease":"Noonan syndrome with multiple lentigines",
      "classification":"no reported evidence",
      "classificationDate":"2018-06-07"
   },
   {
      "diseaseId":"MONDO_0015280",
      "disease":"cardiofaciocutaneous syndrome",
      "classification":"no reported evidence",
      "classificationDate":"2018-06-07"
   }
]
FieldTypeNotes
clingenGeneValidityobject
diseaseIdstringMonarch Disease Ontology ID (MONDO)
diseasestringdisease label
classificationstringsee below for possible values
classificationDatestringyyyy-MM-dd

classification

  • no reported evidence
  • disputed
  • limited
  • moderate
  • definitive
  • strong
  • refuted
  • no known disease relationship

Building the supplementary files

The gene disease validity .nga for Illumina Connected Annotations can be built using the SAUtils command's DiseaseValidity subcommand. The only required data file is Clingen-Gene-Disease-Summary-2021-12-01.tsv (url provided above) and its associated .version file.

NAME=ClinGen disease validity curations
VERSION=20211201
DATE=2021-12-01
DESCRIPTION=Disease validity curations from ClinGen (dbVar)

Here is a sample run:

 dotnet SAUtils.dll DiseaseValidity
---------------------------------------------------------------------------
SAUtils                                             (c) 2023 Illumina, Inc.
Stromberg, Roy, Platzer, Siddiqui, Ouyang, et al         3.21.0-0-gd2a0e953
---------------------------------------------------------------------------

USAGE: dotnet SAUtils.dll diseasevalidity [options]
Creates a gene annotation database from ClinGen gene validity data

OPTIONS:
      --csv, -i <VALUE>      ClinGen gene validity file path
      --cache, -c <directory>
                             input cache directory
      --ref, -r <filename>   input reference filename
      --out, -o <VALUE>      output directory
      --help, -h             displays the help menu
      --version, -v          displays the version

dotnet SAUtils.dll DiseaseValidity --tsv Clingen-Gene-Disease-Summary-2021-12-01.tsv \\
--uga Cache --out SupplementaryDatabase
---------------------------------------------------------------------------
SAUtils                                             (c) 2023 Illumina, Inc.
Stromberg, Roy, Platzer, Siddiqui, Ouyang, et al         3.21.0-0-gd2a0e953
---------------------------------------------------------------------------

Number of geneIds missing from the cache:0 (0%)

Time: 00:00:00.2
+ + \ No newline at end of file diff --git a/3.22/data-sources/clinvar-json/index.html b/3.22/data-sources/clinvar-json/index.html index 83c385cc..05348790 100644 --- a/3.22/data-sources/clinvar-json/index.html +++ b/3.22/data-sources/clinvar-json/index.html @@ -6,13 +6,13 @@ clinvar-json | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

clinvar-json

small variants:

"clinvar":[
   {
      "id":"VCV000036581.3",
      "reviewStatus":"reviewed by expert panel",
      "significance":[
         "benign"
      ],
      "refAllele":"G",
      "altAllele":"A",
      "lastUpdatedDate":"2020-03-01",
      "isAlleleSpecific":true
   },
   {
      "id":"RCV000030258.4",
      "variationId":"VCV000036581.3",
      "reviewStatus":"reviewed by expert panel",
      "alleleOrigins":[
         "germline"
      ],
      "refAllele":"G",
      "altAllele":"A",
      "phenotypes":[
         "Lynch syndrome"
      ],
      "medGenIds":[
         "C1333990"
      ],
      "omimIds":[
         "120435"
      ],
      "significance":[
         "benign"
      ],
      "lastUpdatedDate":"2017-05-01",
      "isAlleleSpecific":true
   }
]

large variants:

"clinvar":[
   {
      "chromosome":"1", 
      "begin":629025, 
      "end":8537745, 
      "variantType":"copy_number_loss", 
      "id":"RCV000051993.4", 
      "variationId":"VCV000058242.1", 
      "reviewStatus":"criteria provided, single submitter", 
      "alleleOrigins":[
         "not provided"
      ], 
      "phenotypes":[
         "See cases"
      ], 
      "significance":[
         "pathogenic"
      ], 
      "lastUpdatedDate":"2022-04-21", 
      "pubMedIds":[
         "21844811"
      ]
   },
   {
      "id":"VCV000058242.1",
      "reviewStatus":"criteria provided, single submitter",
      "significance":[
         "pathogenic"
      ],
      "lastUpdatedDate":"2022-04-21"
   },
        ......
]
FieldTypeNotes
idstringClinVar ID
variationIdstringClinVar VCV ID
variantTypestringvariant type
reviewStatusstringsee possible values below
alleleOriginsstring arraysee possible values below
refAllelestring
altAllelestring
phenotypesstring array
medGenIdsstring arrayMedGen IDs
omimIdsstring arrayOMIM IDs
orphanetIdsstring arrayOrphanet IDs
significancestring arraysee possible values below
lastUpdatedDatestringyyyy-MM-dd
pubMedIdsstring arrayPubMed IDs
isAlleleSpecificbooltrue when the current variant alternate allele matches the ClinVar alternate allele

reviewStatus:

  • no assertion provided
  • no assertion criteria provided
  • criteria provided, single submitter
  • practice guideline
  • classified by multiple submitters
  • criteria provided, conflicting interpretations
  • criteria provided, multiple submitters, no conflicts
  • no interpretation for the single variant

alleleOrigins:

  • unknown
  • other
  • germline
  • somatic
  • inherited
  • paternal
  • maternal
  • de-novo
  • biparental
  • uniparental
  • not-tested
  • tested-inconclusive

significance:

  • uncertain significance
  • not provided
  • benign
  • likely benign
  • likely pathogenic
  • pathogenic
  • drug response
  • histocompatibility
  • association
  • risk factor
  • protective
  • affects
  • conflicting data from submitters
  • other
  • no interpretation for the single variant
  • conflicting interpretations of pathogenicity
- - +
Skip to main content
Version: 3.22

clinvar-json

small variants:

"clinvar":[
   {
      "id":"VCV000036581.3",
      "reviewStatus":"reviewed by expert panel",
      "significance":[
         "benign"
      ],
      "refAllele":"G",
      "altAllele":"A",
      "lastUpdatedDate":"2020-03-01",
      "isAlleleSpecific":true
   },
   {
      "id":"RCV000030258.4",
      "variationId":"VCV000036581.3",
      "reviewStatus":"reviewed by expert panel",
      "alleleOrigins":[
         "germline"
      ],
      "refAllele":"G",
      "altAllele":"A",
      "phenotypes":[
         "Lynch syndrome"
      ],
      "medGenIds":[
         "C1333990"
      ],
      "omimIds":[
         "120435"
      ],
      "significance":[
         "benign"
      ],
      "lastUpdatedDate":"2017-05-01",
      "isAlleleSpecific":true
   }
]

large variants:

"clinvar":[
   {
      "chromosome":"1", 
      "begin":629025, 
      "end":8537745, 
      "variantType":"copy_number_loss", 
      "id":"RCV000051993.4", 
      "variationId":"VCV000058242.1", 
      "reviewStatus":"criteria provided, single submitter", 
      "alleleOrigins":[
         "not provided"
      ], 
      "phenotypes":[
         "See cases"
      ], 
      "significance":[
         "pathogenic"
      ], 
      "lastUpdatedDate":"2022-04-21", 
      "pubMedIds":[
         "21844811"
      ]
   },
   {
      "id":"VCV000058242.1",
      "reviewStatus":"criteria provided, single submitter",
      "significance":[
         "pathogenic"
      ],
      "lastUpdatedDate":"2022-04-21"
   },
        ......
]
FieldTypeNotes
idstringClinVar ID
variationIdstringClinVar VCV ID
variantTypestringvariant type
reviewStatusstringsee possible values below
alleleOriginsstring arraysee possible values below
refAllelestring
altAllelestring
phenotypesstring array
medGenIdsstring arrayMedGen IDs
omimIdsstring arrayOMIM IDs
orphanetIdsstring arrayOrphanet IDs
significancestring arraysee possible values below
lastUpdatedDatestringyyyy-MM-dd
pubMedIdsstring arrayPubMed IDs
isAlleleSpecificbooltrue when the current variant alternate allele matches the ClinVar alternate allele

reviewStatus:

  • no assertion provided
  • no assertion criteria provided
  • criteria provided, single submitter
  • practice guideline
  • classified by multiple submitters
  • criteria provided, conflicting interpretations
  • criteria provided, multiple submitters, no conflicts
  • no interpretation for the single variant

alleleOrigins:

  • unknown
  • other
  • germline
  • somatic
  • inherited
  • paternal
  • maternal
  • de-novo
  • biparental
  • uniparental
  • not-tested
  • tested-inconclusive

significance:

  • uncertain significance
  • not provided
  • benign
  • likely benign
  • likely pathogenic
  • pathogenic
  • drug response
  • histocompatibility
  • association
  • risk factor
  • protective
  • affects
  • conflicting data from submitters
  • other
  • no interpretation for the single variant
  • conflicting interpretations of pathogenicity
+ + \ No newline at end of file diff --git a/3.22/data-sources/clinvar/index.html b/3.22/data-sources/clinvar/index.html index d86c3cd1..1f823832 100644 --- a/3.22/data-sources/clinvar/index.html +++ b/3.22/data-sources/clinvar/index.html @@ -6,14 +6,14 @@ ClinVar | IlluminaConnectedAnnotations - - + +
Skip to main content
Version: 3.22

ClinVar

Overview

ClinVar is a freely accessible, public archive of reports of the relationships among human variations and phenotypes, with supporting evidence. ClinVar thus facilitates access to and communication about the relationships asserted between human variation and observed health status, and the history of that interpretation.

Publication

Melissa J Landrum, Jennifer M Lee, Mark Benson, Garth R Brown, Chen Chao, Shanmuga Chitipiralla, Baoshan Gu, Jennifer Hart, Douglas Hoffman, Wonhee Jang, Karen Karapetyan, Kenneth Katz, Chunlei Liu, Zenith Maddipatla, Adriana Malheiro, Kurt McDaniel, Michael Ovetsky, George Riley, George Zhou, J Bradley Holmes, Brandi L Kattman, Donna R Maglott, ClinVar: improving access to variant interpretations and supporting evidence, Nucleic Acids Research, 46, Issue D1, 4 January 2018, Pages D1062–D1067, https://doi.org/10.1093/nar/gkx1153

RCV File

Example

Here's a full RCV entry.

Parsing

In the following section, we discuss which field of the XML was used to extract information that is presented in the JSON output.

ID

<ClinVarSet>
   <ReferenceClinVarAssertion>
       <ClinVarAccession Acc="RCV000000001" Version="2">
</ClinVarSet>

The Acc and Version fields are merged to form the ID (RCV000000001.2)

LastUpdatedDate

<ClinVarSet>
   <ReferenceClinVarAssertion DateCreated="2012-08-13" DateLastUpdated="2016-02-17" ID="57604" >
</ClinVarSet>

Significance

<ClinVarSet>
   <ReferenceClinVarAssertion>
       <ClinicalSignificance DateLastEvaluated="1996-04-01">
           <ReviewStatus>no assertion criteria provided</ReviewStatus>
           <Description>Pathogenic</Description>
       </ClinicalSignificance>
</ClinVarSet>

ReviewStatus

<ClinVarSet>
   <ReferenceClinVarAssertion>
       <ClinicalSignificance DateLastEvaluated="1996-04-01">
           <ReviewStatus>no assertion criteria provided</ReviewStatus>
           <Description>Pathogenic</Description>
       </ClinicalSignificance>
</ClinVarSet>

Phenotypes

<ReferenceClinVarAssertion>
   <TraitSet Type="Disease" ID="62">
       <Trait Type="Disease">
            <Name>
               <ElementValue Type="Preferred">Joubert syndrome 9</ElementValue>
            </Name>
        </Trait>
   </TraitSet>
</ReferenceClinVarAssertion>

We only use the field with Type="Preferred". Multiple phenotypes may be reported

Location, Variant Type and Variant Id

<ReferenceClinVarAssertion>
<GenotypeSet Type="CompoundHeterozygote" ID="424709">
   <MeasureSet Type="Variant" ID="81">
       <Measure Type="single nucleotide variant" ID="15120">
        <SequenceLocation Assembly="GRCh38" AssemblyAccessionVersion="GCF_000001405.38"
          AssemblyStatus="current" Chr="10" Accession="NC_000010.11" start="89222510"
          stop="89222510" display_start="89222510" display_stop="89222510" variantLength="1"
          positionVCF="89222510" referenceAlleleVCF="C" alternateAlleleVCF="T"/>
        <SequenceLocation Assembly="GRCh37" AssemblyAccessionVersion="GCF_000001405.25"
          AssemblyStatus="previous" Chr="10" Accession="NC_000010.10" start="90982267"
          stop="90982267" display_start="90982267" display_stop="90982267" variantLength="1"
          positionVCF="90982267" referenceAlleleVCF="C" alternateAlleleVCF="T"/>
       </Measure>
   </MeasureSet>
 </GenotypeSet>
</ReferenceClinVarAssertion>
  • The variant position is extracted from the fields for their respective assemblies.
  • Updated records contain positionVCF, referenceAlleleVCF and alternateAlleleVCF fields and when present, we use them to create the variant.
  • For older records, since "start' and "stop" fields are not always available, we use the "display_start" and "display_end" fields.
  • If a required allele is not available, we extract it from the reference sequence.
  • Only variants having a dbSNP id are extracted.
  • Note that a ClinVar accession may have multiple variants associated with it (possible in different locations)
  • VariantId is extracted from the MeasureSet attributes.
  • VariantType is extracted from the Measure attributes.
    unsupported variant types

    We currently don't support the following variant types:

    • Microsatellite
    • protein only
    • fusion
    • Complex
    • Variation
    • Translocation

MedGen, OMIM, Orphanet IDs

<ReferenceClinVarAssertion>
   <TraitSet Type="Disease" ID="175">
       <Trait ID="3036" Type="Disease">
           <XRef ID="C0086651" DB="MedGen"/>
           <XRef ID="309297" DB="Orphanet"/>
           <XRef ID="582" DB="Orphanet"/>
           <XRef Type="MIM" ID="253000" DB="OMIM"/>
       </Trait>
   </TraitSet>
</ReferenceClinVarAssertion>

AlleleOrigins

<ClinVarAssertion>
     <Origin>germline</Origin>
</ClinVarAssertion>

We only extract all Allele Origins from Submissions (SCV) entries.

PubMedIds

<ClinVarAssertion>
   <ClinicalSignificance DateLastEvaluated="1996-04-01">
       <Citation Type="general">
           <ID Source="PubMed">12114475</ID>
       </Citation>
   </ClinicalSignificance>
   <AttributeSet>
       <Attribute Type="AssertionMethod">LMM Criteria</Attribute>
       <Citation>
            <ID Source="PubMed">24033266</ID>
       </Citation>
   </AttributeSet>
   <ObservedIn>
       <ObservedData ID="9727445">
           <Citation Type="general">
               <ID Source="PubMed">9113933</ID>
           </Citation>
       </ObservedData>
   </ObservedIn>
   <Citation Type="general">
       <ID Source="PubMed">23757202</ID>
   </Citation>
</ClinVarAssertion>

We only extract all Pubmed Ids from Submissions (SCV) entries.

Parsing Significance

Extracting significance(s) may involve parsing multiple fields. Take the following snippets into consideration.

<ClinicalSignificance DateLastEvaluated="1996-04-01">
    <ReviewStatus>no assertion criteria provided</ReviewStatus>
    <Description>Pathogenic</Description>
</ClinicalSignificance>

<ClinicalSignificance DateLastEvaluated="2016-10-13">
    <ReviewStatus>criteria provided, multiple submitters, no conflicts</ReviewStatus>
    <Description>Pathogenic/Likely pathogenic</Description>
</ClinicalSignificance>

<ClinicalSignificance DateLastEvaluated="2012-06-07">
    <ReviewStatus>no assertion criteria provided</ReviewStatus>
    <Description>Conflicting interpretations of pathogenicity</Description>
    <Explanation DataSource="ClinVar" Type="public">Pathogenic(1);Uncertain significance(1)</Explanation>
</ClinicalSignificance>

Given the evidence, we converted the significance field into an array of strings which may be parsed out of the Descriptions or Explanation fields.

Varying Delimiters

The delimiters in each field may vary. Currently, the delimiters for Description are , and /. The delimiters for Explanation are ; and /.

VCV File

Example

<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
<ClinVarVariationRelease xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:noNamespaceSchemaLocation="http://ftp.ncbi.nlm.nih.gov/pub/clinvar/xsd_public/clinvar_variation/variation_archive_1.4.xsd" ReleaseDate="2019-12-31">
<VariationArchive VariationID="431749" VariationName="GRCh37/hg19 1p36.31(chr1:6051187-6158763)" VariationType="copy number gain" DateCreated="2017-08-12" DateLastUpdated="2019-09-10" Accession="VCV000431749" Version="1" RecordType="included" NumberOfSubmissions="0" NumberOfSubmitters="0">
  <RecordStatus>current</RecordStatus>
  <Species>Homo sapiens</Species>
  <IncludedRecord>
    <SimpleAllele AlleleID="425239" VariationID="431749">
      <GeneList>
        <Gene Symbol="KCNAB2" FullName="potassium voltage-gated channel subfamily A regulatory beta subunit 2" GeneID="8514" HGNC_ID="HGNC:6229" Source="calculated" RelationshipType="genes overlapped by variant">
          <Location>
            <CytogeneticLocation>1p36.31</CytogeneticLocation>
            <SequenceLocation Assembly="GRCh38" AssemblyAccessionVersion="GCF_000001405.38" AssemblyStatus="current" Chr="1" Accession="NC_000001.11" start="5992639" stop="6101186" display_start="5992639" display_stop="6101186" Strand="+"/>
            <SequenceLocation Assembly="GRCh37" AssemblyAccessionVersion="GCF_000001405.25" AssemblyStatus="previous" Chr="1" Accession="NC_000001.10" start="6052357" stop="6161252" display_start="6052357" display_stop="6161252" Strand="+"/>
          </Location>
          <OMIM>601142</OMIM>
        </Gene>
        <Gene Symbol="NPHP4" FullName="nephrocystin 4" GeneID="261734" HGNC_ID="HGNC:19104" Source="calculated" RelationshipType="genes overlapped by variant">
          <Location>
            <CytogeneticLocation>1p36.31</CytogeneticLocation>
            <SequenceLocation Assembly="GRCh38" AssemblyAccessionVersion="GCF_000001405.38" AssemblyStatus="current" Chr="1" Accession="NC_000001.11" start="5862810" stop="5992425" display_start="5862810" display_stop="5992425" Strand="-"/>
            <SequenceLocation Assembly="GRCh37" AssemblyAccessionVersion="GCF_000001405.25" AssemblyStatus="previous" Chr="1" Accession="NC_000001.10" start="5922869" stop="6052532" display_start="5922869" display_stop="6052532" Strand="-"/>
          </Location>
          <OMIM>607215</OMIM>
        </Gene>
      </GeneList>
      <Name>GRCh37/hg19 1p36.31(chr1:6051187-6158763)</Name>
      <VariantType>copy number gain</VariantType>
      <Location>
        <CytogeneticLocation>1p36.31</CytogeneticLocation>
        <SequenceLocation Assembly="GRCh37" AssemblyAccessionVersion="GCF_000001405.25" forDisplay="true" AssemblyStatus="previous" Chr="1" Accession="NC_000001.10" start="6051187" stop="6158763" display_start="6051187" display_stop="6158763"/>      </Location>
      <Interpretations>
        <Interpretation NumberOfSubmissions="0" NumberOfSubmitters="0" Type="Clinical significance">
          <Description>no interpretation for the single variant</Description>
        </Interpretation>
      </Interpretations>
      <XRefList>
        <XRef Type="Interpreted" ID="431733" DB="ClinVar"/>
      </XRefList>
    </SimpleAllele>
    <ReviewStatus>no interpretation for the single variant</ReviewStatus>
    <Interpretations>
      <Interpretation NumberOfSubmissions="0" NumberOfSubmitters="0" Type="Clinical significance">
        <Description>no interpretation for the single variant</Description>
      </Interpretation>
    </Interpretations>
    <SubmittedInterpretationList>
      <SCV Title="SUB1895145" Accession="SCV000296057" Version="1"/>
    </SubmittedInterpretationList>
    <InterpretedVariationList>
      <InterpretedVariation VariationID="431733" Accession="VCV000431733" Version="1"/>
    </InterpretedVariationList>
  </IncludedRecord>
</VariationArchive>
</ClinVarVariationRelease>

Parsing

In the following section, we discuss which field of the XML was used to extract information that is presented in the JSON output.

id

<VariationArchive VariationID="431749" VariationName="GRCh37/hg19 1p36.31(chr1:6051187-6158763)" VariationType="copy number gain" DateCreated="2017-08-12" DateLastUpdated="2019-09-10" Accession="VCV000431749" Version="1" RecordType="included" NumberOfSubmissions="0" NumberOfSubmitters="0">

The Acc and Version fields are merged to form the ID (RCV000000001.2)

significance

<ClinVarVariationRelease>
  <VariationArchive>
    <IncludedRecord>
      <SimpleAllele>
        <Interpretations>
          <Interpretation NumberOfSubmissions="0" NumberOfSubmitters="0" Type="Clinical significance">
            <Description>no interpretation for the single variant</Description>
          </Interpretation>
        </Interpretations>
      </SimpleAllele>
    </IncludedRecord>
  </VariationArchive>
</ClinVarVariationRelease>

May have multiple significances listed.

reviewStatus

<ClinVarVariationRelease>
  <VariationArchive>
    <IncludedRecord>
      <ReviewStatus>no interpretation for the single variant</ReviewStatus>
    </IncludedRecord>
  </VariationArchive>
</ClinVarVariationRelease>

Known Issues

Known Issues
  • The XML file contains ~1k more entries (out of 162K) than the VCF file
  • The XML file does not have a field indicating that a record is associated with the reference base - something that was present in VCF
  • The XML file contains entries (e.g. RCV000016645 version=1) which have IUPAC ambiguous bases ("R", "Y", "H", -etc.) as their alternate allele

Download URLs

ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/xml/ClinVarFullRelease_00-latest.xml.gz

https://ftp.ncbi.nlm.nih.gov/pub/clinvar/xml/clinvar_variation/ClinVarVariationRelease_00-latest.xml.gz

JSON Output

small variants:

"clinvar":[
   {
      "id":"VCV000036581.3",
      "reviewStatus":"reviewed by expert panel",
      "significance":[
         "benign"
      ],
      "refAllele":"G",
      "altAllele":"A",
      "lastUpdatedDate":"2020-03-01",
      "isAlleleSpecific":true
   },
   {
      "id":"RCV000030258.4",
      "variationId":"VCV000036581.3",
      "reviewStatus":"reviewed by expert panel",
      "alleleOrigins":[
         "germline"
      ],
      "refAllele":"G",
      "altAllele":"A",
      "phenotypes":[
         "Lynch syndrome"
      ],
      "medGenIds":[
         "C1333990"
      ],
      "omimIds":[
         "120435"
      ],
      "significance":[
         "benign"
      ],
      "lastUpdatedDate":"2017-05-01",
      "isAlleleSpecific":true
   }
]

large variants:

"clinvar":[
   {
      "chromosome":"1", 
      "begin":629025, 
      "end":8537745, 
      "variantType":"copy_number_loss", 
      "id":"RCV000051993.4", 
      "variationId":"VCV000058242.1", 
      "reviewStatus":"criteria provided, single submitter", 
      "alleleOrigins":[
         "not provided"
      ], 
      "phenotypes":[
         "See cases"
      ], 
      "significance":[
         "pathogenic"
      ], 
      "lastUpdatedDate":"2022-04-21", 
      "pubMedIds":[
         "21844811"
      ]
   },
   {
      "id":"VCV000058242.1",
      "reviewStatus":"criteria provided, single submitter",
      "significance":[
         "pathogenic"
      ],
      "lastUpdatedDate":"2022-04-21"
   },
        ......
]
FieldTypeNotes
idstringClinVar ID
variationIdstringClinVar VCV ID
variantTypestringvariant type
reviewStatusstringsee possible values below
alleleOriginsstring arraysee possible values below
refAllelestring
altAllelestring
phenotypesstring array
medGenIdsstring arrayMedGen IDs
omimIdsstring arrayOMIM IDs
orphanetIdsstring arrayOrphanet IDs
significancestring arraysee possible values below
lastUpdatedDatestringyyyy-MM-dd
pubMedIdsstring arrayPubMed IDs
isAlleleSpecificbooltrue when the current variant alternate allele matches the ClinVar alternate allele

reviewStatus:

  • no assertion provided
  • no assertion criteria provided
  • criteria provided, single submitter
  • practice guideline
  • classified by multiple submitters
  • criteria provided, conflicting interpretations
  • criteria provided, multiple submitters, no conflicts
  • no interpretation for the single variant

alleleOrigins:

  • unknown
  • other
  • germline
  • somatic
  • inherited
  • paternal
  • maternal
  • de-novo
  • biparental
  • uniparental
  • not-tested
  • tested-inconclusive

significance:

  • uncertain significance
  • not provided
  • benign
  • likely benign
  • likely pathogenic
  • pathogenic
  • drug response
  • histocompatibility
  • association
  • risk factor
  • protective
  • affects
  • conflicting data from submitters
  • other
  • no interpretation for the single variant
  • conflicting interpretations of pathogenicity

Building the supplementary files

The ClinVar .nsa and .nsi for Illumina Connected Annotations can be built using the SAUtils command's clinvar subcommand.

Source data files

Two input .xml files and a .version file are required in order to build the .nsa and .nsi file. You should have the following files:

ClinVarFullRelease_00-latest.xml.gz     ClinVarVariationRelease_00-latest.xml.gz
ClinVarFullRelease_00-latest.xml.gz.version

The version file is a text file with the follwoing format.

NAME=ClinVar
VERSION=20220505
DATE=2022-05-05
DESCRIPTION=A freely accessible, public archive of reports of the relationships among human variations and phenotypes, with supporting evidence

The help menu for the utility is as follows:

dotnet SAUtils.dll clinvar
---------------------------------------------------------------------------
SAUtils                                             (c) 2022 Illumina, Inc.
Stromberg, Roy, Platzer, Siddiqui, Ouyang, et al                     3.18.1
---------------------------------------------------------------------------

USAGE: dotnet SAUtils.dll clinvar [options]
Creates a supplementary database with ClinVar annotations

OPTIONS:
      --ref, -r <VALUE>      compressed reference sequence file
      --rcv, -i <VALUE>      ClinVar Full release XML file
      --vcv, -c <VALUE>      ClinVar Variation release XML file
      --out, -o <VALUE>      output directory
      --help, -h             displays the help menu
      --version, -v          displays the version

dotnet SAUtils.dll clinvar

Here is a sample execution:

dotnet SAUtils.dll clinvar \\
--ref ~/development/References/7/Homo_sapiens.GRCh38.Nirvana.dat --rcv ClinVarFullRelease_00-latest.xml.gz \\
--vcv ClinVarVariationRelease_00-latest.xml.gz --out ~/development/SupplementaryDatabase/63/GRCh38
---------------------------------------------------------------------------
SAUtils                                             (c) 2022 Illumina, Inc.
Stromberg, Roy, Platzer, Siddiqui, Ouyang, et al                     3.18.1
---------------------------------------------------------------------------

Found 1535677 VCV records
Unknown vcv id:225946 found in RCV000211201.2
Unknown vcv id:225946 found in RCV000211253.2
Unknown vcv id:225946 found in RCV000211375.2
Unknown vcv id:976117 found in RCV001253316.1
Unknown vcv id:1321016 found in RCV001776995.2
3 unknown VCVs found in RCVs.
225946,976117,1321016
0 unknown VCVs found in RCVs.
Chromosome 1 completed in 00:00:15.1
Chromosome 2 completed in 00:00:20.0
Chromosome 3 completed in 00:00:09.7
Chromosome 4 completed in 00:00:05.9
Chromosome 5 completed in 00:00:09.8
Chromosome 6 completed in 00:00:08.3
Chromosome 7 completed in 00:00:08.7
Chromosome 8 completed in 00:00:06.2
Chromosome 9 completed in 00:00:08.6
Chromosome 10 completed in 00:00:07.0
Chromosome 11 completed in 00:00:11.7
Chromosome 12 completed in 00:00:08.0
Chromosome 13 completed in 00:00:06.3
Chromosome 14 completed in 00:00:06.0
Chromosome 15 completed in 00:00:06.6
Chromosome 16 completed in 00:00:10.8
Chromosome 17 completed in 00:00:13.8
Chromosome 18 completed in 00:00:02.9
Chromosome 19 completed in 00:00:08.7
Chromosome 20 completed in 00:00:03.6
Chromosome 21 completed in 00:00:02.4
Chromosome 22 completed in 00:00:03.6
Chromosome MT completed in 00:00:00.2
Chromosome X completed in 00:00:07.5
Chromosome Y completed in 00:00:00.0
Maximum bp shifted for any variant:2
Writing 37097 intervals to database...

Time: 00:13:26.9

- - +etc.) as their alternate allele

Download URLs

ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/xml/ClinVarFullRelease_00-latest.xml.gz

https://ftp.ncbi.nlm.nih.gov/pub/clinvar/xml/clinvar_variation/ClinVarVariationRelease_00-latest.xml.gz

JSON Output

small variants:

"clinvar":[
   {
      "id":"VCV000036581.3",
      "reviewStatus":"reviewed by expert panel",
      "significance":[
         "benign"
      ],
      "refAllele":"G",
      "altAllele":"A",
      "lastUpdatedDate":"2020-03-01",
      "isAlleleSpecific":true
   },
   {
      "id":"RCV000030258.4",
      "variationId":"VCV000036581.3",
      "reviewStatus":"reviewed by expert panel",
      "alleleOrigins":[
         "germline"
      ],
      "refAllele":"G",
      "altAllele":"A",
      "phenotypes":[
         "Lynch syndrome"
      ],
      "medGenIds":[
         "C1333990"
      ],
      "omimIds":[
         "120435"
      ],
      "significance":[
         "benign"
      ],
      "lastUpdatedDate":"2017-05-01",
      "isAlleleSpecific":true
   }
]

large variants:

"clinvar":[
   {
      "chromosome":"1", 
      "begin":629025, 
      "end":8537745, 
      "variantType":"copy_number_loss", 
      "id":"RCV000051993.4", 
      "variationId":"VCV000058242.1", 
      "reviewStatus":"criteria provided, single submitter", 
      "alleleOrigins":[
         "not provided"
      ], 
      "phenotypes":[
         "See cases"
      ], 
      "significance":[
         "pathogenic"
      ], 
      "lastUpdatedDate":"2022-04-21", 
      "pubMedIds":[
         "21844811"
      ]
   },
   {
      "id":"VCV000058242.1",
      "reviewStatus":"criteria provided, single submitter",
      "significance":[
         "pathogenic"
      ],
      "lastUpdatedDate":"2022-04-21"
   },
        ......
]
FieldTypeNotes
idstringClinVar ID
variationIdstringClinVar VCV ID
variantTypestringvariant type
reviewStatusstringsee possible values below
alleleOriginsstring arraysee possible values below
refAllelestring
altAllelestring
phenotypesstring array
medGenIdsstring arrayMedGen IDs
omimIdsstring arrayOMIM IDs
orphanetIdsstring arrayOrphanet IDs
significancestring arraysee possible values below
lastUpdatedDatestringyyyy-MM-dd
pubMedIdsstring arrayPubMed IDs
isAlleleSpecificbooltrue when the current variant alternate allele matches the ClinVar alternate allele

reviewStatus:

alleleOrigins:

significance:

Building the supplementary files

The ClinVar .nsa and .nsi for Illumina Connected Annotations can be built using the SAUtils command's clinvar subcommand.

Source data files

Two input .xml files and a .version file are required in order to build the .nsa and .nsi file. You should have the following files:

ClinVarFullRelease_00-latest.xml.gz     ClinVarVariationRelease_00-latest.xml.gz
ClinVarFullRelease_00-latest.xml.gz.version

The version file is a text file with the follwoing format.

NAME=ClinVar
VERSION=20220505
DATE=2022-05-05
DESCRIPTION=A freely accessible, public archive of reports of the relationships among human variations and phenotypes, with supporting evidence

The help menu for the utility is as follows:

dotnet SAUtils.dll clinvar
---------------------------------------------------------------------------
SAUtils                                             (c) 2022 Illumina, Inc.
Stromberg, Roy, Platzer, Siddiqui, Ouyang, et al                     3.18.1
---------------------------------------------------------------------------

USAGE: dotnet SAUtils.dll clinvar [options]
Creates a supplementary database with ClinVar annotations

OPTIONS:
      --ref, -r <VALUE>      compressed reference sequence file
      --rcv, -i <VALUE>      ClinVar Full release XML file
      --vcv, -c <VALUE>      ClinVar Variation release XML file
      --out, -o <VALUE>      output directory
      --help, -h             displays the help menu
      --version, -v          displays the version

dotnet SAUtils.dll clinvar

Here is a sample execution:

dotnet SAUtils.dll clinvar \\
--ref ~/development/References/7/Homo_sapiens.GRCh38.Nirvana.dat --rcv ClinVarFullRelease_00-latest.xml.gz \\
--vcv ClinVarVariationRelease_00-latest.xml.gz --out ~/development/SupplementaryDatabase/63/GRCh38
---------------------------------------------------------------------------
SAUtils                                             (c) 2022 Illumina, Inc.
Stromberg, Roy, Platzer, Siddiqui, Ouyang, et al                     3.18.1
---------------------------------------------------------------------------

Found 1535677 VCV records
Unknown vcv id:225946 found in RCV000211201.2
Unknown vcv id:225946 found in RCV000211253.2
Unknown vcv id:225946 found in RCV000211375.2
Unknown vcv id:976117 found in RCV001253316.1
Unknown vcv id:1321016 found in RCV001776995.2
3 unknown VCVs found in RCVs.
225946,976117,1321016
0 unknown VCVs found in RCVs.
Chromosome 1 completed in 00:00:15.1
Chromosome 2 completed in 00:00:20.0
Chromosome 3 completed in 00:00:09.7
Chromosome 4 completed in 00:00:05.9
Chromosome 5 completed in 00:00:09.8
Chromosome 6 completed in 00:00:08.3
Chromosome 7 completed in 00:00:08.7
Chromosome 8 completed in 00:00:06.2
Chromosome 9 completed in 00:00:08.6
Chromosome 10 completed in 00:00:07.0
Chromosome 11 completed in 00:00:11.7
Chromosome 12 completed in 00:00:08.0
Chromosome 13 completed in 00:00:06.3
Chromosome 14 completed in 00:00:06.0
Chromosome 15 completed in 00:00:06.6
Chromosome 16 completed in 00:00:10.8
Chromosome 17 completed in 00:00:13.8
Chromosome 18 completed in 00:00:02.9
Chromosome 19 completed in 00:00:08.7
Chromosome 20 completed in 00:00:03.6
Chromosome 21 completed in 00:00:02.4
Chromosome 22 completed in 00:00:03.6
Chromosome MT completed in 00:00:00.2
Chromosome X completed in 00:00:07.5
Chromosome Y completed in 00:00:00.0
Maximum bp shifted for any variant:2
Writing 37097 intervals to database...

Time: 00:13:26.9

+ + \ No newline at end of file diff --git a/3.22/data-sources/cosmic-cancer-gene-census/index.html b/3.22/data-sources/cosmic-cancer-gene-census/index.html index 0dc4663c..b738862d 100644 --- a/3.22/data-sources/cosmic-cancer-gene-census/index.html +++ b/3.22/data-sources/cosmic-cancer-gene-census/index.html @@ -6,13 +6,13 @@ cosmic-cancer-gene-census | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

cosmic-cancer-gene-census

   {
  "name": "PRDM16",
  "hgncId": 14000,
  "ncbiGeneId": "63976",
  "ensemblGeneId": "ENSG00000142611",
  "cosmic": {
    "roleInCancer": [
      "oncogene",
      "fusion"
    ]
  }
}
FieldTypeNotes
roleInCancerstring arrayPossible roles in caner
- - +
Skip to main content
Version: 3.22

cosmic-cancer-gene-census

   {
  "name": "PRDM16",
  "hgncId": 14000,
  "ncbiGeneId": "63976",
  "ensemblGeneId": "ENSG00000142611",
  "cosmic": {
    "roleInCancer": [
      "oncogene",
      "fusion"
    ]
  }
}
FieldTypeNotes
roleInCancerstring arrayPossible roles in caner
+ + \ No newline at end of file diff --git a/3.22/data-sources/cosmic-gene-fusion-json/index.html b/3.22/data-sources/cosmic-gene-fusion-json/index.html index 9ac27bd9..32fe5f92 100644 --- a/3.22/data-sources/cosmic-gene-fusion-json/index.html +++ b/3.22/data-sources/cosmic-gene-fusion-json/index.html @@ -6,13 +6,13 @@ cosmic-gene-fusion-json | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

cosmic-gene-fusion-json

   "cosmicGeneFusions":[
      {
         "id":"COSF881",
         "numSamples":6,
         "geneSymbols":[
            "MYB",
            "NFIB"
         ],
         "hgvsr":"ENST00000341911.5(MYB):r.1_2368::ENST00000397581.2(NFIB):r.2592_3318",
         "histologies":[
            {
               "name":"adenoid cystic carcinoma",
               "numSamples":6
            }
         ],
         "sites":[
            {
               "name":"salivary gland (submandibular)",
               "numSamples":1
            },
            {
               "name":"salivary gland (parotid)",
               "numSamples":1
            },
            {
               "name":"salivary gland (nasal cavity)",
               "numSamples":1
            },
            {
               "name":"breast",
               "numSamples":3
            }
         ],
         "pubMedIds":[
            19841262
         ]
      }
   ]
FieldTypeNotes
idstringCOSMIC fusion ID
numSamplesint
geneSymbolsstring array5' gene & 3' gene
hgvsrstringHGVS RNA translocation fusion notation
histologiescount arrayphenotypic descriptions
sitescount arraytissue types
pubMedIdsint arrayPubMed IDs

Count

FieldTypeNotes
namestringdescription
numSamplesint
- - +
Skip to main content
Version: 3.22

cosmic-gene-fusion-json

   "cosmicGeneFusions":[
      {
         "id":"COSF881",
         "numSamples":6,
         "geneSymbols":[
            "MYB",
            "NFIB"
         ],
         "hgvsr":"ENST00000341911.5(MYB):r.1_2368::ENST00000397581.2(NFIB):r.2592_3318",
         "histologies":[
            {
               "name":"adenoid cystic carcinoma",
               "numSamples":6
            }
         ],
         "sites":[
            {
               "name":"salivary gland (submandibular)",
               "numSamples":1
            },
            {
               "name":"salivary gland (parotid)",
               "numSamples":1
            },
            {
               "name":"salivary gland (nasal cavity)",
               "numSamples":1
            },
            {
               "name":"breast",
               "numSamples":3
            }
         ],
         "pubMedIds":[
            19841262
         ]
      }
   ]
FieldTypeNotes
idstringCOSMIC fusion ID
numSamplesint
geneSymbolsstring array5' gene & 3' gene
hgvsrstringHGVS RNA translocation fusion notation
histologiescount arrayphenotypic descriptions
sitescount arraytissue types
pubMedIdsint arrayPubMed IDs

Count

FieldTypeNotes
namestringdescription
numSamplesint
+ + \ No newline at end of file diff --git a/3.22/data-sources/cosmic-json/index.html b/3.22/data-sources/cosmic-json/index.html index 0c50badb..11209c52 100644 --- a/3.22/data-sources/cosmic-json/index.html +++ b/3.22/data-sources/cosmic-json/index.html @@ -6,13 +6,13 @@ cosmic-json | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

cosmic-json

{
   "id":"COSV58272668",
   "numSamples":8,
   "refAllele":"-",
   "altAllele":"CCT",
   "histologies":[
      {
         "name":"carcinoma (serous carcinoma)",
         "numSamples":2
      },
      {
         "name":"meningioma (fibroblastic)",
         "numSamples":1
      },
      {
         "name":"carcinoma",
         "numSamples":1
      },
      {
         "name":"carcinoma (squamous cell carcinoma)",
         "numSamples":1
      },
      {
         "name":"meningioma (transitional)",
         "numSamples":1
      },
      {
         "name":"carcinoma (adenocarcinoma)",
         "numSamples":1
      },
      {
         "name":"other (neoplasm)",
         "numSamples":1
      }
   ],
   "sites":[
      {
         "name":"ovary",
         "numSamples":2
      },
      {
         "name":"meninges",
         "numSamples":2
      },
      {
         "name":"thyroid",
         "numSamples":2
      },
      {
         "name":"cervix",
         "numSamples":1
      },
      {
         "name":"large intestine (colon)",
         "numSamples":1
      }
   ],
   "pubMedIds":[
      25738363,
      27548314
   ],
   "confirmedSomatic":true,
   "drugResistance":true, /* not in this particular COSMIC variant */
   "isAlleleSpecific":true
}
FieldTypeNotes
idstringCOSMIC Genomic Mutation ID
numSamplesint
refAllelestring
altAllelestring
histologiescount arrayphenotypic descriptions
sitescount arraytissue types
pubMedIdsint arrayPubMed IDs
confirmedSomaticbooltrue when the variant is a confirmed somatic variant
drugResistancebooltrue when the variant has been associated with drug resistance

Count

FieldTypeNotes
namestringdescription
numSamplesint
- - +
Skip to main content
Version: 3.22

cosmic-json

{
   "id":"COSV58272668",
   "numSamples":8,
   "refAllele":"-",
   "altAllele":"CCT",
   "histologies":[
      {
         "name":"carcinoma (serous carcinoma)",
         "numSamples":2
      },
      {
         "name":"meningioma (fibroblastic)",
         "numSamples":1
      },
      {
         "name":"carcinoma",
         "numSamples":1
      },
      {
         "name":"carcinoma (squamous cell carcinoma)",
         "numSamples":1
      },
      {
         "name":"meningioma (transitional)",
         "numSamples":1
      },
      {
         "name":"carcinoma (adenocarcinoma)",
         "numSamples":1
      },
      {
         "name":"other (neoplasm)",
         "numSamples":1
      }
   ],
   "sites":[
      {
         "name":"ovary",
         "numSamples":2
      },
      {
         "name":"meninges",
         "numSamples":2
      },
      {
         "name":"thyroid",
         "numSamples":2
      },
      {
         "name":"cervix",
         "numSamples":1
      },
      {
         "name":"large intestine (colon)",
         "numSamples":1
      }
   ],
   "pubMedIds":[
      25738363,
      27548314
   ],
   "confirmedSomatic":true,
   "drugResistance":true, /* not in this particular COSMIC variant */
   "isAlleleSpecific":true
}
FieldTypeNotes
idstringCOSMIC Genomic Mutation ID
numSamplesint
refAllelestring
altAllelestring
histologiescount arrayphenotypic descriptions
sitescount arraytissue types
pubMedIdsint arrayPubMed IDs
confirmedSomaticbooltrue when the variant is a confirmed somatic variant
drugResistancebooltrue when the variant has been associated with drug resistance

Count

FieldTypeNotes
namestringdescription
numSamplesint
+ + \ No newline at end of file diff --git a/3.22/data-sources/cosmic/index.html b/3.22/data-sources/cosmic/index.html index cd3a5b95..122f6ad9 100644 --- a/3.22/data-sources/cosmic/index.html +++ b/3.22/data-sources/cosmic/index.html @@ -6,8 +6,8 @@ COSMIC | IlluminaConnectedAnnotations - - + +
@@ -21,8 +21,8 @@ site might be skin, but the subtype is foot. Therefore, we will combine the values in the following manner: skin (foot).

COSMIC uses NS to show that a value is empty. If the subtype is NS, we will use the primary histology instead.

Download URL

GRCh37

GRCh38

JSON Output

{
   "id":"COSV58272668",
   "numSamples":8,
   "refAllele":"-",
   "altAllele":"CCT",
   "histologies":[
      {
         "name":"carcinoma (serous carcinoma)",
         "numSamples":2
      },
      {
         "name":"meningioma (fibroblastic)",
         "numSamples":1
      },
      {
         "name":"carcinoma",
         "numSamples":1
      },
      {
         "name":"carcinoma (squamous cell carcinoma)",
         "numSamples":1
      },
      {
         "name":"meningioma (transitional)",
         "numSamples":1
      },
      {
         "name":"carcinoma (adenocarcinoma)",
         "numSamples":1
      },
      {
         "name":"other (neoplasm)",
         "numSamples":1
      }
   ],
   "sites":[
      {
         "name":"ovary",
         "numSamples":2
      },
      {
         "name":"meninges",
         "numSamples":2
      },
      {
         "name":"thyroid",
         "numSamples":2
      },
      {
         "name":"cervix",
         "numSamples":1
      },
      {
         "name":"large intestine (colon)",
         "numSamples":1
      }
   ],
   "pubMedIds":[
      25738363,
      27548314
   ],
   "confirmedSomatic":true,
   "drugResistance":true, /* not in this particular COSMIC variant */
   "isAlleleSpecific":true
}
FieldTypeNotes
idstringCOSMIC Genomic Mutation ID
numSamplesint
refAllelestring
altAllelestring
histologiescount arrayphenotypic descriptions
sitescount arraytissue types
pubMedIdsint arrayPubMed IDs
confirmedSomaticbooltrue when the variant is a confirmed somatic variant
drugResistancebooltrue when the variant has been associated with drug resistance

Count

FieldTypeNotes
namestringdescription
numSamplesint

Gene Fusions

Gene fusions are manually curated from peer reviewed publications by expert COSMIC curators. A comprehensive literature curation is completed for each fusion pair when it is released in the database. Currently COSMIC includes information on fusions involved in solid tumours and leukaemias.

TSV extraction

Example

SAMPLE_ID SAMPLE_NAME PRIMARY_SITE  SITE_SUBTYPE_1  SITE_SUBTYPE_2  SITE_SUBTYPE_3  PRIMARY_HISTOLOGY HISTOLOGY_SUBTYPE_1 HISTOLOGY_SUBTYPE_2 HISTOLOGY_SUBTYPE_3 FUSION_ID TRANSLOCATION_NAME  5'_CHROMOSOME 5'_STRAND 5'_GENE_ID  5'_GENE_NAME  5'_LAST_OBSERVED_EXON 5'_GENOME_START_FROM  5'_GENOME_START_TO  5'_GENOME_STOP_FROM 5'_GENOME_STOP_TO 3'_CHROMOSOME 3'_STRAND 3'_GENE_ID  3'_GENE_NAME  3'_FIRST_OBSERVED_EXON  3'_GENOME_START_FROM  3'_GENOME_START_TO  3'_GENOME_STOP_FROM 3'_GENOME_STOP_TO FUSION_TYPE PUBMED_PMID
749711  HCC1187 breast  NS  NS  NS  carcinoma ductal_carcinoma  NS  NS  665 ENST00000360863.10(RGS22):r.1_3555::ENST00000369518.1(SYCP1):r.2100_3452  8 - 197199  RGS22 22  99981937  99981937  100106116 100106116 1 + 212470  SYCP1_ENST00000369518 24  114944339 114944339 114995367 114995367 Inferred Breakpoint 20033038

Parsing

From the TSV file, we're mainly interested in the following columns:

info

For all the histologies and sites, we replace all the underlines with spaces. salivary_gland would become salivary gland.

Parsing

To create the gene fusion entries in Illumina Connected Annotations, we perform the following on each row in the TSV file:

Aggregating Histologies & Sites

Aggregating Histologies & Sites was previously described in the small variants section.

Known Issues

Known Issues

There are some issues with the HGVS RNA notation:

  • For coding transcripts, HGVS numbering should use CDS coordinates. Right now COSMIC is using cDNA coordinates for all their fusions.

Download URL

GRCh37

GRCh38

JSON Output

   "cosmicGeneFusions":[
      {
         "id":"COSF881",
         "numSamples":6,
         "geneSymbols":[
            "MYB",
            "NFIB"
         ],
         "hgvsr":"ENST00000341911.5(MYB):r.1_2368::ENST00000397581.2(NFIB):r.2592_3318",
         "histologies":[
            {
               "name":"adenoid cystic carcinoma",
               "numSamples":6
            }
         ],
         "sites":[
            {
               "name":"salivary gland (submandibular)",
               "numSamples":1
            },
            {
               "name":"salivary gland (parotid)",
               "numSamples":1
            },
            {
               "name":"salivary gland (nasal cavity)",
               "numSamples":1
            },
            {
               "name":"breast",
               "numSamples":3
            }
         ],
         "pubMedIds":[
            19841262
         ]
      }
   ]
FieldTypeNotes
idstringCOSMIC fusion ID
numSamplesint
geneSymbolsstring array5' gene & 3' gene
hgvsrstringHGVS RNA translocation fusion notation
histologiescount arrayphenotypic descriptions
sitescount arraytissue types
pubMedIdsint arrayPubMed IDs

Count

FieldTypeNotes
namestringdescription
numSamplesint

Cancer Gene Census

TSV Extraction

Example

GENE_NAME       CELL_TYPE       PUBMED_PMID     HALLMARK        IMPACT  DESCRIPTION     CELL_LINE
PRDM16      18496560    role in cancer  oncogene    oncogene
PRDM16      16015645    role in cancer  fusion  fusion

Parsing

To extract information about TSGs and oncogenes, the data based on the "role in cancer" attribute is filtered. For tumor suppressor genes, rows with the value "TSG" and for oncogenes, rows with the value "oncogene" are filtered. -Some genes have both "TSG/oncogene" as their role, which indicates that they can act as both.

Columns

Only following columns are needed to gather required roles in cancer:

Possible Roles in Cancer

While parsing, only following roles in cancer are found:

Parsing Stats

The file contained following number of instances for each role type

Role in cancerTotal Instances
fusion149
TSG195
oncogene181
Total525

Known Issues

None

Download URL

JSON output

   {
  "name": "PRDM16",
  "hgncId": 14000,
  "ncbiGeneId": "63976",
  "ensemblGeneId": "ENSG00000142611",
  "cosmic": {
    "roleInCancer": [
      "oncogene",
      "fusion"
    ]
  }
}
FieldTypeNotes
roleInCancerstring arrayPossible roles in caner
- - +Some genes have both "TSG/oncogene" as their role, which indicates that they can act as both.

Columns

Only following columns are needed to gather required roles in cancer:

Possible Roles in Cancer

While parsing, only following roles in cancer are found:

Parsing Stats

The file contained following number of instances for each role type

Role in cancerTotal Instances
fusion149
TSG195
oncogene181
Total525

Known Issues

None

Download URL

JSON output

   {
  "name": "PRDM16",
  "hgncId": 14000,
  "ncbiGeneId": "63976",
  "ensemblGeneId": "ENSG00000142611",
  "cosmic": {
    "roleInCancer": [
      "oncogene",
      "fusion"
    ]
  }
}
FieldTypeNotes
roleInCancerstring arrayPossible roles in caner
+ + \ No newline at end of file diff --git a/3.22/data-sources/dann-json/index.html b/3.22/data-sources/dann-json/index.html index da4b532c..0ae58550 100644 --- a/3.22/data-sources/dann-json/index.html +++ b/3.22/data-sources/dann-json/index.html @@ -6,13 +6,13 @@ dann-json | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

dann-json

"dannScore": 0.27
FieldTypeNotes
dannScorefloatRange: 0 - 1.0
- - +
Skip to main content
Version: 3.22

dann-json

"dannScore": 0.27
FieldTypeNotes
dannScorefloatRange: 0 - 1.0
+ + \ No newline at end of file diff --git a/3.22/data-sources/dann/index.html b/3.22/data-sources/dann/index.html index b337bc15..406b028a 100644 --- a/3.22/data-sources/dann/index.html +++ b/3.22/data-sources/dann/index.html @@ -6,16 +6,16 @@ DANN | IlluminaConnectedAnnotations - - + +
Skip to main content
Version: 3.22

DANN

Overview

DANN uses the same feature set and training data as CADD (Combined Annotation-Dependent Depletion) to train a deep neural network (DNN). CADD is an algorithm designed to annotate both coding and non-coding variants, and has been shown to outperform other annotation algorithms. DANN improves on CADD (which uses Support Vector Machines (SVMs)) by capturing non-linear relationships by using a deep neural network instead of SVMs. -DANN achieves about a 19% relative reduction in the error rate and about a 14% relative increase in the area under the curve (AUC) metric over CADD’s SVM methodology.

Publication

Quang, Daniel, Yifei Chen, and Xiaohui Xie. DANN: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics 31.5 761-763 (2015). https://doi.org/10.1093/bioinformatics/btu703

TSV File

Example

chr     grch37_pos  ref     alt     DANN
1       10001       T       A       0.16461391399220135
1       10001       T       C       0.4396994049749739
1       10001       T       G       0.38108629377072734
1       10002       A       C       0.36182020272810128
1       10002       A       G       0.44413258111779291
1       10002       A       T       0.16812846819989813

Parsing

From the CSV file, we are interested in all columns:

  • chr
  • grch37_pos
  • ref
  • alt
  • DANN

GRCh38 liftover

The data is not available for GRCh38 on DANN website. We performed a liftover from GRCh37 to GRCh38 using crossmap.

Known Issues

None

Download URL

https://cbcl.ics.uci.edu/public_data/DANN/

JSON Output

"dannScore": 0.27
FieldTypeNotes
dannScorefloatRange: 0 - 1.0
- - +DANN achieves about a 19% relative reduction in the error rate and about a 14% relative increase in the area under the curve (AUC) metric over CADD’s SVM methodology.

Publication

Quang, Daniel, Yifei Chen, and Xiaohui Xie. DANN: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics 31.5 761-763 (2015). https://doi.org/10.1093/bioinformatics/btu703

TSV File

Example

chr     grch37_pos  ref     alt     DANN
1       10001       T       A       0.16461391399220135
1       10001       T       C       0.4396994049749739
1       10001       T       G       0.38108629377072734
1       10002       A       C       0.36182020272810128
1       10002       A       G       0.44413258111779291
1       10002       A       T       0.16812846819989813

Parsing

From the CSV file, we are interested in all columns:

GRCh38 liftover

The data is not available for GRCh38 on DANN website. We performed a liftover from GRCh37 to GRCh38 using crossmap.

Known Issues

None

Download URL

https://cbcl.ics.uci.edu/public_data/DANN/

JSON Output

"dannScore": 0.27
FieldTypeNotes
dannScorefloatRange: 0 - 1.0
+ + \ No newline at end of file diff --git a/3.22/data-sources/dbsnp-json/index.html b/3.22/data-sources/dbsnp-json/index.html index 83e5f0bb..c7f9f0a9 100644 --- a/3.22/data-sources/dbsnp-json/index.html +++ b/3.22/data-sources/dbsnp-json/index.html @@ -6,13 +6,13 @@ dbsnp-json | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

dbsnp-json

"dbsnp":[
   "rs1042821"
]
FieldTypeNotes
dbsnpstring arraydbSNP rsIDs
- - +
Skip to main content
Version: 3.22

dbsnp-json

"dbsnp":[
   "rs1042821"
]
FieldTypeNotes
dbsnpstring arraydbSNP rsIDs
+ + \ No newline at end of file diff --git a/3.22/data-sources/dbsnp/index.html b/3.22/data-sources/dbsnp/index.html index 55baa091..d6a5b9b0 100644 --- a/3.22/data-sources/dbsnp/index.html +++ b/3.22/data-sources/dbsnp/index.html @@ -6,13 +6,13 @@ dbSNP | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

dbSNP

Overview

dbSNP contains human single nucleotide variations, microsatellites, and small-scale insertions and deletions along with publication, population frequency, molecular consequence, and genomic and RefSeq mapping information for both common variations and clinical mutations.

Publication

Sherry, S.T., Ward, M. and Sirotkin, K. (1999) dbSNP—Database for Single Nucleotide Polymorphisms and Other Classes of Minor Genetic Variation. Genome Res., 9, 677–679.

VCF File

Example

#CHROM  POS ID  REF ALT QUAL    FILTER  INFO
1   10177   rs367896724 A   AC  .   .   RS=367896724;RSPOS=10177;dbSNPBuildID=138; \ 
      SSR=0;SAO=0;VP=0x050000020005130026000200;GENEINFO=DDX11L1:100287102;WGT=1; \
      VC=DIV;R5;ASP;G5A;G5;KGPhase3;CAF=0.5747,0.4253;COMMON=1; \
      TOPMED=0.76728147298674821,0.23271852701325178

Parsing

From the VCF file, we're mainly interested in the following:

  • rsID from the ID field
  • CAF from the INFO field

Global allele extraction

The global major and minor alleles are extracted based on the frequency of the alleles provided in the CAF field. The global minor allele frequency is the second highest value of the CAF comma delimited field (ignoring '.' values).

Tie Breaking: Global Major Allele

If there are two candidates for global major and the reference allele is one of them, we prefer the reference allele.

Tie Breaking: Global Minor Allele

If there are two candidates for global minor and the reference allele is one of them, we prefer the other allele. If the reference allele is not involved, they are chosen arbitrarily.

Equal Allele Frequency Example (2 alleles)

chr1    100 A   C   CAF=0.5,0.5

We will select A to be the global major allele and C to be the global minor allele.

Equal Allele Frequency Example (3 alleles)

chr1    100 A   C,T CAF=0.33,0.33,0.33

We will select A to be the global major allele and either C or T is chosen (arbitrarily) to be the global minor allele.

Equal Allele Frequency in Alternate Alleles

chr1    100 A   C,T CAF=0.2,0.4,0.4

We will select C or T to be arbitrarily assigned to be the global major or global minor allele.

Equal Allele Frequency Between Reference & Alternate Allele

chr1    100 A   C,T CAF=0.2,0.2,0.6

We will select T to be the global major allele and C to be the global minor allele.

Known Issues

Known Issues

If there are multiple entries with different CAF values for the same allele, we use the first CAF value.

Download URL

https://ftp.ncbi.nih.gov/snp/organisms/

JSON Output

"dbsnp":[
   "rs1042821"
]
FieldTypeNotes
dbsnpstring arraydbSNP rsIDs
- - +
Skip to main content
Version: 3.22

dbSNP

Overview

dbSNP contains human single nucleotide variations, microsatellites, and small-scale insertions and deletions along with publication, population frequency, molecular consequence, and genomic and RefSeq mapping information for both common variations and clinical mutations.

Publication

Sherry, S.T., Ward, M. and Sirotkin, K. (1999) dbSNP—Database for Single Nucleotide Polymorphisms and Other Classes of Minor Genetic Variation. Genome Res., 9, 677–679.

VCF File

Example

#CHROM  POS ID  REF ALT QUAL    FILTER  INFO
1   10177   rs367896724 A   AC  .   .   RS=367896724;RSPOS=10177;dbSNPBuildID=138; \ 
      SSR=0;SAO=0;VP=0x050000020005130026000200;GENEINFO=DDX11L1:100287102;WGT=1; \
      VC=DIV;R5;ASP;G5A;G5;KGPhase3;CAF=0.5747,0.4253;COMMON=1; \
      TOPMED=0.76728147298674821,0.23271852701325178

Parsing

From the VCF file, we're mainly interested in the following:

  • rsID from the ID field
  • CAF from the INFO field

Global allele extraction

The global major and minor alleles are extracted based on the frequency of the alleles provided in the CAF field. The global minor allele frequency is the second highest value of the CAF comma delimited field (ignoring '.' values).

Tie Breaking: Global Major Allele

If there are two candidates for global major and the reference allele is one of them, we prefer the reference allele.

Tie Breaking: Global Minor Allele

If there are two candidates for global minor and the reference allele is one of them, we prefer the other allele. If the reference allele is not involved, they are chosen arbitrarily.

Equal Allele Frequency Example (2 alleles)

chr1    100 A   C   CAF=0.5,0.5

We will select A to be the global major allele and C to be the global minor allele.

Equal Allele Frequency Example (3 alleles)

chr1    100 A   C,T CAF=0.33,0.33,0.33

We will select A to be the global major allele and either C or T is chosen (arbitrarily) to be the global minor allele.

Equal Allele Frequency in Alternate Alleles

chr1    100 A   C,T CAF=0.2,0.4,0.4

We will select C or T to be arbitrarily assigned to be the global major or global minor allele.

Equal Allele Frequency Between Reference & Alternate Allele

chr1    100 A   C,T CAF=0.2,0.2,0.6

We will select T to be the global major allele and C to be the global minor allele.

Known Issues

Known Issues

If there are multiple entries with different CAF values for the same allele, we use the first CAF value.

Download URL

https://ftp.ncbi.nih.gov/snp/organisms/

JSON Output

"dbsnp":[
   "rs1042821"
]
FieldTypeNotes
dbsnpstring arraydbSNP rsIDs
+ + \ No newline at end of file diff --git a/3.22/data-sources/decipher-json/index.html b/3.22/data-sources/decipher-json/index.html index 455fe8da..a10f51ae 100644 --- a/3.22/data-sources/decipher-json/index.html +++ b/3.22/data-sources/decipher-json/index.html @@ -6,13 +6,13 @@ decipher-json | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

decipher-json

"decipher":[
  {
    "chromosome":"1",
    "begin":13516,
    "end":91073,
    "numDeletions":27,
    "deletionFrequency":0.675,
    "numDuplications":27,
    "duplicationFrequency":0.675,
    "sampleSize":40,
    "reciprocalOverlap": 0.27555,
    "annotationOverlap": 0.5901
  }
],
FieldTypeNotes
chromosomeintEnsembl-style chromosome names
beginint1-based position
endint1-based position
numDeletionsint# of observed deletions
deletionFrequencyfloatdeletion frequency
numDuplicationsint# of observed duplications
duplicationFrequencyfloatduplication frequency
sampleSizeinttotal # of samples
reciprocalOverlapfloatRange: 0 - 1. E.g. 0.57 would indicate a 57% reciprocal overlap
annotationOverlapfloatRange: 0 - 1. E.g. 0.57 would indicate a 57% annotation overlap
- - +
Skip to main content
Version: 3.22

decipher-json

"decipher":[
  {
    "chromosome":"1",
    "begin":13516,
    "end":91073,
    "numDeletions":27,
    "deletionFrequency":0.675,
    "numDuplications":27,
    "duplicationFrequency":0.675,
    "sampleSize":40,
    "reciprocalOverlap": 0.27555,
    "annotationOverlap": 0.5901
  }
],
FieldTypeNotes
chromosomeintEnsembl-style chromosome names
beginint1-based position
endint1-based position
numDeletionsint# of observed deletions
deletionFrequencyfloatdeletion frequency
numDuplicationsint# of observed duplications
duplicationFrequencyfloatduplication frequency
sampleSizeinttotal # of samples
reciprocalOverlapfloatRange: 0 - 1. E.g. 0.57 would indicate a 57% reciprocal overlap
annotationOverlapfloatRange: 0 - 1. E.g. 0.57 would indicate a 57% annotation overlap
+ + \ No newline at end of file diff --git a/3.22/data-sources/decipher/index.html b/3.22/data-sources/decipher/index.html index fe5d1ddf..e8504fd9 100644 --- a/3.22/data-sources/decipher/index.html +++ b/3.22/data-sources/decipher/index.html @@ -6,14 +6,14 @@ DECIPHER | IlluminaConnectedAnnotations - - + +
Skip to main content
Version: 3.22

DECIPHER

Overview

DECIPHER (DatabasE of genomiC varIation and Phenotype in Humans using Ensembl Resources) is an interactive web-based database which incorporates a suite of tools designed to aid the interpretation of genomic variants.

DECIPHER enhances clinical diagnosis by retrieving information from a variety of bioinformatics resources relevant to the variant found in the patient. The patient's variant is displayed in the context of both normal variation and pathogenic variation reported at that locus thereby facilitating interpretation.

Publication

DECIPHER: Database of Chromosomal Imbalance and Phenotype in Humans using Ensembl Resources. Firth, H.V. et al., 2009. Am.J.Hum.Genet 84, 524-533 (DOI: dx.doi.org/10/1016/j.ajhg.2009.03.010)

TSV Extraction

#population_cnv_id  chr start   end deletion_observations   deletion_frequency  deletion_standard_error duplication_observations    duplication_frequency   duplication_standard_error  observations    frequency   standard_error  type    sample_size study
1   1   10529   177368  0   0   1   3   0.075   0.555277708 3   0.075   0.555277708 1   40  42M calls
2   1   13516   91073   0   0   1   27  0.675   0.109713431 27  0.675   0.109713431 1   40  42M calls
3   1   18888   35451   0   0   1   2   0.002366864 0.706269473 2   0.002366864 0.706269473 1   845 DDD

Parsing

We parse the DECIPHER tsv file and extract the following columns:

  • chr
  • start
  • end
  • deletion_observations
  • deletion_frequency
  • duplication_observations
  • duplication_frequency
  • sample_size

Download URL

https://www.deciphergenomics.org/files/downloads/population_cnv_grch38.txt.gz -https://www.deciphergenomics.org/files/downloads/population_cnv_grch37.txt.gz

JSON output

"decipher":[
  {
    "chromosome":"1",
    "begin":13516,
    "end":91073,
    "numDeletions":27,
    "deletionFrequency":0.675,
    "numDuplications":27,
    "duplicationFrequency":0.675,
    "sampleSize":40,
    "reciprocalOverlap": 0.27555,
    "annotationOverlap": 0.5901
  }
],
FieldTypeNotes
chromosomeintEnsembl-style chromosome names
beginint1-based position
endint1-based position
numDeletionsint# of observed deletions
deletionFrequencyfloatdeletion frequency
numDuplicationsint# of observed duplications
duplicationFrequencyfloatduplication frequency
sampleSizeinttotal # of samples
reciprocalOverlapfloatRange: 0 - 1. E.g. 0.57 would indicate a 57% reciprocal overlap
annotationOverlapfloatRange: 0 - 1. E.g. 0.57 would indicate a 57% annotation overlap
- - +https://www.deciphergenomics.org/files/downloads/population_cnv_grch37.txt.gz

JSON output

"decipher":[
  {
    "chromosome":"1",
    "begin":13516,
    "end":91073,
    "numDeletions":27,
    "deletionFrequency":0.675,
    "numDuplications":27,
    "duplicationFrequency":0.675,
    "sampleSize":40,
    "reciprocalOverlap": 0.27555,
    "annotationOverlap": 0.5901
  }
],
FieldTypeNotes
chromosomeintEnsembl-style chromosome names
beginint1-based position
endint1-based position
numDeletionsint# of observed deletions
deletionFrequencyfloatdeletion frequency
numDuplicationsint# of observed duplications
duplicationFrequencyfloatduplication frequency
sampleSizeinttotal # of samples
reciprocalOverlapfloatRange: 0 - 1. E.g. 0.57 would indicate a 57% reciprocal overlap
annotationOverlapfloatRange: 0 - 1. E.g. 0.57 would indicate a 57% annotation overlap
+ + \ No newline at end of file diff --git a/3.22/data-sources/fusioncatcher-json/index.html b/3.22/data-sources/fusioncatcher-json/index.html index 978e2bf8..e3010745 100644 --- a/3.22/data-sources/fusioncatcher-json/index.html +++ b/3.22/data-sources/fusioncatcher-json/index.html @@ -6,13 +6,13 @@ fusioncatcher-json | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

fusioncatcher-json

   "fusionCatcher":[
      {
         "genes":{
            "first":{
               "hgnc":"ETV6",
               "isOncogene":true
            },
            "second":{
               "hgnc":"RUNX1"
            },
            "isParalogPair":true,
            "isPseudogenePair":true,
            "isReadthrough":true
         },
         "germlineSources":[
            "1000 Genomes Project"
         ],
         "somaticSources":[
            "COSMIC",
            "TCGA oesophageal carcinomas"
         ]
      }
   ]
FieldTypeNotes
genesgenes object5' gene & 3' gene
germlineSourcesstring arraymatches in known germline data sources
somaticSourcesstring arraymatches in known somatic data sources

genes

FieldTypeNotes
firstgene object5' gene
secondgene object3' gene
isParalogPairbooltrue when both genes are paralogs for each other
isPseudogenePairbooltrue when both genes are pseudogenes for each other
isReadthroughbooltrue when this fusion gene is a readthrough event (both are on the same strand and there are no genes between them)

gene

FieldTypeNotes
hgncstringgene symbol. e.g. MSH6
isOncogenebooltrue when this gene is an oncogene
- - +
Skip to main content
Version: 3.22

fusioncatcher-json

   "fusionCatcher":[
      {
         "genes":{
            "first":{
               "hgnc":"ETV6",
               "isOncogene":true
            },
            "second":{
               "hgnc":"RUNX1"
            },
            "isParalogPair":true,
            "isPseudogenePair":true,
            "isReadthrough":true
         },
         "germlineSources":[
            "1000 Genomes Project"
         ],
         "somaticSources":[
            "COSMIC",
            "TCGA oesophageal carcinomas"
         ]
      }
   ]
FieldTypeNotes
genesgenes object5' gene & 3' gene
germlineSourcesstring arraymatches in known germline data sources
somaticSourcesstring arraymatches in known somatic data sources

genes

FieldTypeNotes
firstgene object5' gene
secondgene object3' gene
isParalogPairbooltrue when both genes are paralogs for each other
isPseudogenePairbooltrue when both genes are pseudogenes for each other
isReadthroughbooltrue when this fusion gene is a readthrough event (both are on the same strand and there are no genes between them)

gene

FieldTypeNotes
hgncstringgene symbol. e.g. MSH6
isOncogenebooltrue when this gene is an oncogene
+ + \ No newline at end of file diff --git a/3.22/data-sources/fusioncatcher/index.html b/3.22/data-sources/fusioncatcher/index.html index 38759f8f..a49b1fff 100644 --- a/3.22/data-sources/fusioncatcher/index.html +++ b/3.22/data-sources/fusioncatcher/index.html @@ -6,13 +6,13 @@ FusionCatcher | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

FusionCatcher

Overview

FusionCatcher is a well-known tool that searches for somatic novel/known fusion genes, translocations, and/or chimeras in RNA-seq data. While FusionCatcher itself is not part of Illumina Connected Annotations, we have included a subset of their genomic databases in Illumina Connected Annotations.

Publication

Daniel Nicorici, Mihaela Şatalan, Henrik Edgren, Sara Kangaspeska, Astrid Murumägi, Olli Kallioniemi, Sami Virtanen, Olavi Kilkku. (2014) FusionCatcher – a tool for finding somatic fusion genes in paired-end RNA-sequencing data. bioRxiv 011650

Supported Data Sources

Oncogenes

The following data sources are aggregated and used to populate the isOncogene field in the gene JSON object:

DescriptionReferenceDataFusionCatcher filename
Bushmanbushmanlab.orgcancer_genes.txt
ONGENEJGGbioinfo-minzhao.orgoncogenes_more.txt
UniProt tumor genesNARuniprot.orgtumor_genes.txt

Germline

Illumina Connected Annotations labelReferenceDataFusionCatcher filename
1000 Genomes ProjectPLOS ONE1000genomes.txt
Healthy (strong support)banned.txt
Illumina Body Map 2.0EBIbodymap2.txt
CACGGenomicscacg.txt
ConjoinGPLOS ONEconjoing.txt
Healthy prefrontal cortexBMC Medical GenomicsNCBI GEOcortex.txt
Duplicated Genes DatabasePLOS ONEgenouest.orgdgd.txt
GTEx healthy tissuesgtexportal.orggtex.txt
Healthyhealthy.txt
Human Protein AtlasMCPEBIhpa.txt
Babiceanu non-cancer tissuesNARNARnon-cancer_tissues.txt
non-tumor cell linesnon-tumor_cells.txt
TumorFusions normalNARNARtcga-normal.txt

Somatic

Illumina Connected Annotations labelReferenceDataFusionCatcher filename
Alaei-Mahabadi 18 cancersPNAS18cancers.txt
DepMap CCLEdepmap.orgccle.txt
CCLE KlijnNature BiotechnologyNature Biotechnologyccle2.txt
CCLE VellichirammalMolecular Therapy Nucleic Acidsccle3.txt
Cancer Genome ProjectCOSMICcgp.txt
ChimerKB 4.0NARkobic.re.krchimerdb4kb.txt
ChimerPub 4.0NARkobic.re.krchimerdb4pub.txt
ChimerSeq 4.0NARkobic.re.krchimerdb4seq.txt
COSMICNARCOSMICcosmic.txt
Bao gliomasGenome Researchgliomas.txt
Knownknown.txt
Mitelman DBISB-CGCGoogle Cloudmitelman.txt
TCGA oesophageal carcinomasNatureoesophagus.txt
Bailey pancreatic cancersNatureNaturepancreases.txt
PCAWGCellICGCpcawg.txt
Robinson prostate cancersCellCellprostate_cancer.txt
TCGAcancer.govtcga.txt
TumorFusions tumorNARNARtcga-cancer.txt
TCGA GaoCellCelltcga2.txt
TCGA VellichirammalMolecular Therapy Nucleic Acidstcga3.txt
TICdbBMC Genomicsunav.eduticdb.txt

Gene Pair TSV File

Most of the data files in FusionCatcher are two-column TSV files containing the Ensembl gene IDs that are paired together.

Example

Here are the first few lines of the 1000genomes.txt file:

ENSG00000006210 ENSG00000102962
ENSG00000006652 ENSG00000181016
ENSG00000014138 ENSG00000149798
ENSG00000026297 ENSG00000071242
ENSG00000035499 ENSG00000155959
ENSG00000055211 ENSG00000131013
ENSG00000055332 ENSG00000179915
ENSG00000062485 ENSG00000257727
ENSG00000065978 ENSG00000166501
ENSG00000066044 ENSG00000104980

Parsing

In Illumina Connected Annotations, we will only import a gene pair if both Ensembl gene IDs are recognized from either our GRCh37 or GRCh38 cache files.

Gene TSV File

Some of the data files are single-column files containing Ensembl gene IDs. This is commonly used in the data files representing oncogene data sources.

Example

Here are the first few lines of the oncogenes_more.txt file:

ENSG00000000938
ENSG00000003402
ENSG00000005469
ENSG00000005884
ENSG00000006128
ENSG00000006453
ENSG00000006468
ENSG00000007350
ENSG00000008294
ENSG00000008952

Parsing

Known Issues

Known Issues

FusionCatcher also uses creates custom Ensembl genes (e.g. ENSG09000000002) to handle missing Ensembl genes. Illumina Connected Annotations will ignore these entries since we only include the gene IDs that are currently recognized by Illumina Connected Annotations.

I suspect that these were originally RefSeq genes and if so, we can support those directly in Illumina Connected Annotations in the future.

Download URL

https://sourceforge.net/projects/fusioncatcher/files/data

JSON Output

   "fusionCatcher":[
      {
         "genes":{
            "first":{
               "hgnc":"ETV6",
               "isOncogene":true
            },
            "second":{
               "hgnc":"RUNX1"
            },
            "isParalogPair":true,
            "isPseudogenePair":true,
            "isReadthrough":true
         },
         "germlineSources":[
            "1000 Genomes Project"
         ],
         "somaticSources":[
            "COSMIC",
            "TCGA oesophageal carcinomas"
         ]
      }
   ]
FieldTypeNotes
genesgenes object5' gene & 3' gene
germlineSourcesstring arraymatches in known germline data sources
somaticSourcesstring arraymatches in known somatic data sources

genes

FieldTypeNotes
firstgene object5' gene
secondgene object3' gene
isParalogPairbooltrue when both genes are paralogs for each other
isPseudogenePairbooltrue when both genes are pseudogenes for each other
isReadthroughbooltrue when this fusion gene is a readthrough event (both are on the same strand and there are no genes between them)

gene

FieldTypeNotes
hgncstringgene symbol. e.g. MSH6
isOncogenebooltrue when this gene is an oncogene
- - +
Skip to main content
Version: 3.22

FusionCatcher

Overview

FusionCatcher is a well-known tool that searches for somatic novel/known fusion genes, translocations, and/or chimeras in RNA-seq data. While FusionCatcher itself is not part of Illumina Connected Annotations, we have included a subset of their genomic databases in Illumina Connected Annotations.

Publication

Daniel Nicorici, Mihaela Şatalan, Henrik Edgren, Sara Kangaspeska, Astrid Murumägi, Olli Kallioniemi, Sami Virtanen, Olavi Kilkku. (2014) FusionCatcher – a tool for finding somatic fusion genes in paired-end RNA-sequencing data. bioRxiv 011650

Supported Data Sources

Oncogenes

The following data sources are aggregated and used to populate the isOncogene field in the gene JSON object:

DescriptionReferenceDataFusionCatcher filename
Bushmanbushmanlab.orgcancer_genes.txt
ONGENEJGGbioinfo-minzhao.orgoncogenes_more.txt
UniProt tumor genesNARuniprot.orgtumor_genes.txt

Germline

Illumina Connected Annotations labelReferenceDataFusionCatcher filename
1000 Genomes ProjectPLOS ONE1000genomes.txt
Healthy (strong support)banned.txt
Illumina Body Map 2.0EBIbodymap2.txt
CACGGenomicscacg.txt
ConjoinGPLOS ONEconjoing.txt
Healthy prefrontal cortexBMC Medical GenomicsNCBI GEOcortex.txt
Duplicated Genes DatabasePLOS ONEgenouest.orgdgd.txt
GTEx healthy tissuesgtexportal.orggtex.txt
Healthyhealthy.txt
Human Protein AtlasMCPEBIhpa.txt
Babiceanu non-cancer tissuesNARNARnon-cancer_tissues.txt
non-tumor cell linesnon-tumor_cells.txt
TumorFusions normalNARNARtcga-normal.txt

Somatic

Illumina Connected Annotations labelReferenceDataFusionCatcher filename
Alaei-Mahabadi 18 cancersPNAS18cancers.txt
DepMap CCLEdepmap.orgccle.txt
CCLE KlijnNature BiotechnologyNature Biotechnologyccle2.txt
CCLE VellichirammalMolecular Therapy Nucleic Acidsccle3.txt
Cancer Genome ProjectCOSMICcgp.txt
ChimerKB 4.0NARkobic.re.krchimerdb4kb.txt
ChimerPub 4.0NARkobic.re.krchimerdb4pub.txt
ChimerSeq 4.0NARkobic.re.krchimerdb4seq.txt
COSMICNARCOSMICcosmic.txt
Bao gliomasGenome Researchgliomas.txt
Knownknown.txt
Mitelman DBISB-CGCGoogle Cloudmitelman.txt
TCGA oesophageal carcinomasNatureoesophagus.txt
Bailey pancreatic cancersNatureNaturepancreases.txt
PCAWGCellICGCpcawg.txt
Robinson prostate cancersCellCellprostate_cancer.txt
TCGAcancer.govtcga.txt
TumorFusions tumorNARNARtcga-cancer.txt
TCGA GaoCellCelltcga2.txt
TCGA VellichirammalMolecular Therapy Nucleic Acidstcga3.txt
TICdbBMC Genomicsunav.eduticdb.txt

Gene Pair TSV File

Most of the data files in FusionCatcher are two-column TSV files containing the Ensembl gene IDs that are paired together.

Example

Here are the first few lines of the 1000genomes.txt file:

ENSG00000006210 ENSG00000102962
ENSG00000006652 ENSG00000181016
ENSG00000014138 ENSG00000149798
ENSG00000026297 ENSG00000071242
ENSG00000035499 ENSG00000155959
ENSG00000055211 ENSG00000131013
ENSG00000055332 ENSG00000179915
ENSG00000062485 ENSG00000257727
ENSG00000065978 ENSG00000166501
ENSG00000066044 ENSG00000104980

Parsing

In Illumina Connected Annotations, we will only import a gene pair if both Ensembl gene IDs are recognized from either our GRCh37 or GRCh38 cache files.

Gene TSV File

Some of the data files are single-column files containing Ensembl gene IDs. This is commonly used in the data files representing oncogene data sources.

Example

Here are the first few lines of the oncogenes_more.txt file:

ENSG00000000938
ENSG00000003402
ENSG00000005469
ENSG00000005884
ENSG00000006128
ENSG00000006453
ENSG00000006468
ENSG00000007350
ENSG00000008294
ENSG00000008952

Parsing

Known Issues

Known Issues

FusionCatcher also uses creates custom Ensembl genes (e.g. ENSG09000000002) to handle missing Ensembl genes. Illumina Connected Annotations will ignore these entries since we only include the gene IDs that are currently recognized by Illumina Connected Annotations.

I suspect that these were originally RefSeq genes and if so, we can support those directly in Illumina Connected Annotations in the future.

Download URL

https://sourceforge.net/projects/fusioncatcher/files/data

JSON Output

   "fusionCatcher":[
      {
         "genes":{
            "first":{
               "hgnc":"ETV6",
               "isOncogene":true
            },
            "second":{
               "hgnc":"RUNX1"
            },
            "isParalogPair":true,
            "isPseudogenePair":true,
            "isReadthrough":true
         },
         "germlineSources":[
            "1000 Genomes Project"
         ],
         "somaticSources":[
            "COSMIC",
            "TCGA oesophageal carcinomas"
         ]
      }
   ]
FieldTypeNotes
genesgenes object5' gene & 3' gene
germlineSourcesstring arraymatches in known germline data sources
somaticSourcesstring arraymatches in known somatic data sources

genes

FieldTypeNotes
firstgene object5' gene
secondgene object3' gene
isParalogPairbooltrue when both genes are paralogs for each other
isPseudogenePairbooltrue when both genes are pseudogenes for each other
isReadthroughbooltrue when this fusion gene is a readthrough event (both are on the same strand and there are no genes between them)

gene

FieldTypeNotes
hgncstringgene symbol. e.g. MSH6
isOncogenebooltrue when this gene is an oncogene
+ + \ No newline at end of file diff --git a/3.22/data-sources/gerp-json/index.html b/3.22/data-sources/gerp-json/index.html index 9d56c3ee..bccdb2cb 100644 --- a/3.22/data-sources/gerp-json/index.html +++ b/3.22/data-sources/gerp-json/index.html @@ -6,13 +6,13 @@ gerp-json | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

gerp-json

"gerpScore": 1.27
FieldTypeNotes
gerpScorefloatRange: -∞ to +∞
- - +
Skip to main content
Version: 3.22

gerp-json

"gerpScore": 1.27
FieldTypeNotes
gerpScorefloatRange: -∞ to +∞
+ + \ No newline at end of file diff --git a/3.22/data-sources/gerp/index.html b/3.22/data-sources/gerp/index.html index bc72ddbf..6563f104 100644 --- a/3.22/data-sources/gerp/index.html +++ b/3.22/data-sources/gerp/index.html @@ -6,15 +6,15 @@ GERP | IlluminaConnectedAnnotations - - + +
Skip to main content
Version: 3.22

GERP

Overview

GERP identifies constrained elements in multiple alignments by quantifying substitution deficits. These deficits represent substitutions that would have occurred if the element were neutral DNA, but did not occur because the element has been under functional constraint (Rejected Substitutions). -Illumina Connected Annotations uses GERP++ which is based on a significantly faster and more statistically robust maximum likelihood estimation procedure to compute expected rates of evolution.

Publication

Davydov, Eugene V., et al. "Identifying a high fraction of the human genome to be under selective constraint using GERP++." PLoS computational biology 6.12 e1001025 (2010). https://doi.org/10.1371/journal.pcbi.1001025

Source Files

Example GRCh37

GRCh37 file is a TSV format

chr     position    GERP
1       12177       0.83
1       12178       -0.206
1       12179       -0.492
1       12180       -1.66
1       12181       0.83
1       12182       0.83
1       12183       -0.417
1       12184       0.83

Example GRCh38

GRCh38 file is a lift-over BED format

chr     pos_start   pos_end     GERP
1       12646       12647       0.298
1       12647       12648       2.63
1       12648       12649       1.87
1       12649       12650       0.252
1       12650       12651       -2.06
1       12651       12652       2.61
1       12652       12653       3.97

Parsing

From the CSV file, we are interested in columns:

  • chr
  • position
  • GERP

Known Issues

None

Download URL

GRCh37

http://mendel.stanford.edu/SidowLab/downloads/gerp/index.html

GRCh38

The data is not available for GRCh38 on GERP++ website, and was obtained from https://personal.broadinstitute.org/konradk/loftee_data/GRCh38/

JSON Output

"gerpScore": 1.27
FieldTypeNotes
gerpScorefloatRange: -∞ to +∞
- - +Illumina Connected Annotations uses GERP++ which is based on a significantly faster and more statistically robust maximum likelihood estimation procedure to compute expected rates of evolution.

Publication

Davydov, Eugene V., et al. "Identifying a high fraction of the human genome to be under selective constraint using GERP++." PLoS computational biology 6.12 e1001025 (2010). https://doi.org/10.1371/journal.pcbi.1001025

Source Files

Example GRCh37

GRCh37 file is a TSV format

chr     position    GERP
1       12177       0.83
1       12178       -0.206
1       12179       -0.492
1       12180       -1.66
1       12181       0.83
1       12182       0.83
1       12183       -0.417
1       12184       0.83

Example GRCh38

GRCh38 file is a lift-over BED format

chr     pos_start   pos_end     GERP
1       12646       12647       0.298
1       12647       12648       2.63
1       12648       12649       1.87
1       12649       12650       0.252
1       12650       12651       -2.06
1       12651       12652       2.61
1       12652       12653       3.97

Parsing

From the CSV file, we are interested in columns:

Known Issues

None

Download URL

GRCh37

http://mendel.stanford.edu/SidowLab/downloads/gerp/index.html

GRCh38

The data is not available for GRCh38 on GERP++ website, and was obtained from https://personal.broadinstitute.org/konradk/loftee_data/GRCh38/

JSON Output

"gerpScore": 1.27
FieldTypeNotes
gerpScorefloatRange: -∞ to +∞
+ + \ No newline at end of file diff --git a/3.22/data-sources/gme-json/index.html b/3.22/data-sources/gme-json/index.html index dde54d61..cddc490c 100644 --- a/3.22/data-sources/gme-json/index.html +++ b/3.22/data-sources/gme-json/index.html @@ -6,13 +6,13 @@ gme-json | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

gme-json

"gmeVariome":{
   "allAc":10,
   "allAn":202,
   "allAf":0.049504,
   "failedFilter":true
}
FieldTypeNotes
allAcintGME allele count
allAnintGME allele number
allAffloatGME allele frequency
failedFilterboolTrue if this variant failed any filters
- - +
Skip to main content
Version: 3.22

gme-json

"gmeVariome":{
   "allAc":10,
   "allAn":202,
   "allAf":0.049504,
   "failedFilter":true
}
FieldTypeNotes
allAcintGME allele count
allAnintGME allele number
allAffloatGME allele frequency
failedFilterboolTrue if this variant failed any filters
+ + \ No newline at end of file diff --git a/3.22/data-sources/gme/index.html b/3.22/data-sources/gme/index.html index f6f42140..96cad7d8 100644 --- a/3.22/data-sources/gme/index.html +++ b/3.22/data-sources/gme/index.html @@ -6,13 +6,13 @@ GME Variome | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

GME Variome

Overview

The Greater Middle East (GME) Variome Project is aimed at generating a coding base reference for the countries found in the Greater Middle East. Illumina Connected Annotations presents variant frequencies for the Greater Middle Eastern population.

Publication

Scott, E. M., Halees, A., Itan, Y., Spencer, E. G., He, Y., Azab, M. A., Gabriel, S. B., Belkadi, A., Boisson, B., Abel, L., Clark, A. G., Greater Middle East Variome Consortium, Alkuraya, F. S., Casanova, J. L., & Gleeson, J. G. (2016). Characterization of Greater Middle Eastern genetic variation for enhanced disease gene discovery. Nature genetics, 48(9), 1071–1076. https://doi.org/10.1038/ng.3592

TSV Extraction

chrom   pos     ref     alt     AA      filter  FunctionGVS     geneFunction    Gene    GeneID  SIFT_pred       GERP++  AF      GME_GC  GME_AC  GME_AF  NWA     NEA     AP      Israel  SD      TP      CA      FunctionGVS_new Priority        Polyphen2_HVAR_pred     LRT_pred        MutationTaster_pred     rsid    OMIM_MIM        OMIM_Disease    AA_AC   EA_AC   rsid_link       position_link
1       69134   A       G       A       VQSRTrancheSNP99.90to100.00     nonsynonymous_SNV       exonic  OR4F5   79501   T       2.31            96:0:5  10,192  0.04950495049504951     4:0:0   59:0:2  12:0:0  0:0:0   6:0:0   9:0:2   13:0:2  nonsynonymous_SNV       MODERATE        B       N       N       none    -       -       none    none    -       http://genome.ucsc.edu/cgi-bin/hgTracks?db=hg19&org=human&position=chr1%3A69134-69133
1       69270   A       G       A       PASS    synonymous_SNV  exonic  OR4F5   79501   .       .               93:38:240       518,224 0.6981132075471698      5:5:11  63:30:86        12:5:28 1:0:2   2:2:18  7:3:46  7:2:52  synonymous_SNV  LOW     .       .       .       rs201219564     -       -       none    none    http://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?searchType=adhoc_search&type=rs&rs=rs201219564     http://genome.ucsc.edu/cgi-bin/hgTracks?db=hg19&org=human&position=chr1%3A69270-69269
1       69428   T       G       T       PASS    nonsynonymous_SNV       exonic  OR4F5   79501   D       0.891           676:44:15       74,1396 0.050340136054421766    43:0:2  313:16:10       88:7:3  6:0:0   44:8:0  102:9:0 102:4:2 nonsynonymous_SNV       MODERATE        D       N       N       rs140739101     -       -       14,3808 313,6535        http://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?searchType=adhoc_search&type=rs&rs=rs140739101     http://genome.ucsc.edu/cgi-bin/hgTracks?db=hg19&org=human&position=chr1%3A69428-69427

Parsing

We parse the GME tsv file and extract the following columns:

  • chrom
  • pos
  • ref
  • alt
  • filter
  • GME_AC
  • GME_AF

GRCh37 liftover

The data is not available for GRCh38 on GME website. We performed a liftover from GRCh37 to GRCh38 using CrossMap.

Download URL

http://igm.ucsd.edu/gme/download.shtml

JSON output

"gmeVariome":{
   "allAc":10,
   "allAn":202,
   "allAf":0.049504,
   "failedFilter":true
}
FieldTypeNotes
allAcintGME allele count
allAnintGME allele number
allAffloatGME allele frequency
failedFilterboolTrue if this variant failed any filters
- - +
Skip to main content
Version: 3.22

GME Variome

Overview

The Greater Middle East (GME) Variome Project is aimed at generating a coding base reference for the countries found in the Greater Middle East. Illumina Connected Annotations presents variant frequencies for the Greater Middle Eastern population.

Publication

Scott, E. M., Halees, A., Itan, Y., Spencer, E. G., He, Y., Azab, M. A., Gabriel, S. B., Belkadi, A., Boisson, B., Abel, L., Clark, A. G., Greater Middle East Variome Consortium, Alkuraya, F. S., Casanova, J. L., & Gleeson, J. G. (2016). Characterization of Greater Middle Eastern genetic variation for enhanced disease gene discovery. Nature genetics, 48(9), 1071–1076. https://doi.org/10.1038/ng.3592

TSV Extraction

chrom   pos     ref     alt     AA      filter  FunctionGVS     geneFunction    Gene    GeneID  SIFT_pred       GERP++  AF      GME_GC  GME_AC  GME_AF  NWA     NEA     AP      Israel  SD      TP      CA      FunctionGVS_new Priority        Polyphen2_HVAR_pred     LRT_pred        MutationTaster_pred     rsid    OMIM_MIM        OMIM_Disease    AA_AC   EA_AC   rsid_link       position_link
1       69134   A       G       A       VQSRTrancheSNP99.90to100.00     nonsynonymous_SNV       exonic  OR4F5   79501   T       2.31            96:0:5  10,192  0.04950495049504951     4:0:0   59:0:2  12:0:0  0:0:0   6:0:0   9:0:2   13:0:2  nonsynonymous_SNV       MODERATE        B       N       N       none    -       -       none    none    -       http://genome.ucsc.edu/cgi-bin/hgTracks?db=hg19&org=human&position=chr1%3A69134-69133
1       69270   A       G       A       PASS    synonymous_SNV  exonic  OR4F5   79501   .       .               93:38:240       518,224 0.6981132075471698      5:5:11  63:30:86        12:5:28 1:0:2   2:2:18  7:3:46  7:2:52  synonymous_SNV  LOW     .       .       .       rs201219564     -       -       none    none    http://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?searchType=adhoc_search&type=rs&rs=rs201219564     http://genome.ucsc.edu/cgi-bin/hgTracks?db=hg19&org=human&position=chr1%3A69270-69269
1       69428   T       G       T       PASS    nonsynonymous_SNV       exonic  OR4F5   79501   D       0.891           676:44:15       74,1396 0.050340136054421766    43:0:2  313:16:10       88:7:3  6:0:0   44:8:0  102:9:0 102:4:2 nonsynonymous_SNV       MODERATE        D       N       N       rs140739101     -       -       14,3808 313,6535        http://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?searchType=adhoc_search&type=rs&rs=rs140739101     http://genome.ucsc.edu/cgi-bin/hgTracks?db=hg19&org=human&position=chr1%3A69428-69427

Parsing

We parse the GME tsv file and extract the following columns:

  • chrom
  • pos
  • ref
  • alt
  • filter
  • GME_AC
  • GME_AF

GRCh37 liftover

The data is not available for GRCh38 on GME website. We performed a liftover from GRCh37 to GRCh38 using CrossMap.

Download URL

http://igm.ucsd.edu/gme/download.shtml

JSON output

"gmeVariome":{
   "allAc":10,
   "allAn":202,
   "allAf":0.049504,
   "failedFilter":true
}
FieldTypeNotes
allAcintGME allele count
allAnintGME allele number
allAffloatGME allele frequency
failedFilterboolTrue if this variant failed any filters
+ + \ No newline at end of file diff --git a/3.22/data-sources/gnomad-lof-json/index.html b/3.22/data-sources/gnomad-lof-json/index.html index 14e8f326..dee0cea9 100644 --- a/3.22/data-sources/gnomad-lof-json/index.html +++ b/3.22/data-sources/gnomad-lof-json/index.html @@ -6,13 +6,13 @@ gnomad-lof-json | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

gnomad-lof-json

"gnomAD":{ 
   "pLi":1.00e0,
   "pNull":8.94e-40,
   "pRec":1.84e-16,
   "synZ":-8.44e-2,
   "misZ":5.96e-1,
   "loeuf":1.13e0
}
FieldTypeNotes
pLifloatprobability of being intolerant of a single loss-of-function variant (like haploinsufficient genes, observed ~ 0.1*expected)
pNullfloatprobability of being completely tolerant of loss of function variation (observed = expected)
pRecfloatprobability of being intolerant of two loss of function variants (like recessive genes, observed ~ 0.5*expected)
synZfloatcorrected synonymous Z score
misZfloatcorrected missense Z score
loeuffloatloss of function observed/expected upper bound fraction (LOEUF)
- - +
Skip to main content
Version: 3.22

gnomad-lof-json

"gnomAD":{ 
   "pLi":1.00e0,
   "pNull":8.94e-40,
   "pRec":1.84e-16,
   "synZ":-8.44e-2,
   "misZ":5.96e-1,
   "loeuf":1.13e0
}
FieldTypeNotes
pLifloatprobability of being intolerant of a single loss-of-function variant (like haploinsufficient genes, observed ~ 0.1*expected)
pNullfloatprobability of being completely tolerant of loss of function variation (observed = expected)
pRecfloatprobability of being intolerant of two loss of function variants (like recessive genes, observed ~ 0.5*expected)
synZfloatcorrected synonymous Z score
misZfloatcorrected missense Z score
loeuffloatloss of function observed/expected upper bound fraction (LOEUF)
+ + \ No newline at end of file diff --git a/3.22/data-sources/gnomad-small-variants-json/index.html b/3.22/data-sources/gnomad-small-variants-json/index.html index 978faf0e..d4882ee1 100644 --- a/3.22/data-sources/gnomad-small-variants-json/index.html +++ b/3.22/data-sources/gnomad-small-variants-json/index.html @@ -6,13 +6,13 @@ gnomad-small-variants-json | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

gnomad-small-variants-json

"gnomad":{ 
   "coverage":20,
   "allAf":0.190317,
   "maleAf":0.193,
   "femaleAf": 0.1935,
   "afrAf":0.222876,
   "amrAf":0.121394,
   "easAf":0.239802,
   "finAf":0.136833,
   "nfeAf":0.181282,
   "asjAf":0.258278,
   "othAf":0.186094,
   "allAn":30796,
   "maleAn":15096,
   "femaleAn":15700
   "afrAn":8664,
   "amrAn":832,
   "easAn":1618,
   "finAn":3486,
   "nfeAn":14916,
   "asjAn":302,
   "othAn":978,
   "allAc":5861,
   "maleAc":2930,
   "femaleAc": 2931,
   "afrAc":1931,
   "amrAc":101,
   "easAc":388,
   "finAc":477,
   "nfeAc":2704,
   "asjAc":78,
   "othAc":182,
   "allHc":561,
   "afrHc":208,
   "amrHc":6,
   "easHc":42,
   "finHc":31,
   "nfeHc":242,
   "asjHc":13,
   "othHc":19,
   "maleHc":280,
   "femaleHc":281,
   "controlsAllAf":0.190317,
   "controlsAllAn":30796,
   "controlsAllAc":5861,
   "lowComplexityRegion":true,
   "failedFilter":true
}
FieldTypeNotes
coverageintaverage coverage (non-negative integer values)
allAffloatallele frequency for all populations. Range: 0 - 1.0
maleAffloatallele frequency for male population. Range: 0 - 1.0
femaleAffloatallele frequency for female population. Range: 0 - 1.0
controlsAllAffloatallele frequency for the controls subset. Range: 0 - 1.0
allAcintallele count for all populations. Integer.
maleAcintallele count for male population. Integer.
femaleAcintallele count for female population. Integer.
controlsAllAcintallele count for the controls subset. Integer.
allAnintallele number for all populations. Non-zero integer.
maleAnintallele number for male population. Non-zero integer.
femaleAnintallele number for female population. Non-zero integer.
controlsAllAnintallele number for the controls subset. Non-zero integer.
allHcintcount of homozygous individuals for all populations. Non-negative integer.
maleHcintcount of homozygous individuals for male population. Non-negative integer.
femaleHcintcount of homozygous individuals for female population. Non-negative integer.
afrAffloatallele frequency for the African / African American population. Range: 0 - 1.0
afrAcintallele count for the African / African American population. Integer.
afrAnintallele number for the African / African American population. Non-zero integer.
afrHcintcount of homozygous individuals for African / African American population. Non-negative integer.
amrAffloatallele frequency for the Latino population. Range: 0 - 1.0
amrAcintallele count for the Latino population. Integer.
amrAnintallele number for the Latino population. Non-zero integer.
amrHcintcount of homozygous individuals for Latino population. Non-negative integer.
easAffloatallele frequency for the East Asian population. Range: 0 - 1.0
easAcintallele count for the East Asian population. Integer.
easAnintallele number for the East Asian population. Non-zero integer.
easHcintcount of homozygous individuals for East Asian population. Non-negative integer.
finAffloatallele frequency for the Finnish population. Range: 0 - 1.0
finAcintallele count for the Finnish population. Integer.
finAnintallele number for the Finnish population. Non-zero integer.
finHcintcount of homozygous individuals for Finnish population. Non-negative integer
nfeAffloatallele frequency for the Non-Finnish European population. Range: 0 - 1.0
nfeAcintallele count for the Non-Finnish European population. Integer.
nfeAnintallele number for the Non-Finnish European population. Non-zero integer.
nfeHcintcount of homozygous individuals for Non-Finnish European population. Non-negative integer
othAffloatallele frequency for the Other population. Range: 0 - 1.0
othAcintallele count for the Other population. Integer.
othAnintallele number for the Other population. Non-zero integer.
othHcintcount of homozygous individuals for Other population. Non-negative integer
asjAffloatallele frequency for the Ashkenazi Jewish population. Range: 0 - 1.0
asjAcintallele count for the Ashkenazi Jewish population Integer.
asjAnintallele number for the Ashkenazi Jewish population. Non-zero integer.
asjHcintcount of homozygous individuals for the Ashkenazi Jewish population. Non-negative integer
sasAffloatallele frequency for the South Asian population. Range: 0 - 1.0
sasAcintallele count for the South Asian population Integer.
sasAnintallele number for the South Asian population. Non-zero integer.
sasHcintcount of homozygous individuals for the South Asian population. Non-negative integer.
failedFilterboolTrue if this variant failed any filters (Note: we do not list the failed filters)
lowComplexityRegionboolTrue if this variant is located in a low complexity region.
- - +
Skip to main content
Version: 3.22

gnomad-small-variants-json

"gnomad":{ 
   "coverage":20,
   "allAf":0.190317,
   "maleAf":0.193,
   "femaleAf": 0.1935,
   "afrAf":0.222876,
   "amrAf":0.121394,
   "easAf":0.239802,
   "finAf":0.136833,
   "nfeAf":0.181282,
   "asjAf":0.258278,
   "othAf":0.186094,
   "allAn":30796,
   "maleAn":15096,
   "femaleAn":15700
   "afrAn":8664,
   "amrAn":832,
   "easAn":1618,
   "finAn":3486,
   "nfeAn":14916,
   "asjAn":302,
   "othAn":978,
   "allAc":5861,
   "maleAc":2930,
   "femaleAc": 2931,
   "afrAc":1931,
   "amrAc":101,
   "easAc":388,
   "finAc":477,
   "nfeAc":2704,
   "asjAc":78,
   "othAc":182,
   "allHc":561,
   "afrHc":208,
   "amrHc":6,
   "easHc":42,
   "finHc":31,
   "nfeHc":242,
   "asjHc":13,
   "othHc":19,
   "maleHc":280,
   "femaleHc":281,
   "controlsAllAf":0.190317,
   "controlsAllAn":30796,
   "controlsAllAc":5861,
   "lowComplexityRegion":true,
   "failedFilter":true
}
FieldTypeNotes
coverageintaverage coverage (non-negative integer values)
allAffloatallele frequency for all populations. Range: 0 - 1.0
maleAffloatallele frequency for male population. Range: 0 - 1.0
femaleAffloatallele frequency for female population. Range: 0 - 1.0
controlsAllAffloatallele frequency for the controls subset. Range: 0 - 1.0
allAcintallele count for all populations. Integer.
maleAcintallele count for male population. Integer.
femaleAcintallele count for female population. Integer.
controlsAllAcintallele count for the controls subset. Integer.
allAnintallele number for all populations. Non-zero integer.
maleAnintallele number for male population. Non-zero integer.
femaleAnintallele number for female population. Non-zero integer.
controlsAllAnintallele number for the controls subset. Non-zero integer.
allHcintcount of homozygous individuals for all populations. Non-negative integer.
maleHcintcount of homozygous individuals for male population. Non-negative integer.
femaleHcintcount of homozygous individuals for female population. Non-negative integer.
afrAffloatallele frequency for the African / African American population. Range: 0 - 1.0
afrAcintallele count for the African / African American population. Integer.
afrAnintallele number for the African / African American population. Non-zero integer.
afrHcintcount of homozygous individuals for African / African American population. Non-negative integer.
amrAffloatallele frequency for the Latino population. Range: 0 - 1.0
amrAcintallele count for the Latino population. Integer.
amrAnintallele number for the Latino population. Non-zero integer.
amrHcintcount of homozygous individuals for Latino population. Non-negative integer.
easAffloatallele frequency for the East Asian population. Range: 0 - 1.0
easAcintallele count for the East Asian population. Integer.
easAnintallele number for the East Asian population. Non-zero integer.
easHcintcount of homozygous individuals for East Asian population. Non-negative integer.
finAffloatallele frequency for the Finnish population. Range: 0 - 1.0
finAcintallele count for the Finnish population. Integer.
finAnintallele number for the Finnish population. Non-zero integer.
finHcintcount of homozygous individuals for Finnish population. Non-negative integer
nfeAffloatallele frequency for the Non-Finnish European population. Range: 0 - 1.0
nfeAcintallele count for the Non-Finnish European population. Integer.
nfeAnintallele number for the Non-Finnish European population. Non-zero integer.
nfeHcintcount of homozygous individuals for Non-Finnish European population. Non-negative integer
othAffloatallele frequency for the Other population. Range: 0 - 1.0
othAcintallele count for the Other population. Integer.
othAnintallele number for the Other population. Non-zero integer.
othHcintcount of homozygous individuals for Other population. Non-negative integer
asjAffloatallele frequency for the Ashkenazi Jewish population. Range: 0 - 1.0
asjAcintallele count for the Ashkenazi Jewish population Integer.
asjAnintallele number for the Ashkenazi Jewish population. Non-zero integer.
asjHcintcount of homozygous individuals for the Ashkenazi Jewish population. Non-negative integer
sasAffloatallele frequency for the South Asian population. Range: 0 - 1.0
sasAcintallele count for the South Asian population Integer.
sasAnintallele number for the South Asian population. Non-zero integer.
sasHcintcount of homozygous individuals for the South Asian population. Non-negative integer.
failedFilterboolTrue if this variant failed any filters (Note: we do not list the failed filters)
lowComplexityRegionboolTrue if this variant is located in a low complexity region.
+ + \ No newline at end of file diff --git a/3.22/data-sources/gnomad-structural-variants-data_description/index.html b/3.22/data-sources/gnomad-structural-variants-data_description/index.html index 91e4f29a..ce64b394 100644 --- a/3.22/data-sources/gnomad-structural-variants-data_description/index.html +++ b/3.22/data-sources/gnomad-structural-variants-data_description/index.html @@ -6,14 +6,14 @@ gnomad-structural-variants-data_description | IlluminaConnectedAnnotations - - + +
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Version: 3.22

gnomad-structural-variants-data_description

Bed Example

The bed file was obtained from original source for GRCh37

#chrom  start   end name    svtype  ALGORITHMS  BOTHSIDES_SUPPORT   CHR2    CPX_INTERVALS   CPX_TYPE    END2    ENDEVIDENCE HIGH_SR_BACKGROUND  PCRPLUS_DEPLETED    PESR_GT_OVERDISPERSION  POS2    PROTEIN_CODING__COPY_GAIN   PROTEIN_CODING__DUP_LOF PROTEIN_CODING__DUP_PARTIAL PROTEIN_CODING__INTERGENIC  PROTEIN_CODING__INTRONIC    PROTEIN_CODING__INV_SPAN    PROTEIN_CODING__LOF PROTEIN_CODING__MSV_EXON_OVR    PROTEIN_CODING__NEAREST_TSS PROTEIN_CODING__PROMOTER    PROTEIN_CODING__UTR SOURCE  STRANDS SVLEN   SVTYPE  UNRESOLVED_TYPE UNSTABLE_AF_PCRPLUS VARIABLE_ACROSS_BATCHES AN  AC  AF  N_BI_GENOS  N_HOMREF    N_HET   N_HOMALT    FREQ_HOMREF FREQ_HET    FREQ_HOMALT MALE_AN MALE_AC MALE_AF MALE_N_BI_GENOS MALE_N_HOMREF   MALE_N_HET  MALE_N_HOMALT   MALE_FREQ_HOMREF    MALE_FREQ_HET   MALE_FREQ_HOMALT    MALE_N_HEMIREF  MALE_N_HEMIALT  MALE_FREQ_HEMIREF   MALE_FREQ_HEMIALT   PAR FEMALE_AN   FEMALE_AC   FEMALE_AF   FEMALE_N_BI_GENOS   FEMALE_N_HOMREF FEMALE_N_HET    FEMALE_N_HOMALT FEMALE_FREQ_HOMREF  FEMALE_FREQ_HET FEMALE_FREQ_HOMALT  POPMAX_AF   AFR_AN  AFR_AC  AFR_AF  AFR_N_BI_GENOS  AFR_N_HOMREF    AFR_N_HET   AFR_N_HOMALT    AFR_FREQ_HOMREF AFR_FREQ_HEAFR_FREQ_HOMALT  AFR_MALE_AN AFR_MALE_AC AFR_MALE_AF AFR_MALE_N_BI_GENOS AFR_MALE_N_HOMREF   AFR_MALE_N_HET  AFR_MALE_N_HOMALT   AFR_MALE_FREQ_HOMREF    AFR_MALE_FREQ_HET   AFR_MALE_FREQ_HOMALT    AFR_MALE_N_HEMIREF  AFR_MALE_N_HEMIALT  AFR_MALE_FREQ_HEMIREF   AFR_MALE_FREQ_HEMIALT   AFR_FEMALE_AN   AFR_FEMALE_AC   AFR_FEMALE_AF   AFR_FEMALE_N_BI_GENOS   AFR_FEMALE_N_HOMREF AFR_FEMALE_N_HET    AFR_FEMALE_N_HOMALT AFR_FEMALE_FREQ_HOMREF  AFR_FEMALE_FREQ_HET AFR_FEMALE_FREQ_HOMALT  AMR_AN  AMR_AC  AMR_AF  AMR_N_BI_GENOS  AMR_N_HOMREF    AMR_N_HET   AMR_N_HOMALT    AMR_FREQ_HOMREF AMR_FREQ_HET    AMR_FREQ_HOMALT AMR_MALE_AN AMR_MALE_AC AMR_MALE_AF AMR_MALE_N_BI_GENOS AMR_MALE_N_HOMREF   AMR_MALE_N_HET  AMR_MALE_N_HOMALT   AMR_MALE_FREQ_HOMREF    AMR_MALE_FREQ_HET   AMR_MALE_FREQ_HOMALT    AMR_MALE_N_HEMIREF  AMR_MALE_N_HEMIALT  AMR_MALE_FREQ_HEMIREF   AMR_MALE_FREQ_HEMIALT   AMR_FEMALE_AN   AMR_FEMALE_AC   AMR_FEMALE_AF   AMR_FEMALE_N_BI_GENOS   AMR_FEMALE_N_HOMREF AMR_FEMALE_N_HET    AMR_FEMALE_N_HOMALT AMR_FEMALE_FREQ_HOMREF  AMR_FEMALE_FREQ_HET AMR_FEMALE_FREQ_HOMALT  EAS_AN  EAS_AC  EAS_AF  EAS_N_BI_GENOS  EAS_N_HOMREF    EAS_N_HET   EAS_N_HOMALT    EAS_FREQ_HOMREF EAS_FREQ_HET    EAS_FREQ_HOMALT EAS_MALE_AN EAS_MALE_AC EAS_MALE_AF EAS_MALE_N_BI_GENOS EAS_MALE_N_HOMREF   EAS_MALE_N_HET  EAS_MALE_N_HOMALT   EAS_MALE_FREQ_HOMREF    EAS_MALE_FREQ_HET   EAS_MALE_FREQ_HOMALT    EAS_MALE_N_HEMIREF  EAS_MALE_N_HEMIALT  EAS_MALE_FREQ_HEMIREF   EAS_MALE_FREQ_HEMIALT   EAS_FEMALE_AN   EAS_FEMALE_AC   EAS_FEMALE_AF   EAS_FEMALE_N_BI_GENOS   EAS_FEMALE_N_HOMREF EAS_FEMALE_N_HET    EAS_FEMALE_N_HOMALT EAS_FEMALE_FREQ_HOMREF  EAS_FEMALE_FREQ_HET EAS_FEMALE_FREQ_HOMALT  EUR_AN  EUR_AC  EUR_AF  EUR_N_BI_GENOS  EUR_N_HOMREF    EUR_N_HET   EUR_N_HOMALT    EUR_FREQ_HOMREF EUR_FREQ_HET    EUR_FREQ_HOMALT EUR_MALE_AN EUR_MALE_AC EUR_MALE_AF EUR_MALE_N_BI_GENOS EUR_MALE_N_HOMREF   EUR_MALE_N_HET  EUR_MALE_N_HOMALT   EUR_MALE_FREQ_HOMREF    EUR_MALE_FREQ_HET   EUR_MALE_FREQ_HOMALT    EUR_MALE_N_HEMIREF  EUR_MALE_N_HEMIALT  EUR_MALE_FREQ_HEMIREF   EUR_MALE_FREQ_HEMIALT   EUR_FEMALE_AN   EUR_FEMALE_AC   EUR_FEMALE_AF   EUR_FEMALE_N_BI_GENOS   EUR_FEMALE_N_HOMREF EUR_FEMALE_N_HET    EUR_FEMALE_N_HOMALT EUR_FEMALE_FREQ_HOMREF  EUR_FEMALE_FREQ_HET EUR_FEMALE_FREQ_HOMALT  OTH_AN  OTH_AC  OTH_AF  OTH_N_BI_GENOS  OTH_N_HOMREF    OTH_N_HET   OTH_N_HOMALT    OTH_FREQ_HOMREF OTH_FREQ_HET    OTH_FREQ_HOMALT OTH_MALE_AN OTH_MALE_AC OTH_MALE_AF OTH_MALE_N_BI_GENOS OTH_MALE_N_HOMREF   OTH_MALE_N_HET  OTH_MALE_N_HOMALT   OTH_MALE_FREQ_HOMREF    OTH_MALE_FREQ_HET   OTH_MALE_FREQ_HOMALT    OTH_MALE_N_HEMIREF  OTH_MALE_N_HEMIALT  OTH_MALE_FREQ_HEMIREF   OTH_MALE_FREQ_HEMIALT   OTH_FEMALE_AN   OTH_FEMALE_AC   OTH_FEMALE_AF   OTH_FEMALE_N_BI_GENOS   OTH_FEMALE_N_HOMREF OTH_FEMALE_N_HET    OTH_FEMALE_N_HOMALT OTH_FEMALE_FREQ_HOMREF  OTH_FEMALE_FREQ_HET OTH_FEMALE_FREQ_HOMALT  FILTER
1   10641   10642   gnomAD-SV_v2.1_BND_1_1  BND manta   False   15  NA  NA  10643   10643   PE,SR   False   False   True    10642   NA  NA  NA  False   NA  NA  NA  NA  NA  NA  NA  NA  NA  -1  BND SINGLE_ENDER_-- False   False   21366   145 0.006785999983549118    10683   10543   135 5   0.9868950247764587  0.012636899948120117    0.00046803298755548894  10866   69  0.00634999992325902 5433    5366    65  2   0.987667977809906   0.011963900178670883    0.000368120992789045    NA  NA  NA  NA  False   10454   76  0.007269999943673615227 5154    70  3   0.9860339760780334  0.013392000459134579    0.0005739430198445916   0.015956999734044075    93972   0.007660999894142151    4699    4629    68  2   0.9851030111312866  0.014471200294792652    0.0004256220126990229   5154    33  0.006403000093996525    2577    2544    33  0   0.9871940016746521  0.012805599719285965    0.0NA   NA  NA  NA  4232    39  0.009216000325977802    2116    2079    35  2   0.9825140237808228  0.01654059998691082 0.0009451800142414868   1910    7   0.003664999967440963    955 949 5   1   0.9937170147895813  0.00523559981957078 0.001047119963914156    950 4   0.004211000166833401    475 472 2   1   0.9936839938163757  0.00421052984893322 0.0021052600350230932   NA  NA  NA  NA  952 3   0.0031510000117123127   476473  3   0   0.9936969876289368  0.006302520167082548    0.0 2296    31  0.013501999899744987    1148    11131   0   0.9729970097541809  0.02700350061058998 0.0 1312    13  0.009909000247716904    656 643 13  0.9801830053329468  0.01981710083782673 0.0 NA  NA  NA  NA  976 18  0.018442999571561813    488470  18  0   0.9631149768829346  0.03688519820570946 0.0 7574    32  0.004224999807775021    3787    37528   2   0.9920780062675476  0.007393720094114542    0.0005281229969114065   3374    17  0.005038999952375889    1681671 15  1   0.9905160069465637  0.008891520090401173    0.000592768017668277    NA  NA  NA  NA  41815   0.003587000072002411    2091    2077    13  1   0.9933050274848938  0.006217120215296745    0.00047823999193497188  3   0.015956999734044075    94  91  3   0   0.968084990978241   0.03191490098834038 0.0 76  0.026316000148653984    38  36  2   0   0.9473680257797241  0.05263160169124603 0.0 NA  NA  NA  NA  112 1   0.008929000236093998    56  55  1   0   0.982142984867096   0.017857100814580917    0.0UNRESOLVED

TSV Example

The tsv was obtained from lifted over dataset created by dbVar for GRCh38

#variant_call_accession variant_call_id variant_call_type   experiment_id   sample_id   sampleset_id    assembly    chrcontig   outer_start start   inner_start inner_stop  stop    outer_stop  insertion_length    variant_region_acc  variant_region_id   copy_number description validation  zygosity    origin  phenotype   hgvs_name   placement_method    placement_rank  placements_per_assembly remap_alignment remap_best_within_cluster   remap_coverage  remap_diff_chr  remap_failure_code  allele_count    allele_frequency    allele_number
nssv15777856    gnomAD-SV_v2.1_CNV_10_564_alt_1 copy number variation   1       1   GRCh38.p12  10          736806          738184          nsv4039284  10__782746___784124______GRCh37.p13_copy_number_variation   0   Remapped    BestAvailable   Single  First Pass  0   1           AC=21,AFR_AC=10,AMR_AC=9,EAS_AC=0,EUR_AC=2,OTH_AC=0AF=0.038889,AFR_AF=0.044643,AMR_AF=0.03913,EAS_AF=0,EUR_AF=0.023256,OTH_AF=0 AN=540,AFR_AN=224,AMR_AN=230,EAS_AN=0,EUR_AN=86,OTH_AN=0

Structural Variant Type Mapping

The source files represented the structural variants with keys using various naming conventions. -In the Illumina Connected Annotations JSON output, these keys will be mapped according to the following.

Illumina Connected Annotations JSON SV Type KeyGRCh37 Source SV Type KeyGRCh38 Source SV Type Key
copy_number_variationcopy number variation
deletionDEL, CN=0deletion
duplicationDUPduplication
insertionINSinsertion
inversionINVinversion
mobile_element_insertionINS:MEmobile element insertion
mobile_element_insertionINS:ME:ALUalu insertion
mobile_element_insertionINS:ME:LINE1line1 insertion
mobile_element_insertionINS:ME:SVAsva insertion
structural alterationsequence alteration
complex_structural_alterationCPX
- - +In the Illumina Connected Annotations JSON output, these keys will be mapped according to the following.

Illumina Connected Annotations JSON SV Type KeyGRCh37 Source SV Type KeyGRCh38 Source SV Type Key
copy_number_variationcopy number variation
deletionDEL, CN=0deletion
duplicationDUPduplication
insertionINSinsertion
inversionINVinversion
mobile_element_insertionINS:MEmobile element insertion
mobile_element_insertionINS:ME:ALUalu insertion
mobile_element_insertionINS:ME:LINE1line1 insertion
mobile_element_insertionINS:ME:SVAsva insertion
structural alterationsequence alteration
complex_structural_alterationCPX
+ + \ No newline at end of file diff --git a/3.22/data-sources/gnomad-structural-variants-json/index.html b/3.22/data-sources/gnomad-structural-variants-json/index.html index 8d2a4fc8..3c598ab9 100644 --- a/3.22/data-sources/gnomad-structural-variants-json/index.html +++ b/3.22/data-sources/gnomad-structural-variants-json/index.html @@ -6,13 +6,13 @@ gnomad-structural-variants-json | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

gnomad-structural-variants-json

"gnomAD-preview": [
  {
    "chromosome": "1",
    "begin": 40001,
    "end": 47200,
    "variantId": "gnomAD-SV_v2.1_DUP_1_1",
    "variantType": "duplication",
    "failedFilter": true,
    "allAf": 0.068963,
    "afrAf": 0.135694,
    "amrAf": 0.022876,
    "easAf": 0.01101,
    "eurAf": 0.007846,
    "othAf": 0.017544,
    "femaleAf": 0.065288,
    "maleAf": 0.07255,
    "allAc": 943,
    "afrAc": 866,
    "amrAc": 21,
    "easAc": 17,
    "eurAc": 37,
    "othAc": 2,
    "femaleAc": 442,
    "maleAc": 499,
    "allAn": 13674,
    "afrAn": 6382,
    "amrAn": 918,
    "easAn": 1544,
    "eurAn": 4716,
    "othAn": 114,
    "femaleAn": 6770,
    "maleAn": 6878,
    "allHc": 91,
    "afrHc": 90,
    "amrHc": 1,
    "easHc": 0,
    "eurHc": 0,
    "othHc": 55,
    "femaleHc": 44,
    "maleHc": 47,
    "reciprocalOverlap": 0.01839,
    "annotationOverlap": 0.16667
  }
]

FieldTypeNotes
chromosomestringchromosome number
beginintegerposition interval start
endintegerposition internal end
variantTypestringstructural variant type
variantIdstringgnomAD ID
allAffloating pointallele frequency for all populations. Range: 0 - 1.0
afrAffloating pointallele frequency for the African super population. Range: 0 - 1.0
amrAffloating pointallele frequency for the Ad Mixed American super population. Range: 0 - 1.0
easAffloating pointallele frequency for the East Asian super population. Range: 0 - 1.0
eurAffloating pointallele frequency for the European super population. Range: 0 - 1.0
othAffloating pointallele frequency for all other populations. Range: 0 - 1.0
femaleAffloating pointallele frequency for female population. Range: 0 - 1.0
maleAffloating pointallele frequency for male population. Range: 0 - 1.0
allAcintegerallele count for all populations.
afrAcintegerallele count for the African super population.
amrAcintegerallele count for the Ad Mixed American super population.
easAcintegerallele count for the East Asian super population.
eurAcintegerallele count for the European super population.
othAcintegerallele count for all other populations.
maleAcintegerallele count for male population.
femaleAcintegerallele count for female population.
allAnintegerallele number for all populations.
afrAnintegerallele number for the African super population.
amrAnintegerallele number for the Ad Mixed American super population.
easAnintegerallele number for the East Asian super population.
eurAnintegerallele number for the European super population.
othAnintegerallele number for all other populations.
femaleAnintegerallele number for female population.
maleAnintegerallele number for male population.
allHcintegercount of homozygous individuals for all populations.
afrHcintegercount of homozygous individuals for the African / African American population.
amrHcintegercount of homozygous individuals for the Latino population.
easHcintegercount of homozygous individuals for the East Asian population.
eurAcintegercount of homozygous individuals for the European super population.
othHcintegercount of homozygous individuals for all other populations.
maleHcintegercount of homozygous individuals for male population.
femaleHcintegercount of homozygous individuals for female population.
failedFilterbooleanTrue if this variant failed any filters (Note: we do not list the failed filters)
reciprocalOverlapfloating pointReciprocal overlap. Range: 0 - 1.0
annotationOverlapfloating pointReciprocal overlap. Range: 0 - 1.0

Note: Following fields are not available in GRCh38 because the source file does not contain this information:

Field
femaleAf
maleAf
maleAc
femaleAc
femaleAn
maleAn
allHc
afrHc
amrHc
easHc
eurAc
othHc
maleHc
femaleHc
failedFilter
- - +
Skip to main content
Version: 3.22

gnomad-structural-variants-json

"gnomAD-preview": [
  {
    "chromosome": "1",
    "begin": 40001,
    "end": 47200,
    "variantId": "gnomAD-SV_v2.1_DUP_1_1",
    "variantType": "duplication",
    "failedFilter": true,
    "allAf": 0.068963,
    "afrAf": 0.135694,
    "amrAf": 0.022876,
    "easAf": 0.01101,
    "eurAf": 0.007846,
    "othAf": 0.017544,
    "femaleAf": 0.065288,
    "maleAf": 0.07255,
    "allAc": 943,
    "afrAc": 866,
    "amrAc": 21,
    "easAc": 17,
    "eurAc": 37,
    "othAc": 2,
    "femaleAc": 442,
    "maleAc": 499,
    "allAn": 13674,
    "afrAn": 6382,
    "amrAn": 918,
    "easAn": 1544,
    "eurAn": 4716,
    "othAn": 114,
    "femaleAn": 6770,
    "maleAn": 6878,
    "allHc": 91,
    "afrHc": 90,
    "amrHc": 1,
    "easHc": 0,
    "eurHc": 0,
    "othHc": 55,
    "femaleHc": 44,
    "maleHc": 47,
    "reciprocalOverlap": 0.01839,
    "annotationOverlap": 0.16667
  }
]

FieldTypeNotes
chromosomestringchromosome number
beginintegerposition interval start
endintegerposition internal end
variantTypestringstructural variant type
variantIdstringgnomAD ID
allAffloating pointallele frequency for all populations. Range: 0 - 1.0
afrAffloating pointallele frequency for the African super population. Range: 0 - 1.0
amrAffloating pointallele frequency for the Ad Mixed American super population. Range: 0 - 1.0
easAffloating pointallele frequency for the East Asian super population. Range: 0 - 1.0
eurAffloating pointallele frequency for the European super population. Range: 0 - 1.0
othAffloating pointallele frequency for all other populations. Range: 0 - 1.0
femaleAffloating pointallele frequency for female population. Range: 0 - 1.0
maleAffloating pointallele frequency for male population. Range: 0 - 1.0
allAcintegerallele count for all populations.
afrAcintegerallele count for the African super population.
amrAcintegerallele count for the Ad Mixed American super population.
easAcintegerallele count for the East Asian super population.
eurAcintegerallele count for the European super population.
othAcintegerallele count for all other populations.
maleAcintegerallele count for male population.
femaleAcintegerallele count for female population.
allAnintegerallele number for all populations.
afrAnintegerallele number for the African super population.
amrAnintegerallele number for the Ad Mixed American super population.
easAnintegerallele number for the East Asian super population.
eurAnintegerallele number for the European super population.
othAnintegerallele number for all other populations.
femaleAnintegerallele number for female population.
maleAnintegerallele number for male population.
allHcintegercount of homozygous individuals for all populations.
afrHcintegercount of homozygous individuals for the African / African American population.
amrHcintegercount of homozygous individuals for the Latino population.
easHcintegercount of homozygous individuals for the East Asian population.
eurAcintegercount of homozygous individuals for the European super population.
othHcintegercount of homozygous individuals for all other populations.
maleHcintegercount of homozygous individuals for male population.
femaleHcintegercount of homozygous individuals for female population.
failedFilterbooleanTrue if this variant failed any filters (Note: we do not list the failed filters)
reciprocalOverlapfloating pointReciprocal overlap. Range: 0 - 1.0
annotationOverlapfloating pointReciprocal overlap. Range: 0 - 1.0

Note: Following fields are not available in GRCh38 because the source file does not contain this information:

Field
femaleAf
maleAf
maleAc
femaleAc
femaleAn
maleAn
allHc
afrHc
amrHc
easHc
eurAc
othHc
maleHc
femaleHc
failedFilter
+ + \ No newline at end of file diff --git a/3.22/data-sources/gnomad/index.html b/3.22/data-sources/gnomad/index.html index 2eed6432..a3c7d9e9 100644 --- a/3.22/data-sources/gnomad/index.html +++ b/3.22/data-sources/gnomad/index.html @@ -6,8 +6,8 @@ gnomAD | IlluminaConnectedAnnotations - - + +
@@ -15,8 +15,8 @@ The gnomAD structural variant annotations are in a preview stage at the moment. Currently, the annotations do not include translocation breakends. Future updates will include a better way of annotating the structural variants.

Source Files

Bed Example

The bed file was obtained from original source for GRCh37

#chrom  start   end name    svtype  ALGORITHMS  BOTHSIDES_SUPPORT   CHR2    CPX_INTERVALS   CPX_TYPE    END2    ENDEVIDENCE HIGH_SR_BACKGROUND  PCRPLUS_DEPLETED    PESR_GT_OVERDISPERSION  POS2    PROTEIN_CODING__COPY_GAIN   PROTEIN_CODING__DUP_LOF PROTEIN_CODING__DUP_PARTIAL PROTEIN_CODING__INTERGENIC  PROTEIN_CODING__INTRONIC    PROTEIN_CODING__INV_SPAN    PROTEIN_CODING__LOF PROTEIN_CODING__MSV_EXON_OVR    PROTEIN_CODING__NEAREST_TSS PROTEIN_CODING__PROMOTER    PROTEIN_CODING__UTR SOURCE  STRANDS SVLEN   SVTYPE  UNRESOLVED_TYPE UNSTABLE_AF_PCRPLUS VARIABLE_ACROSS_BATCHES AN  AC  AF  N_BI_GENOS  N_HOMREF    N_HET   N_HOMALT    FREQ_HOMREF FREQ_HET    FREQ_HOMALT MALE_AN MALE_AC MALE_AF MALE_N_BI_GENOS MALE_N_HOMREF   MALE_N_HET  MALE_N_HOMALT   MALE_FREQ_HOMREF    MALE_FREQ_HET   MALE_FREQ_HOMALT    MALE_N_HEMIREF  MALE_N_HEMIALT  MALE_FREQ_HEMIREF   MALE_FREQ_HEMIALT   PAR FEMALE_AN   FEMALE_AC   FEMALE_AF   FEMALE_N_BI_GENOS   FEMALE_N_HOMREF FEMALE_N_HET    FEMALE_N_HOMALT FEMALE_FREQ_HOMREF  FEMALE_FREQ_HET FEMALE_FREQ_HOMALT  POPMAX_AF   AFR_AN  AFR_AC  AFR_AF  AFR_N_BI_GENOS  AFR_N_HOMREF    AFR_N_HET   AFR_N_HOMALT    AFR_FREQ_HOMREF AFR_FREQ_HEAFR_FREQ_HOMALT  AFR_MALE_AN AFR_MALE_AC AFR_MALE_AF AFR_MALE_N_BI_GENOS AFR_MALE_N_HOMREF   AFR_MALE_N_HET  AFR_MALE_N_HOMALT   AFR_MALE_FREQ_HOMREF    AFR_MALE_FREQ_HET   AFR_MALE_FREQ_HOMALT    AFR_MALE_N_HEMIREF  AFR_MALE_N_HEMIALT  AFR_MALE_FREQ_HEMIREF   AFR_MALE_FREQ_HEMIALT   AFR_FEMALE_AN   AFR_FEMALE_AC   AFR_FEMALE_AF   AFR_FEMALE_N_BI_GENOS   AFR_FEMALE_N_HOMREF AFR_FEMALE_N_HET    AFR_FEMALE_N_HOMALT AFR_FEMALE_FREQ_HOMREF  AFR_FEMALE_FREQ_HET AFR_FEMALE_FREQ_HOMALT  AMR_AN  AMR_AC  AMR_AF  AMR_N_BI_GENOS  AMR_N_HOMREF    AMR_N_HET   AMR_N_HOMALT    AMR_FREQ_HOMREF AMR_FREQ_HET    AMR_FREQ_HOMALT AMR_MALE_AN AMR_MALE_AC AMR_MALE_AF AMR_MALE_N_BI_GENOS AMR_MALE_N_HOMREF   AMR_MALE_N_HET  AMR_MALE_N_HOMALT   AMR_MALE_FREQ_HOMREF    AMR_MALE_FREQ_HET   AMR_MALE_FREQ_HOMALT    AMR_MALE_N_HEMIREF  AMR_MALE_N_HEMIALT  AMR_MALE_FREQ_HEMIREF   AMR_MALE_FREQ_HEMIALT   AMR_FEMALE_AN   AMR_FEMALE_AC   AMR_FEMALE_AF   AMR_FEMALE_N_BI_GENOS   AMR_FEMALE_N_HOMREF AMR_FEMALE_N_HET    AMR_FEMALE_N_HOMALT AMR_FEMALE_FREQ_HOMREF  AMR_FEMALE_FREQ_HET AMR_FEMALE_FREQ_HOMALT  EAS_AN  EAS_AC  EAS_AF  EAS_N_BI_GENOS  EAS_N_HOMREF    EAS_N_HET   EAS_N_HOMALT    EAS_FREQ_HOMREF EAS_FREQ_HET    EAS_FREQ_HOMALT EAS_MALE_AN EAS_MALE_AC EAS_MALE_AF EAS_MALE_N_BI_GENOS EAS_MALE_N_HOMREF   EAS_MALE_N_HET  EAS_MALE_N_HOMALT   EAS_MALE_FREQ_HOMREF    EAS_MALE_FREQ_HET   EAS_MALE_FREQ_HOMALT    EAS_MALE_N_HEMIREF  EAS_MALE_N_HEMIALT  EAS_MALE_FREQ_HEMIREF   EAS_MALE_FREQ_HEMIALT   EAS_FEMALE_AN   EAS_FEMALE_AC   EAS_FEMALE_AF   EAS_FEMALE_N_BI_GENOS   EAS_FEMALE_N_HOMREF EAS_FEMALE_N_HET    EAS_FEMALE_N_HOMALT EAS_FEMALE_FREQ_HOMREF  EAS_FEMALE_FREQ_HET EAS_FEMALE_FREQ_HOMALT  EUR_AN  EUR_AC  EUR_AF  EUR_N_BI_GENOS  EUR_N_HOMREF    EUR_N_HET   EUR_N_HOMALT    EUR_FREQ_HOMREF EUR_FREQ_HET    EUR_FREQ_HOMALT EUR_MALE_AN EUR_MALE_AC EUR_MALE_AF EUR_MALE_N_BI_GENOS EUR_MALE_N_HOMREF   EUR_MALE_N_HET  EUR_MALE_N_HOMALT   EUR_MALE_FREQ_HOMREF    EUR_MALE_FREQ_HET   EUR_MALE_FREQ_HOMALT    EUR_MALE_N_HEMIREF  EUR_MALE_N_HEMIALT  EUR_MALE_FREQ_HEMIREF   EUR_MALE_FREQ_HEMIALT   EUR_FEMALE_AN   EUR_FEMALE_AC   EUR_FEMALE_AF   EUR_FEMALE_N_BI_GENOS   EUR_FEMALE_N_HOMREF EUR_FEMALE_N_HET    EUR_FEMALE_N_HOMALT EUR_FEMALE_FREQ_HOMREF  EUR_FEMALE_FREQ_HET EUR_FEMALE_FREQ_HOMALT  OTH_AN  OTH_AC  OTH_AF  OTH_N_BI_GENOS  OTH_N_HOMREF    OTH_N_HET   OTH_N_HOMALT    OTH_FREQ_HOMREF OTH_FREQ_HET    OTH_FREQ_HOMALT OTH_MALE_AN OTH_MALE_AC OTH_MALE_AF OTH_MALE_N_BI_GENOS OTH_MALE_N_HOMREF   OTH_MALE_N_HET  OTH_MALE_N_HOMALT   OTH_MALE_FREQ_HOMREF    OTH_MALE_FREQ_HET   OTH_MALE_FREQ_HOMALT    OTH_MALE_N_HEMIREF  OTH_MALE_N_HEMIALT  OTH_MALE_FREQ_HEMIREF   OTH_MALE_FREQ_HEMIALT   OTH_FEMALE_AN   OTH_FEMALE_AC   OTH_FEMALE_AF   OTH_FEMALE_N_BI_GENOS   OTH_FEMALE_N_HOMREF OTH_FEMALE_N_HET    OTH_FEMALE_N_HOMALT OTH_FEMALE_FREQ_HOMREF  OTH_FEMALE_FREQ_HET OTH_FEMALE_FREQ_HOMALT  FILTER
1   10641   10642   gnomAD-SV_v2.1_BND_1_1  BND manta   False   15  NA  NA  10643   10643   PE,SR   False   False   True    10642   NA  NA  NA  False   NA  NA  NA  NA  NA  NA  NA  NA  NA  -1  BND SINGLE_ENDER_-- False   False   21366   145 0.006785999983549118    10683   10543   135 5   0.9868950247764587  0.012636899948120117    0.00046803298755548894  10866   69  0.00634999992325902 5433    5366    65  2   0.987667977809906   0.011963900178670883    0.000368120992789045    NA  NA  NA  NA  False   10454   76  0.007269999943673615227 5154    70  3   0.9860339760780334  0.013392000459134579    0.0005739430198445916   0.015956999734044075    93972   0.007660999894142151    4699    4629    68  2   0.9851030111312866  0.014471200294792652    0.0004256220126990229   5154    33  0.006403000093996525    2577    2544    33  0   0.9871940016746521  0.012805599719285965    0.0NA   NA  NA  NA  4232    39  0.009216000325977802    2116    2079    35  2   0.9825140237808228  0.01654059998691082 0.0009451800142414868   1910    7   0.003664999967440963    955 949 5   1   0.9937170147895813  0.00523559981957078 0.001047119963914156    950 4   0.004211000166833401    475 472 2   1   0.9936839938163757  0.00421052984893322 0.0021052600350230932   NA  NA  NA  NA  952 3   0.0031510000117123127   476473  3   0   0.9936969876289368  0.006302520167082548    0.0 2296    31  0.013501999899744987    1148    11131   0   0.9729970097541809  0.02700350061058998 0.0 1312    13  0.009909000247716904    656 643 13  0.9801830053329468  0.01981710083782673 0.0 NA  NA  NA  NA  976 18  0.018442999571561813    488470  18  0   0.9631149768829346  0.03688519820570946 0.0 7574    32  0.004224999807775021    3787    37528   2   0.9920780062675476  0.007393720094114542    0.0005281229969114065   3374    17  0.005038999952375889    1681671 15  1   0.9905160069465637  0.008891520090401173    0.000592768017668277    NA  NA  NA  NA  41815   0.003587000072002411    2091    2077    13  1   0.9933050274848938  0.006217120215296745    0.00047823999193497188  3   0.015956999734044075    94  91  3   0   0.968084990978241   0.03191490098834038 0.0 76  0.026316000148653984    38  36  2   0   0.9473680257797241  0.05263160169124603 0.0 NA  NA  NA  NA  112 1   0.008929000236093998    56  55  1   0   0.982142984867096   0.017857100814580917    0.0UNRESOLVED

TSV Example

The tsv was obtained from lifted over dataset created by dbVar for GRCh38

#variant_call_accession variant_call_id variant_call_type   experiment_id   sample_id   sampleset_id    assembly    chrcontig   outer_start start   inner_start inner_stop  stop    outer_stop  insertion_length    variant_region_acc  variant_region_id   copy_number description validation  zygosity    origin  phenotype   hgvs_name   placement_method    placement_rank  placements_per_assembly remap_alignment remap_best_within_cluster   remap_coverage  remap_diff_chr  remap_failure_code  allele_count    allele_frequency    allele_number
nssv15777856    gnomAD-SV_v2.1_CNV_10_564_alt_1 copy number variation   1       1   GRCh38.p12  10          736806          738184          nsv4039284  10__782746___784124______GRCh37.p13_copy_number_variation   0   Remapped    BestAvailable   Single  First Pass  0   1           AC=21,AFR_AC=10,AMR_AC=9,EAS_AC=0,EUR_AC=2,OTH_AC=0AF=0.038889,AFR_AF=0.044643,AMR_AF=0.03913,EAS_AF=0,EUR_AF=0.023256,OTH_AF=0 AN=540,AFR_AN=224,AMR_AN=230,EAS_AN=0,EUR_AN=86,OTH_AN=0

Structural Variant Type Mapping

The source files represented the structural variants with keys using various naming conventions. -In the Illumina Connected Annotations JSON output, these keys will be mapped according to the following.

Illumina Connected Annotations JSON SV Type KeyGRCh37 Source SV Type KeyGRCh38 Source SV Type Key
copy_number_variationcopy number variation
deletionDEL, CN=0deletion
duplicationDUPduplication
insertionINSinsertion
inversionINVinversion
mobile_element_insertionINS:MEmobile element insertion
mobile_element_insertionINS:ME:ALUalu insertion
mobile_element_insertionINS:ME:LINE1line1 insertion
mobile_element_insertionINS:ME:SVAsva insertion
structural alterationsequence alteration
complex_structural_alterationCPX

Download URLs

GRCh37

The GRCh37 file was downloaded from the original source. Following table gives some essential data metrics:

https://storage.googleapis.com/gcp-public-data--gnomad/papers/2019-sv/gnomad_v2.1_sv.sites.bed.gz

GRCh38

Note: The data was unavailable from gnomAD 2.1 original source, however the lifted over structural variant dataset was created by dbVar and was obtained from them https://www.ncbi.nlm.nih.gov/sites/dbvarapp/studies/nstd166/.

Download URL

https://ftp.ncbi.nlm.nih.gov/pub/dbVar/data/Homo_sapiens/by_study/tsv/nstd166.GRCh38.variant_call.tsv.gz

JSON output

"gnomAD-preview": [
  {
    "chromosome": "1",
    "begin": 40001,
    "end": 47200,
    "variantId": "gnomAD-SV_v2.1_DUP_1_1",
    "variantType": "duplication",
    "failedFilter": true,
    "allAf": 0.068963,
    "afrAf": 0.135694,
    "amrAf": 0.022876,
    "easAf": 0.01101,
    "eurAf": 0.007846,
    "othAf": 0.017544,
    "femaleAf": 0.065288,
    "maleAf": 0.07255,
    "allAc": 943,
    "afrAc": 866,
    "amrAc": 21,
    "easAc": 17,
    "eurAc": 37,
    "othAc": 2,
    "femaleAc": 442,
    "maleAc": 499,
    "allAn": 13674,
    "afrAn": 6382,
    "amrAn": 918,
    "easAn": 1544,
    "eurAn": 4716,
    "othAn": 114,
    "femaleAn": 6770,
    "maleAn": 6878,
    "allHc": 91,
    "afrHc": 90,
    "amrHc": 1,
    "easHc": 0,
    "eurHc": 0,
    "othHc": 55,
    "femaleHc": 44,
    "maleHc": 47,
    "reciprocalOverlap": 0.01839,
    "annotationOverlap": 0.16667
  }
]

FieldTypeNotes
chromosomestringchromosome number
beginintegerposition interval start
endintegerposition internal end
variantTypestringstructural variant type
variantIdstringgnomAD ID
allAffloating pointallele frequency for all populations. Range: 0 - 1.0
afrAffloating pointallele frequency for the African super population. Range: 0 - 1.0
amrAffloating pointallele frequency for the Ad Mixed American super population. Range: 0 - 1.0
easAffloating pointallele frequency for the East Asian super population. Range: 0 - 1.0
eurAffloating pointallele frequency for the European super population. Range: 0 - 1.0
othAffloating pointallele frequency for all other populations. Range: 0 - 1.0
femaleAffloating pointallele frequency for female population. Range: 0 - 1.0
maleAffloating pointallele frequency for male population. Range: 0 - 1.0
allAcintegerallele count for all populations.
afrAcintegerallele count for the African super population.
amrAcintegerallele count for the Ad Mixed American super population.
easAcintegerallele count for the East Asian super population.
eurAcintegerallele count for the European super population.
othAcintegerallele count for all other populations.
maleAcintegerallele count for male population.
femaleAcintegerallele count for female population.
allAnintegerallele number for all populations.
afrAnintegerallele number for the African super population.
amrAnintegerallele number for the Ad Mixed American super population.
easAnintegerallele number for the East Asian super population.
eurAnintegerallele number for the European super population.
othAnintegerallele number for all other populations.
femaleAnintegerallele number for female population.
maleAnintegerallele number for male population.
allHcintegercount of homozygous individuals for all populations.
afrHcintegercount of homozygous individuals for the African / African American population.
amrHcintegercount of homozygous individuals for the Latino population.
easHcintegercount of homozygous individuals for the East Asian population.
eurAcintegercount of homozygous individuals for the European super population.
othHcintegercount of homozygous individuals for all other populations.
maleHcintegercount of homozygous individuals for male population.
femaleHcintegercount of homozygous individuals for female population.
failedFilterbooleanTrue if this variant failed any filters (Note: we do not list the failed filters)
reciprocalOverlapfloating pointReciprocal overlap. Range: 0 - 1.0
annotationOverlapfloating pointReciprocal overlap. Range: 0 - 1.0

Note: Following fields are not available in GRCh38 because the source file does not contain this information:

Field
femaleAf
maleAf
maleAc
femaleAc
femaleAn
maleAn
allHc
afrHc
amrHc
easHc
eurAc
othHc
maleHc
femaleHc
failedFilter
- - +In the Illumina Connected Annotations JSON output, these keys will be mapped according to the following.

Illumina Connected Annotations JSON SV Type KeyGRCh37 Source SV Type KeyGRCh38 Source SV Type Key
copy_number_variationcopy number variation
deletionDEL, CN=0deletion
duplicationDUPduplication
insertionINSinsertion
inversionINVinversion
mobile_element_insertionINS:MEmobile element insertion
mobile_element_insertionINS:ME:ALUalu insertion
mobile_element_insertionINS:ME:LINE1line1 insertion
mobile_element_insertionINS:ME:SVAsva insertion
structural alterationsequence alteration
complex_structural_alterationCPX

Download URLs

GRCh37

The GRCh37 file was downloaded from the original source. Following table gives some essential data metrics:

https://storage.googleapis.com/gcp-public-data--gnomad/papers/2019-sv/gnomad_v2.1_sv.sites.bed.gz

GRCh38

Note: The data was unavailable from gnomAD 2.1 original source, however the lifted over structural variant dataset was created by dbVar and was obtained from them https://www.ncbi.nlm.nih.gov/sites/dbvarapp/studies/nstd166/.

Download URL

https://ftp.ncbi.nlm.nih.gov/pub/dbVar/data/Homo_sapiens/by_study/tsv/nstd166.GRCh38.variant_call.tsv.gz

JSON output

"gnomAD-preview": [
  {
    "chromosome": "1",
    "begin": 40001,
    "end": 47200,
    "variantId": "gnomAD-SV_v2.1_DUP_1_1",
    "variantType": "duplication",
    "failedFilter": true,
    "allAf": 0.068963,
    "afrAf": 0.135694,
    "amrAf": 0.022876,
    "easAf": 0.01101,
    "eurAf": 0.007846,
    "othAf": 0.017544,
    "femaleAf": 0.065288,
    "maleAf": 0.07255,
    "allAc": 943,
    "afrAc": 866,
    "amrAc": 21,
    "easAc": 17,
    "eurAc": 37,
    "othAc": 2,
    "femaleAc": 442,
    "maleAc": 499,
    "allAn": 13674,
    "afrAn": 6382,
    "amrAn": 918,
    "easAn": 1544,
    "eurAn": 4716,
    "othAn": 114,
    "femaleAn": 6770,
    "maleAn": 6878,
    "allHc": 91,
    "afrHc": 90,
    "amrHc": 1,
    "easHc": 0,
    "eurHc": 0,
    "othHc": 55,
    "femaleHc": 44,
    "maleHc": 47,
    "reciprocalOverlap": 0.01839,
    "annotationOverlap": 0.16667
  }
]

FieldTypeNotes
chromosomestringchromosome number
beginintegerposition interval start
endintegerposition internal end
variantTypestringstructural variant type
variantIdstringgnomAD ID
allAffloating pointallele frequency for all populations. Range: 0 - 1.0
afrAffloating pointallele frequency for the African super population. Range: 0 - 1.0
amrAffloating pointallele frequency for the Ad Mixed American super population. Range: 0 - 1.0
easAffloating pointallele frequency for the East Asian super population. Range: 0 - 1.0
eurAffloating pointallele frequency for the European super population. Range: 0 - 1.0
othAffloating pointallele frequency for all other populations. Range: 0 - 1.0
femaleAffloating pointallele frequency for female population. Range: 0 - 1.0
maleAffloating pointallele frequency for male population. Range: 0 - 1.0
allAcintegerallele count for all populations.
afrAcintegerallele count for the African super population.
amrAcintegerallele count for the Ad Mixed American super population.
easAcintegerallele count for the East Asian super population.
eurAcintegerallele count for the European super population.
othAcintegerallele count for all other populations.
maleAcintegerallele count for male population.
femaleAcintegerallele count for female population.
allAnintegerallele number for all populations.
afrAnintegerallele number for the African super population.
amrAnintegerallele number for the Ad Mixed American super population.
easAnintegerallele number for the East Asian super population.
eurAnintegerallele number for the European super population.
othAnintegerallele number for all other populations.
femaleAnintegerallele number for female population.
maleAnintegerallele number for male population.
allHcintegercount of homozygous individuals for all populations.
afrHcintegercount of homozygous individuals for the African / African American population.
amrHcintegercount of homozygous individuals for the Latino population.
easHcintegercount of homozygous individuals for the East Asian population.
eurAcintegercount of homozygous individuals for the European super population.
othHcintegercount of homozygous individuals for all other populations.
maleHcintegercount of homozygous individuals for male population.
femaleHcintegercount of homozygous individuals for female population.
failedFilterbooleanTrue if this variant failed any filters (Note: we do not list the failed filters)
reciprocalOverlapfloating pointReciprocal overlap. Range: 0 - 1.0
annotationOverlapfloating pointReciprocal overlap. Range: 0 - 1.0

Note: Following fields are not available in GRCh38 because the source file does not contain this information:

Field
femaleAf
maleAf
maleAc
femaleAc
femaleAn
maleAn
allHc
afrHc
amrHc
easHc
eurAc
othHc
maleHc
femaleHc
failedFilter
+ + \ No newline at end of file diff --git a/3.22/data-sources/mito-heteroplasmy/index.html b/3.22/data-sources/mito-heteroplasmy/index.html index 3104fb2f..f51eb6aa 100644 --- a/3.22/data-sources/mito-heteroplasmy/index.html +++ b/3.22/data-sources/mito-heteroplasmy/index.html @@ -6,13 +6,13 @@ Mitochondrial Heteroplasmy | IlluminaConnectedAnnotations - - + +
-
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Version: 3.22

Mitochondrial Heteroplasmy

Overview

Mitochondrial Heteroplasmy is an aggregate population data set that characterizes the amount of heteroplasmy observed for each variant. The latest version of this data set is based on re-processed 1000 Genomes Project data using the Illumina DRAGEN pipeline.

JSON File

Example

{
   "T:C":{
      "ad":[
         1,
         1,
         1,
         1,
         1,
         1
      ],
      "allele_type":"alt",
      "vrf":[
         0.002369668246445498,
         0.0024937655860349127,
         0.0016129032258064516,
         0.0025188916876574307,
         0.0022935779816513763,
         0.002008032128514056
      ],
      "vrf_stats":{
         "kurtosis":38.889891511122556,
         "max":0.0025188916876574307,
         "mean":5.4052190471990743e-05,
         "min":0.0,
         "nobs":246,
         "skewness":6.346664692283075,
         "stdev":0.0003461416264750575,
         "variance":1.1981402557879823e-07
      }
   }
}

Parsing

From the JSON file, we're mainly interested in the following keys:

  • variant (i.e. T:C)
  • ad
  • vrf
  • nobs (number of observations)
Adjusting for null observations

The nobs value indicates how many observations were made. Ideally this would have been represented in the ad and vrf arrays, but it's left as an exercise for the reader.

Binning VRF Data

The vrf (variant read frequency) array in the JSON object above is paired with with the ad array (allele depths) shown above.

The data in the JSON object has a crazy number of significant digits. This means that as the number of samples increase, this array will grow. To make this more future-proof, Illumina Connected Annotations bins everything according to 0.1% increments.

With the binned data, we end up having 775 distinct vrf values in the entire JSON file. This also means that the variant with the largest number of VRFs would originally have 246 entries, but due to binning this will decrease to 143.

Pre-processing the Data

The JSON file is converted into a small TSV file that is embedded in Illumina Connected Annotations. Here is an example of the TSV file:

#CHROM  POS REF ALT VRF_BINS    VRF_COUNTS
chrM    1   G   .   0.981,0.987,0.988,0.989,0.99,0.991,0.992,0.993,0.994,0.995,0.996,0.997,0.998,0.999  1,2,2,4,7,8,11,19,43,60,48,64,499,1736
chrM    2   A   .   0.981,0.987,0.988,0.989,0.99,0.991,0.992,0.993,0.994,0.995,0.996,0.997,0.998,0.999  1,2,2,4,7,8,11,19,43,60,48,64,499,1736

Algorithm

Illumina Connected Annotations will calculate mitochondrial heteroplasmy data for every sample in the VCF. Using the computed VRF for each sample, we compute where in the empirical mitochondrial heteroplasmy distribution that VRF occurs and express that as a percentile.

Percentiles

Illumina Connected Annotations uses the statistical definition of percentile (indicating the value below which a given percentage of observations in a group of observations falls). Unless the sample's VRF is higher than all the VRFs represented in the distribution, the range will be [0, 1).

Download URL

Unavailable

The original data set is only available internally at Illumina at the moment.

JSON Output

"samples":[
   {
      "genotype":"0/1",
      "variantFrequencies":[
         0.333,
         0.5
      ],
      ],
      "alleleDepths":[
         10,
         20,
         30
      ],
      "heteroplasmyPercentile":[
         23.13,
         12.65
      ]
   }
]
FieldTypeNotes
heteroplasmyPercentilefloat arrayone percentile for each variant frequency (each alternate allele)
- - +
Skip to main content
Version: 3.22

Mitochondrial Heteroplasmy

Overview

Mitochondrial Heteroplasmy is an aggregate population data set that characterizes the amount of heteroplasmy observed for each variant. The latest version of this data set is based on re-processed 1000 Genomes Project data using the Illumina DRAGEN pipeline.

JSON File

Example

{
   "T:C":{
      "ad":[
         1,
         1,
         1,
         1,
         1,
         1
      ],
      "allele_type":"alt",
      "vrf":[
         0.002369668246445498,
         0.0024937655860349127,
         0.0016129032258064516,
         0.0025188916876574307,
         0.0022935779816513763,
         0.002008032128514056
      ],
      "vrf_stats":{
         "kurtosis":38.889891511122556,
         "max":0.0025188916876574307,
         "mean":5.4052190471990743e-05,
         "min":0.0,
         "nobs":246,
         "skewness":6.346664692283075,
         "stdev":0.0003461416264750575,
         "variance":1.1981402557879823e-07
      }
   }
}

Parsing

From the JSON file, we're mainly interested in the following keys:

  • variant (i.e. T:C)
  • ad
  • vrf
  • nobs (number of observations)
Adjusting for null observations

The nobs value indicates how many observations were made. Ideally this would have been represented in the ad and vrf arrays, but it's left as an exercise for the reader.

Binning VRF Data

The vrf (variant read frequency) array in the JSON object above is paired with with the ad array (allele depths) shown above.

The data in the JSON object has a crazy number of significant digits. This means that as the number of samples increase, this array will grow. To make this more future-proof, Illumina Connected Annotations bins everything according to 0.1% increments.

With the binned data, we end up having 775 distinct vrf values in the entire JSON file. This also means that the variant with the largest number of VRFs would originally have 246 entries, but due to binning this will decrease to 143.

Pre-processing the Data

The JSON file is converted into a small TSV file that is embedded in Illumina Connected Annotations. Here is an example of the TSV file:

#CHROM  POS REF ALT VRF_BINS    VRF_COUNTS
chrM    1   G   .   0.981,0.987,0.988,0.989,0.99,0.991,0.992,0.993,0.994,0.995,0.996,0.997,0.998,0.999  1,2,2,4,7,8,11,19,43,60,48,64,499,1736
chrM    2   A   .   0.981,0.987,0.988,0.989,0.99,0.991,0.992,0.993,0.994,0.995,0.996,0.997,0.998,0.999  1,2,2,4,7,8,11,19,43,60,48,64,499,1736

Algorithm

Illumina Connected Annotations will calculate mitochondrial heteroplasmy data for every sample in the VCF. Using the computed VRF for each sample, we compute where in the empirical mitochondrial heteroplasmy distribution that VRF occurs and express that as a percentile.

Percentiles

Illumina Connected Annotations uses the statistical definition of percentile (indicating the value below which a given percentage of observations in a group of observations falls). Unless the sample's VRF is higher than all the VRFs represented in the distribution, the range will be [0, 1).

Download URL

Unavailable

The original data set is only available internally at Illumina at the moment.

JSON Output

"samples":[
   {
      "genotype":"0/1",
      "variantFrequencies":[
         0.333,
         0.5
      ],
      ],
      "alleleDepths":[
         10,
         20,
         30
      ],
      "heteroplasmyPercentile":[
         23.13,
         12.65
      ]
   }
]
FieldTypeNotes
heteroplasmyPercentilefloat arrayone percentile for each variant frequency (each alternate allele)
+ + \ No newline at end of file diff --git a/3.22/data-sources/mitomap-small-variants-json/index.html b/3.22/data-sources/mitomap-small-variants-json/index.html index c87f3787..e2361c5d 100644 --- a/3.22/data-sources/mitomap-small-variants-json/index.html +++ b/3.22/data-sources/mitomap-small-variants-json/index.html @@ -6,13 +6,13 @@ mitomap-small-variants-json | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

mitomap-small-variants-json

"mitomap":[ 
   { 
      "refAllele":"G",
      "altAllele":"A",
      "diseases":[ 
         "Bipolar disorder",
         "Melanoma"
      ],
      "hasHomoplasmy":false,
      "hasHeteroplasmy":true,
      "status":"Reported",
      "clinicalSignificance":"confirmed pathogenic",
      "scorePercentile":83.30,
      "numGenBankFullLengthSeqs":2,
      "pubMedIds":["2316527","6299878","6301949"],
      "isAlleleSpecific":true
   }
]
FieldTypeNotes
refAllelestring
altAllelestring
diseasesstring arrayassociated diseases
hasHomoplasmyboolean
hasHeteroplasmyboolean
statusstringrecord status
clinicalSignificancestringpredicted pathogenicity
scorePercentilefloatMitoTIP score
numGenBankFullLengthSeqsinteger# of GenBank full-length sequences
pubMedIdsstring array
isAlleleSpecificbooleantrue when the current variant alternate allele matches the MITOMAP alternate allele
- - +
Skip to main content
Version: 3.22

mitomap-small-variants-json

"mitomap":[ 
   { 
      "refAllele":"G",
      "altAllele":"A",
      "diseases":[ 
         "Bipolar disorder",
         "Melanoma"
      ],
      "hasHomoplasmy":false,
      "hasHeteroplasmy":true,
      "status":"Reported",
      "clinicalSignificance":"confirmed pathogenic",
      "scorePercentile":83.30,
      "numGenBankFullLengthSeqs":2,
      "pubMedIds":["2316527","6299878","6301949"],
      "isAlleleSpecific":true
   }
]
FieldTypeNotes
refAllelestring
altAllelestring
diseasesstring arrayassociated diseases
hasHomoplasmyboolean
hasHeteroplasmyboolean
statusstringrecord status
clinicalSignificancestringpredicted pathogenicity
scorePercentilefloatMitoTIP score
numGenBankFullLengthSeqsinteger# of GenBank full-length sequences
pubMedIdsstring array
isAlleleSpecificbooleantrue when the current variant alternate allele matches the MITOMAP alternate allele
+ + \ No newline at end of file diff --git a/3.22/data-sources/mitomap-structural-variants-json/index.html b/3.22/data-sources/mitomap-structural-variants-json/index.html index 2b718140..8d1679fb 100644 --- a/3.22/data-sources/mitomap-structural-variants-json/index.html +++ b/3.22/data-sources/mitomap-structural-variants-json/index.html @@ -6,13 +6,13 @@ mitomap-structural-variants-json | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

mitomap-structural-variants-json

"mitomap":[ 
   { 
      "chromosome":"MT",
      "begin":3166,
      "end":14152,
      "variantType":"deletion",
      "reciprocalOverlap":0.18068,
      "annotationOverlap":0.42405
   }
]
FieldTypeNotes
chromosomestring
begininteger
endinteger
variantTypestring array
reciprocalOverlapfloatRange: 0 - 1. Specified up to 5 decimal places
annotationOverlapfloatRange: 0 - 1. Specified up to 5 decimal places
- - +
Skip to main content
Version: 3.22

mitomap-structural-variants-json

"mitomap":[ 
   { 
      "chromosome":"MT",
      "begin":3166,
      "end":14152,
      "variantType":"deletion",
      "reciprocalOverlap":0.18068,
      "annotationOverlap":0.42405
   }
]
FieldTypeNotes
chromosomestring
begininteger
endinteger
variantTypestring array
reciprocalOverlapfloatRange: 0 - 1. Specified up to 5 decimal places
annotationOverlapfloatRange: 0 - 1. Specified up to 5 decimal places
+ + \ No newline at end of file diff --git a/3.22/data-sources/mitomap/index.html b/3.22/data-sources/mitomap/index.html index 03ad95fe..37ac6a9f 100644 --- a/3.22/data-sources/mitomap/index.html +++ b/3.22/data-sources/mitomap/index.html @@ -6,13 +6,13 @@ MITOMAP | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

MITOMAP

Overview

MITOMAP provides a compendium of polymorphisms and mutations in human mitochondrial DNA.

Publication

Lott, M.T., Leipzig, J.N., Derbeneva, O., Xie, H.M., Chalkia, D., Sarmady, M., Procaccio, V., and Wallace, D.C. mtDNA variation and analysis using MITOMAP and MITOMASTER. Current Protocols in Bioinformatics 1(123):1.23.1-26 (2013). http://www.mitomap.org

Scraping HTML Pages

Example

MITOMAP is unique in that it doesn't offer the data in a downloadable format. As a result, the annotation content in Illumina Connected Annotations is scraped from the following MITOMAP pages:

  1. mtDNA Control Region Sequence Variants
  2. mtDNA Coding Region & RNA Sequence Variants
  3. Reported Mitochondrial DNA Base Substitution Diseases: rRNA/tRNA mutations
  4. Reported Mitochondrial DNA Base Substitution Diseases: Coding and Control Region Point Mutations
  5. Reported mtDNA Deletions
  6. mtDNA Simple Insertions

Parsing

Here's what the HTML code looks like:

["582","<a href='/MITOMAP/GenomeLoci#MTTF'>MT-TF</a>","Mitochondrial myopathy","T582C","tRNA Phe","-","+","Reported","<span style='display:inline-block;white-space:nowrap;'><a href='/cgi-bin/mitotip?pos=582&alt=C&quart=2'><u>72.90%</u></a> <i class='fa fa-arrow-up' style='color:orange' aria-hidden='true'></i></span>","0","<a href='/cgi-bin/print_ref_list?refs=90165,91590&title=RNA+Mutation+T582C' target='_blank'>2</a>"],
["583","<a href='/MITOMAP/GenomeLoci#MTTF'>MT-TF</a>","MELAS / MM & EXIT","G583A","tRNA Phe","-","+","Cfrm","<span style='display:inline-block;white-space:nowrap;'><a href='/cgi-bin/mitotip?pos=583&alt=A&quart=0'><u>93.10%</u></a> <i class='fa fa-arrow-up' style='color:red' aria-hidden='true'></i><i class='fa fa-arrow-up' style='color:red' aria-hidden='true'></i><i class='fa fa-arrow-up' style='color:red' aria-hidden='true'></i></span>","0","<a href='/cgi-bin/print_ref_list?refs=2066,90532,91590&title=RNA+Mutation+G583A' target='_blank'>3</a>"],

We're mainly interested in the following columns (numbers indicate the HTML page above):

  • Position1,2,3,4
  • Disease3,4
  • Nucleotide Change1,2
  • Allele3,4
  • Homoplasmy3,4
  • Heteroplasmy3,4
  • Status3,4
  • MitoTIP3,4
  • GB Seqs FL(CR)1,2,3,4
  • Deletion Junction5
  • Insert (nt)6
  • Insert Point (nt)6
  • References/Curated References1,2,3,4
MitoTIP

The MitoTIP information is used to populate the clinicalSignificance and scorePercentile JSON keys. The "frequency alert" entries are skipped since it's not directly relevant to clinical significance.

Left alignment

Many of the variants in MITOMAP have not been normalized. As part of our import procedure, we left align all insertions and deletions.

Variant Enumeration

Sometimes MITOMAP provides data that indicates that multiple values have been observed. Some examples of this are C-C(2-8) and A-AC or ACC. Alternate alleles containing IUPAC ambiguity codes are similarly enumerated.

Inversions

MITOMAP inversions are currently treated as MNVs.

Allele Parsing

The following MITOMAP allele parsing conventions are supported:

  • C123T
  • 16021_16022del
  • 8042del2
  • C9537insC
  • 3902_3908invACCTTGC
  • A-AC or ACC
  • C-C(2-8)
  • 8042delAT

PostgreSQL Dump File

Example

COPY mitomap.reference (id, authors, title, publication, editors, volume, number, pages, date, city, publisher, keywords, abstract, nlmid) FROM stdin;
1       Albring, M., Griffith, J. and Attardi, G.       Association of a protein structure of probable membrane derivation with HeLa cell mitochondrial DNA near its origin of replication      Proceedings of the National Academy of Sciences of the United States of America .       74      4       1348-1352       1977    .       .       Deoxyribonucleoproteins; DNA Replication; DNA, Mitochondrial; Hela Cells; Membrane Proteins; Microscopy, Electron; Molecular Weight; Neoplasm Proteins; Protein Binding Almost all (about 95 percent) of the mitochondrial DNA molecules released by Triton X-100 lysis of HeLa cell mitochondria in the presence of 0.15 M salt are associated with a single protein-containing structure varying in appearance between a 10-20 nm knob and a 100-500 nm membrane-like patch. Analysis by high resolution electron microscopy and by polyacrylamide gel electrophoresis after cleavage of mitochondrial DNA with the endonucleases EcoRI, HindIII, and Hpa II has shown that the protein structure is attached to the DNA in the region of the D-loop, and probably near the origin of mitochondrial DNA replication. The data strongly suggest that HeLa cell mitochondrial DNA is attached in vivo to the inner mitochondrial membrane at or near the origin of replication, and that a membrane fragment of variable size remains associated with the DNA during the isolation. After sodium dodecyl sulfate extraction of mitochondrial DNA, a small 5-10 nm protein is found at the same site on a fraction of the mitochondrial DNA molecules.   266177
2       Anderson, S., Bankier, A.T., Barrell, B.G., de Bruijn, M.H., Coulson, A.R., Drouin, J., Eperon, I.C., Nierlich, D.P., Roe, B.A., Sanger, F., Schreier, P.H., Smith, A.J., Staden, R., Young, I.G.       Sequence and organization of the human mitochondrial genome     Nature  .       290     5806    457-465 1981    .       .       Base Sequence; Codon; DNA Replication; mtDNA; Evolution; Genes, Structural; Human; Nucleic Acid Precursors; Peptide Chain Initiation; Peptide Chain Termination; RNA, Ribosomal; RNA, Transfer; Transcription, Genetic  The complete sequence of the 16,569-base pair human mitochondrial genome is presented. The genes for the 12S and 16S rRNAs, 22 tRNAs, cytochrome c oxidase subunits I, II and III, ATPase subunit 6, cytochrome b and eight other predicted protein coding genes have been located. The sequence shows extreme economy in that the genes have none or only a few noncoding bases between them, and in many cases the termination codons are not coded in the DNA but are created post- transcriptionally by polyadenylation of the mRNAs.       7219534

Parsing

From the PostgreSQL dump file, we're interested in parsing the mapping between reference IDs and the PubMed IDs:

  • id
  • nlmid
Why not use the PostgreSQL file for everything?

Ideally we would use this file for parsing all of our data, but the schema contains 80+ tables and we haven't invested the time yet to see how the tables are linked together to produce the 6 main HTML pages that we're interested in.

Known Issues

Duplicated records

Multiple records describing the same nucleotide change are merged into the same record. If any conflicting information is found (homoplasmy, heteroplasmy, status, clinical significance, score percentile, end coordinate, variant type), an exception is thrown.

  • For diseases and PubMed IDs, we take the union of the values in the duplicated records.
  • For full length GenBank sequences, we take the largest number from each of the duplicated records since it provides the strongest evidence for this variant.
Skipped records

Records that represent an alternate notation of the original variant are skipped. Similarly some variants with confusing alleles (T961delT+ / -C(n)ins) are also skipped.

Download URLs

JSON Output

Small Variants

"mitomap":[ 
   { 
      "refAllele":"G",
      "altAllele":"A",
      "diseases":[ 
         "Bipolar disorder",
         "Melanoma"
      ],
      "hasHomoplasmy":false,
      "hasHeteroplasmy":true,
      "status":"Reported",
      "clinicalSignificance":"confirmed pathogenic",
      "scorePercentile":83.30,
      "numGenBankFullLengthSeqs":2,
      "pubMedIds":["2316527","6299878","6301949"],
      "isAlleleSpecific":true
   }
]
FieldTypeNotes
refAllelestring
altAllelestring
diseasesstring arrayassociated diseases
hasHomoplasmyboolean
hasHeteroplasmyboolean
statusstringrecord status
clinicalSignificancestringpredicted pathogenicity
scorePercentilefloatMitoTIP score
numGenBankFullLengthSeqsinteger# of GenBank full-length sequences
pubMedIdsstring array
isAlleleSpecificbooleantrue when the current variant alternate allele matches the MITOMAP alternate allele

Structural Variants

"mitomap":[ 
   { 
      "chromosome":"MT",
      "begin":3166,
      "end":14152,
      "variantType":"deletion",
      "reciprocalOverlap":0.18068,
      "annotationOverlap":0.42405
   }
]
FieldTypeNotes
chromosomestring
begininteger
endinteger
variantTypestring array
reciprocalOverlapfloatRange: 0 - 1. Specified up to 5 decimal places
annotationOverlapfloatRange: 0 - 1. Specified up to 5 decimal places
- - +
Skip to main content
Version: 3.22

MITOMAP

Overview

MITOMAP provides a compendium of polymorphisms and mutations in human mitochondrial DNA.

Publication

Lott, M.T., Leipzig, J.N., Derbeneva, O., Xie, H.M., Chalkia, D., Sarmady, M., Procaccio, V., and Wallace, D.C. mtDNA variation and analysis using MITOMAP and MITOMASTER. Current Protocols in Bioinformatics 1(123):1.23.1-26 (2013). http://www.mitomap.org

Scraping HTML Pages

Example

MITOMAP is unique in that it doesn't offer the data in a downloadable format. As a result, the annotation content in Illumina Connected Annotations is scraped from the following MITOMAP pages:

  1. mtDNA Control Region Sequence Variants
  2. mtDNA Coding Region & RNA Sequence Variants
  3. Reported Mitochondrial DNA Base Substitution Diseases: rRNA/tRNA mutations
  4. Reported Mitochondrial DNA Base Substitution Diseases: Coding and Control Region Point Mutations
  5. Reported mtDNA Deletions
  6. mtDNA Simple Insertions

Parsing

Here's what the HTML code looks like:

["582","<a href='/MITOMAP/GenomeLoci#MTTF'>MT-TF</a>","Mitochondrial myopathy","T582C","tRNA Phe","-","+","Reported","<span style='display:inline-block;white-space:nowrap;'><a href='/cgi-bin/mitotip?pos=582&alt=C&quart=2'><u>72.90%</u></a> <i class='fa fa-arrow-up' style='color:orange' aria-hidden='true'></i></span>","0","<a href='/cgi-bin/print_ref_list?refs=90165,91590&title=RNA+Mutation+T582C' target='_blank'>2</a>"],
["583","<a href='/MITOMAP/GenomeLoci#MTTF'>MT-TF</a>","MELAS / MM & EXIT","G583A","tRNA Phe","-","+","Cfrm","<span style='display:inline-block;white-space:nowrap;'><a href='/cgi-bin/mitotip?pos=583&alt=A&quart=0'><u>93.10%</u></a> <i class='fa fa-arrow-up' style='color:red' aria-hidden='true'></i><i class='fa fa-arrow-up' style='color:red' aria-hidden='true'></i><i class='fa fa-arrow-up' style='color:red' aria-hidden='true'></i></span>","0","<a href='/cgi-bin/print_ref_list?refs=2066,90532,91590&title=RNA+Mutation+G583A' target='_blank'>3</a>"],

We're mainly interested in the following columns (numbers indicate the HTML page above):

  • Position1,2,3,4
  • Disease3,4
  • Nucleotide Change1,2
  • Allele3,4
  • Homoplasmy3,4
  • Heteroplasmy3,4
  • Status3,4
  • MitoTIP3,4
  • GB Seqs FL(CR)1,2,3,4
  • Deletion Junction5
  • Insert (nt)6
  • Insert Point (nt)6
  • References/Curated References1,2,3,4
MitoTIP

The MitoTIP information is used to populate the clinicalSignificance and scorePercentile JSON keys. The "frequency alert" entries are skipped since it's not directly relevant to clinical significance.

Left alignment

Many of the variants in MITOMAP have not been normalized. As part of our import procedure, we left align all insertions and deletions.

Variant Enumeration

Sometimes MITOMAP provides data that indicates that multiple values have been observed. Some examples of this are C-C(2-8) and A-AC or ACC. Alternate alleles containing IUPAC ambiguity codes are similarly enumerated.

Inversions

MITOMAP inversions are currently treated as MNVs.

Allele Parsing

The following MITOMAP allele parsing conventions are supported:

  • C123T
  • 16021_16022del
  • 8042del2
  • C9537insC
  • 3902_3908invACCTTGC
  • A-AC or ACC
  • C-C(2-8)
  • 8042delAT

PostgreSQL Dump File

Example

COPY mitomap.reference (id, authors, title, publication, editors, volume, number, pages, date, city, publisher, keywords, abstract, nlmid) FROM stdin;
1       Albring, M., Griffith, J. and Attardi, G.       Association of a protein structure of probable membrane derivation with HeLa cell mitochondrial DNA near its origin of replication      Proceedings of the National Academy of Sciences of the United States of America .       74      4       1348-1352       1977    .       .       Deoxyribonucleoproteins; DNA Replication; DNA, Mitochondrial; Hela Cells; Membrane Proteins; Microscopy, Electron; Molecular Weight; Neoplasm Proteins; Protein Binding Almost all (about 95 percent) of the mitochondrial DNA molecules released by Triton X-100 lysis of HeLa cell mitochondria in the presence of 0.15 M salt are associated with a single protein-containing structure varying in appearance between a 10-20 nm knob and a 100-500 nm membrane-like patch. Analysis by high resolution electron microscopy and by polyacrylamide gel electrophoresis after cleavage of mitochondrial DNA with the endonucleases EcoRI, HindIII, and Hpa II has shown that the protein structure is attached to the DNA in the region of the D-loop, and probably near the origin of mitochondrial DNA replication. The data strongly suggest that HeLa cell mitochondrial DNA is attached in vivo to the inner mitochondrial membrane at or near the origin of replication, and that a membrane fragment of variable size remains associated with the DNA during the isolation. After sodium dodecyl sulfate extraction of mitochondrial DNA, a small 5-10 nm protein is found at the same site on a fraction of the mitochondrial DNA molecules.   266177
2       Anderson, S., Bankier, A.T., Barrell, B.G., de Bruijn, M.H., Coulson, A.R., Drouin, J., Eperon, I.C., Nierlich, D.P., Roe, B.A., Sanger, F., Schreier, P.H., Smith, A.J., Staden, R., Young, I.G.       Sequence and organization of the human mitochondrial genome     Nature  .       290     5806    457-465 1981    .       .       Base Sequence; Codon; DNA Replication; mtDNA; Evolution; Genes, Structural; Human; Nucleic Acid Precursors; Peptide Chain Initiation; Peptide Chain Termination; RNA, Ribosomal; RNA, Transfer; Transcription, Genetic  The complete sequence of the 16,569-base pair human mitochondrial genome is presented. The genes for the 12S and 16S rRNAs, 22 tRNAs, cytochrome c oxidase subunits I, II and III, ATPase subunit 6, cytochrome b and eight other predicted protein coding genes have been located. The sequence shows extreme economy in that the genes have none or only a few noncoding bases between them, and in many cases the termination codons are not coded in the DNA but are created post- transcriptionally by polyadenylation of the mRNAs.       7219534

Parsing

From the PostgreSQL dump file, we're interested in parsing the mapping between reference IDs and the PubMed IDs:

  • id
  • nlmid
Why not use the PostgreSQL file for everything?

Ideally we would use this file for parsing all of our data, but the schema contains 80+ tables and we haven't invested the time yet to see how the tables are linked together to produce the 6 main HTML pages that we're interested in.

Known Issues

Duplicated records

Multiple records describing the same nucleotide change are merged into the same record. If any conflicting information is found (homoplasmy, heteroplasmy, status, clinical significance, score percentile, end coordinate, variant type), an exception is thrown.

  • For diseases and PubMed IDs, we take the union of the values in the duplicated records.
  • For full length GenBank sequences, we take the largest number from each of the duplicated records since it provides the strongest evidence for this variant.
Skipped records

Records that represent an alternate notation of the original variant are skipped. Similarly some variants with confusing alleles (T961delT+ / -C(n)ins) are also skipped.

Download URLs

JSON Output

Small Variants

"mitomap":[ 
   { 
      "refAllele":"G",
      "altAllele":"A",
      "diseases":[ 
         "Bipolar disorder",
         "Melanoma"
      ],
      "hasHomoplasmy":false,
      "hasHeteroplasmy":true,
      "status":"Reported",
      "clinicalSignificance":"confirmed pathogenic",
      "scorePercentile":83.30,
      "numGenBankFullLengthSeqs":2,
      "pubMedIds":["2316527","6299878","6301949"],
      "isAlleleSpecific":true
   }
]
FieldTypeNotes
refAllelestring
altAllelestring
diseasesstring arrayassociated diseases
hasHomoplasmyboolean
hasHeteroplasmyboolean
statusstringrecord status
clinicalSignificancestringpredicted pathogenicity
scorePercentilefloatMitoTIP score
numGenBankFullLengthSeqsinteger# of GenBank full-length sequences
pubMedIdsstring array
isAlleleSpecificbooleantrue when the current variant alternate allele matches the MITOMAP alternate allele

Structural Variants

"mitomap":[ 
   { 
      "chromosome":"MT",
      "begin":3166,
      "end":14152,
      "variantType":"deletion",
      "reciprocalOverlap":0.18068,
      "annotationOverlap":0.42405
   }
]
FieldTypeNotes
chromosomestring
begininteger
endinteger
variantTypestring array
reciprocalOverlapfloatRange: 0 - 1. Specified up to 5 decimal places
annotationOverlapfloatRange: 0 - 1. Specified up to 5 decimal places
+ + \ No newline at end of file diff --git a/3.22/data-sources/omim-json/index.html b/3.22/data-sources/omim-json/index.html index 127da168..945ddfd4 100644 --- a/3.22/data-sources/omim-json/index.html +++ b/3.22/data-sources/omim-json/index.html @@ -6,13 +6,13 @@ omim-json | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

omim-json

"omim":[ 
   { 
      "mimNumber":600678,
      "geneName":"MutS, E. coli, homolog of, 6",
      "description":"The transcription factor p53 responds to diverse cellular stresses to regulate target genes that induce cell cycle arrest, apoptosis, senescence, DNA repair, or changes in metabolism. In addition, p53 appears to induce apoptosis through nontranscriptional cytoplasmic processes. In unstressed cells, p53 is kept inactive essentially through the actions of the ubiquitin ligase MDM2, which inhibits p53 transcriptional activity and ubiquitinates p53 to promote its degradation. Numerous posttranslational modifications modulate p53 activity, most notably phosphorylation and acetylation. Several less abundant p53 isoforms also modulate p53 activity. Activity of p53 is ubiquitously lost in human cancer either by mutation of the p53 gene itself or by loss of cell signaling upstream or downstream of p53 (Toledo and Wahl, 2006; Bourdon, 2007; Vousden and Lane, 2007)",
      "phenotypes":[ 
         { 
            "mimNumber":614350,
            "phenotype":"Colorectal cancer, hereditary nonpolyposis, type 5",
            "description":"Hereditary nonpolyposis colorectal cancer type 5 is a cancer predisposition syndrome ...",
            "mapping":"molecular basis of the disorder is known",
            "inheritances":[ 
               "Autosomal dominant"
            ]
         },
         { 
            "mimNumber":608089,
            "phenotype":"Endometrial cancer, familial",
            "mapping":"molecular basis of the disorder is known"
         },
         { 
            "mimNumber":276300,
            "phenotype":"Mismatch repair cancer syndrome",
            "description":"Constitutional mismatch repair deficiency is a rare childhood cancer predisposition syndrome ...",
            "mapping":"molecular basis of the disorder is known",
            "inheritances":[ 
               "Autosomal recessive"
            ],
            "comments"     : [
                "contribute to susceptibility to multifactorial disorders or to susceptibility to infection",
                "unconfirmed or possibly spurious mapping"
            ]
         }
      ]
   }
]
FieldTypeNotes
mimNumberintOMIM ID for gene
geneNamestringgene name
descriptionstring
phenotypesobject arraysee Phenotype entry below

Phenotype

FieldTypeNotes
mimNumberint
phenotypestring
descriptionstring
mappingstringsee possible values below
inheritancestring arraysee possible values below
commentsstring arraysee possible values below

Mapping

  1. disorder was positioned by mapping of the wild type gene
  2. disease phenotype itself was mapped
  3. molecular basis of the disorder is known
  4. disorder is a chromosome deletion or duplication syndrome

Inheritance

  • autosomal recessive
  • autosomal dominant

Comments

  • contributes to the susceptibility to multifactorial disorders
  • variations that lead to apparently abnormal laboratory test values
  • unconfirmed mapping
- - +
Skip to main content
Version: 3.22

omim-json

"omim":[ 
   { 
      "mimNumber":600678,
      "geneName":"MutS, E. coli, homolog of, 6",
      "description":"The transcription factor p53 responds to diverse cellular stresses to regulate target genes that induce cell cycle arrest, apoptosis, senescence, DNA repair, or changes in metabolism. In addition, p53 appears to induce apoptosis through nontranscriptional cytoplasmic processes. In unstressed cells, p53 is kept inactive essentially through the actions of the ubiquitin ligase MDM2, which inhibits p53 transcriptional activity and ubiquitinates p53 to promote its degradation. Numerous posttranslational modifications modulate p53 activity, most notably phosphorylation and acetylation. Several less abundant p53 isoforms also modulate p53 activity. Activity of p53 is ubiquitously lost in human cancer either by mutation of the p53 gene itself or by loss of cell signaling upstream or downstream of p53 (Toledo and Wahl, 2006; Bourdon, 2007; Vousden and Lane, 2007)",
      "phenotypes":[ 
         { 
            "mimNumber":614350,
            "phenotype":"Colorectal cancer, hereditary nonpolyposis, type 5",
            "description":"Hereditary nonpolyposis colorectal cancer type 5 is a cancer predisposition syndrome ...",
            "mapping":"molecular basis of the disorder is known",
            "inheritances":[ 
               "Autosomal dominant"
            ]
         },
         { 
            "mimNumber":608089,
            "phenotype":"Endometrial cancer, familial",
            "mapping":"molecular basis of the disorder is known"
         },
         { 
            "mimNumber":276300,
            "phenotype":"Mismatch repair cancer syndrome",
            "description":"Constitutional mismatch repair deficiency is a rare childhood cancer predisposition syndrome ...",
            "mapping":"molecular basis of the disorder is known",
            "inheritances":[ 
               "Autosomal recessive"
            ],
            "comments"     : [
                "contribute to susceptibility to multifactorial disorders or to susceptibility to infection",
                "unconfirmed or possibly spurious mapping"
            ]
         }
      ]
   }
]
FieldTypeNotes
mimNumberintOMIM ID for gene
geneNamestringgene name
descriptionstring
phenotypesobject arraysee Phenotype entry below

Phenotype

FieldTypeNotes
mimNumberint
phenotypestring
descriptionstring
mappingstringsee possible values below
inheritancestring arraysee possible values below
commentsstring arraysee possible values below

Mapping

  1. disorder was positioned by mapping of the wild type gene
  2. disease phenotype itself was mapped
  3. molecular basis of the disorder is known
  4. disorder is a chromosome deletion or duplication syndrome

Inheritance

  • autosomal recessive
  • autosomal dominant

Comments

  • contributes to the susceptibility to multifactorial disorders
  • variations that lead to apparently abnormal laboratory test values
  • unconfirmed mapping
+ + \ No newline at end of file diff --git a/3.22/data-sources/omim/index.html b/3.22/data-sources/omim/index.html index 60bb3b2e..a05ae959 100644 --- a/3.22/data-sources/omim/index.html +++ b/3.22/data-sources/omim/index.html @@ -6,8 +6,8 @@ OMIM | IlluminaConnectedAnnotations - - + +
@@ -16,8 +16,8 @@ 3 to molecular basis of the disorder is known
4 to disorder is a chromosome deletion or duplication syndrome

Phenotype character to comment

? to unconfirmed or possibly spurious mapping
[/] to nondiseases
-{/} to contribute to susceptibility to multifactorial disorders or to susceptibility to infection

There are different types of link in the OMIM description section. For example, in above JSON response, we have the description of MIM entry 100640:

The ALDH1A1 gene encodes a liver cytosolic isoform of acetaldehyde dehydrogenase ({EC 1.2.1.3}), an enzyme involved in the major pathway of alcohol metabolism after alcohol dehydrogenase (ADH, see {103700}). See also liver mitochondrial ALDH2 ({100650}), variation in which has been implicated in different responses to alcohol ingestion.\n\nALDH1 is associated with a low Km for NAD, a high Km for acetaldehyde, and is strongly inactivated by disulfiram. ALDH2 is associated with a high Km for NAD, and low Km for acetaldehyde, and is insensitive to inhibition by disulfiram ({4:Hsu et al., 1985}).

As the descriptions will be shown as plain text, we remove the curry brackets surrounding links and try to make the text still readable with minimal modifications. Briefly:

Here is a list of examples about how the description section supposed to be processed:

Original textProcessed text
({516030}, {516040}, and {516050})
(e.g., D1, {168461}; D2, {123833}; D3, {123834})(e.g., D1; D2; D3)
(desmocollins; see DSC2, {125645})(desmocollins; see DSC2)
(e.g., see {102700}, {300755})
(ADH, see {103700}). See also liver mitochondrial ALDH2 ({100650})(ADH). See also liver mitochondrial ALDH2
(see, e.g., CACNA1A; {601011})(see, e.g., CACNA1A)
(e.g., GSTA1; {138359}), mu (e.g., {138350})(e.g., GSTA1), mu
(NFKB; see {164011})(NFKB)
(see ISGF3G, {147574})(see ISGF3G)
(DCK; {EC 2.7.1.74}; {125450})(DCK; EC 2.7.1.74)

JSON output

"omim":[ 
   { 
      "mimNumber":600678,
      "geneName":"MutS, E. coli, homolog of, 6",
      "description":"The transcription factor p53 responds to diverse cellular stresses to regulate target genes that induce cell cycle arrest, apoptosis, senescence, DNA repair, or changes in metabolism. In addition, p53 appears to induce apoptosis through nontranscriptional cytoplasmic processes. In unstressed cells, p53 is kept inactive essentially through the actions of the ubiquitin ligase MDM2, which inhibits p53 transcriptional activity and ubiquitinates p53 to promote its degradation. Numerous posttranslational modifications modulate p53 activity, most notably phosphorylation and acetylation. Several less abundant p53 isoforms also modulate p53 activity. Activity of p53 is ubiquitously lost in human cancer either by mutation of the p53 gene itself or by loss of cell signaling upstream or downstream of p53 (Toledo and Wahl, 2006; Bourdon, 2007; Vousden and Lane, 2007)",
      "phenotypes":[ 
         { 
            "mimNumber":614350,
            "phenotype":"Colorectal cancer, hereditary nonpolyposis, type 5",
            "description":"Hereditary nonpolyposis colorectal cancer type 5 is a cancer predisposition syndrome ...",
            "mapping":"molecular basis of the disorder is known",
            "inheritances":[ 
               "Autosomal dominant"
            ]
         },
         { 
            "mimNumber":608089,
            "phenotype":"Endometrial cancer, familial",
            "mapping":"molecular basis of the disorder is known"
         },
         { 
            "mimNumber":276300,
            "phenotype":"Mismatch repair cancer syndrome",
            "description":"Constitutional mismatch repair deficiency is a rare childhood cancer predisposition syndrome ...",
            "mapping":"molecular basis of the disorder is known",
            "inheritances":[ 
               "Autosomal recessive"
            ],
            "comments"     : [
                "contribute to susceptibility to multifactorial disorders or to susceptibility to infection",
                "unconfirmed or possibly spurious mapping"
            ]
         }
      ]
   }
]
FieldTypeNotes
mimNumberintOMIM ID for gene
geneNamestringgene name
descriptionstring
phenotypesobject arraysee Phenotype entry below

Phenotype

FieldTypeNotes
mimNumberint
phenotypestring
descriptionstring
mappingstringsee possible values below
inheritancestring arraysee possible values below
commentsstring arraysee possible values below

Mapping

  1. disorder was positioned by mapping of the wild type gene
  2. disease phenotype itself was mapped
  3. molecular basis of the disorder is known
  4. disorder is a chromosome deletion or duplication syndrome

Inheritance

Comments

Building the supplementary files

The first step in builing the OMIM .nga files is to use the SAUtils command's subcommand downloadOMIM to download the necessary data. In order to download the data the user must possess an API key obtained from OMIM. This key has to be set as the environment variable OmimApiKey.

export OmimApiKey=<users-omim-api-key>
SAUtils.dll downloadOMIM
---------------------------------------------------------------------------
SAUtils                                             (c) 2023 Illumina, Inc.
Stromberg, Roy, Platzer, Siddiqui, Ouyang, et al         3.21.0-0-gd2a0e953
---------------------------------------------------------------------------

USAGE: dotnet SAUtils.dll downloadomim [options]
Download the OMIM gene annotation data

OPTIONS:
      --cache, -c <directory>
                             input cache directory
      --ref, -r <filename>   input reference filename
      --out, -o <VALUE>      output directory
      --help, -h             displays the help menu
      --version, -v          displays the version

dotnet SAUtils.dll downloadOMIM --ref References/7/Homo_sapiens.GRCh38.Nirvana.dat --uga Cache/ --out ExternalDataSources/OMIM/2021-06-14
---------------------------------------------------------------------------
SAUtils                                             (c) 2023 Illumina, Inc.
Stromberg, Roy, Platzer, Siddiqui, Ouyang, et al         3.21.0-0-gd2a0e953
---------------------------------------------------------------------------

Gene Symbol Update Statistics
============================================
{
  "NumGeneSymbolsUpToDate": 16788,
  "NumGeneSymbolsUpdated": 95,
  "NumGenesWhereBothIdsAreNull": 0,
  "NumGeneSymbolsNotInCache": 106,
  "NumResolvedGeneSymbolConflicts": 15,
  "NumUnresolvedGeneSymbolConflicts": 0
}

Time: 00:04:08.9

Once the download has succeeded, the nga files can be produced using the SAUtils command's subcommand omim.

dotnet SAUtils.dll omim
---------------------------------------------------------------------------
SAUtils                                             (c) 2023 Illumina, Inc.
Stromberg, Roy, Platzer, Siddiqui, Ouyang, et al         3.21.0-0-gd2a0e953
---------------------------------------------------------------------------

USAGE: dotnet SAUtils.dll omim [options]
Creates a gene annotation database from OMIM data

OPTIONS:
      --m2g, -m <VALUE>      MimToGeneSymbol tsv file
      --json, -j <VALUE>     OMIM entry json file
      --out, -o <VALUE>      output directory
      --help, -h             displays the help menu
      --version, -v          displays the version


dotnet SAUtils.dll omim --m2g ExternalDataSources/OMIM/2021-06-14/MimToGeneSymbol.tsv --json ExternalDataSources/OMIM/2021-06-14/MimEntries.json.gz --out SupplementaryDatabase/63/
---------------------------------------------------------------------------
SAUtils                                             (c) 2023 Illumina, Inc.
Stromberg, Roy, Platzer, Siddiqui, Ouyang, et al         3.21.0-0-gd2a0e953
---------------------------------------------------------------------------


Time: 00:00:04.5
- - +{/} to contribute to susceptibility to multifactorial disorders or to susceptibility to infection

There are different types of link in the OMIM description section. For example, in above JSON response, we have the description of MIM entry 100640:

The ALDH1A1 gene encodes a liver cytosolic isoform of acetaldehyde dehydrogenase ({EC 1.2.1.3}), an enzyme involved in the major pathway of alcohol metabolism after alcohol dehydrogenase (ADH, see {103700}). See also liver mitochondrial ALDH2 ({100650}), variation in which has been implicated in different responses to alcohol ingestion.\n\nALDH1 is associated with a low Km for NAD, a high Km for acetaldehyde, and is strongly inactivated by disulfiram. ALDH2 is associated with a high Km for NAD, and low Km for acetaldehyde, and is insensitive to inhibition by disulfiram ({4:Hsu et al., 1985}).

As the descriptions will be shown as plain text, we remove the curry brackets surrounding links and try to make the text still readable with minimal modifications. Briefly:

Here is a list of examples about how the description section supposed to be processed:

Original textProcessed text
({516030}, {516040}, and {516050})
(e.g., D1, {168461}; D2, {123833}; D3, {123834})(e.g., D1; D2; D3)
(desmocollins; see DSC2, {125645})(desmocollins; see DSC2)
(e.g., see {102700}, {300755})
(ADH, see {103700}). See also liver mitochondrial ALDH2 ({100650})(ADH). See also liver mitochondrial ALDH2
(see, e.g., CACNA1A; {601011})(see, e.g., CACNA1A)
(e.g., GSTA1; {138359}), mu (e.g., {138350})(e.g., GSTA1), mu
(NFKB; see {164011})(NFKB)
(see ISGF3G, {147574})(see ISGF3G)
(DCK; {EC 2.7.1.74}; {125450})(DCK; EC 2.7.1.74)

JSON output

"omim":[ 
   { 
      "mimNumber":600678,
      "geneName":"MutS, E. coli, homolog of, 6",
      "description":"The transcription factor p53 responds to diverse cellular stresses to regulate target genes that induce cell cycle arrest, apoptosis, senescence, DNA repair, or changes in metabolism. In addition, p53 appears to induce apoptosis through nontranscriptional cytoplasmic processes. In unstressed cells, p53 is kept inactive essentially through the actions of the ubiquitin ligase MDM2, which inhibits p53 transcriptional activity and ubiquitinates p53 to promote its degradation. Numerous posttranslational modifications modulate p53 activity, most notably phosphorylation and acetylation. Several less abundant p53 isoforms also modulate p53 activity. Activity of p53 is ubiquitously lost in human cancer either by mutation of the p53 gene itself or by loss of cell signaling upstream or downstream of p53 (Toledo and Wahl, 2006; Bourdon, 2007; Vousden and Lane, 2007)",
      "phenotypes":[ 
         { 
            "mimNumber":614350,
            "phenotype":"Colorectal cancer, hereditary nonpolyposis, type 5",
            "description":"Hereditary nonpolyposis colorectal cancer type 5 is a cancer predisposition syndrome ...",
            "mapping":"molecular basis of the disorder is known",
            "inheritances":[ 
               "Autosomal dominant"
            ]
         },
         { 
            "mimNumber":608089,
            "phenotype":"Endometrial cancer, familial",
            "mapping":"molecular basis of the disorder is known"
         },
         { 
            "mimNumber":276300,
            "phenotype":"Mismatch repair cancer syndrome",
            "description":"Constitutional mismatch repair deficiency is a rare childhood cancer predisposition syndrome ...",
            "mapping":"molecular basis of the disorder is known",
            "inheritances":[ 
               "Autosomal recessive"
            ],
            "comments"     : [
                "contribute to susceptibility to multifactorial disorders or to susceptibility to infection",
                "unconfirmed or possibly spurious mapping"
            ]
         }
      ]
   }
]
FieldTypeNotes
mimNumberintOMIM ID for gene
geneNamestringgene name
descriptionstring
phenotypesobject arraysee Phenotype entry below

Phenotype

FieldTypeNotes
mimNumberint
phenotypestring
descriptionstring
mappingstringsee possible values below
inheritancestring arraysee possible values below
commentsstring arraysee possible values below

Mapping

  1. disorder was positioned by mapping of the wild type gene
  2. disease phenotype itself was mapped
  3. molecular basis of the disorder is known
  4. disorder is a chromosome deletion or duplication syndrome

Inheritance

Comments

Building the supplementary files

The first step in builing the OMIM .nga files is to use the SAUtils command's subcommand downloadOMIM to download the necessary data. In order to download the data the user must possess an API key obtained from OMIM. This key has to be set as the environment variable OmimApiKey.

export OmimApiKey=<users-omim-api-key>
SAUtils.dll downloadOMIM
---------------------------------------------------------------------------
SAUtils                                             (c) 2023 Illumina, Inc.
Stromberg, Roy, Platzer, Siddiqui, Ouyang, et al         3.21.0-0-gd2a0e953
---------------------------------------------------------------------------

USAGE: dotnet SAUtils.dll downloadomim [options]
Download the OMIM gene annotation data

OPTIONS:
      --cache, -c <directory>
                             input cache directory
      --ref, -r <filename>   input reference filename
      --out, -o <VALUE>      output directory
      --help, -h             displays the help menu
      --version, -v          displays the version

dotnet SAUtils.dll downloadOMIM --ref References/7/Homo_sapiens.GRCh38.Nirvana.dat --uga Cache/ --out ExternalDataSources/OMIM/2021-06-14
---------------------------------------------------------------------------
SAUtils                                             (c) 2023 Illumina, Inc.
Stromberg, Roy, Platzer, Siddiqui, Ouyang, et al         3.21.0-0-gd2a0e953
---------------------------------------------------------------------------

Gene Symbol Update Statistics
============================================
{
  "NumGeneSymbolsUpToDate": 16788,
  "NumGeneSymbolsUpdated": 95,
  "NumGenesWhereBothIdsAreNull": 0,
  "NumGeneSymbolsNotInCache": 106,
  "NumResolvedGeneSymbolConflicts": 15,
  "NumUnresolvedGeneSymbolConflicts": 0
}

Time: 00:04:08.9

Once the download has succeeded, the nga files can be produced using the SAUtils command's subcommand omim.

dotnet SAUtils.dll omim
---------------------------------------------------------------------------
SAUtils                                             (c) 2023 Illumina, Inc.
Stromberg, Roy, Platzer, Siddiqui, Ouyang, et al         3.21.0-0-gd2a0e953
---------------------------------------------------------------------------

USAGE: dotnet SAUtils.dll omim [options]
Creates a gene annotation database from OMIM data

OPTIONS:
      --m2g, -m <VALUE>      MimToGeneSymbol tsv file
      --json, -j <VALUE>     OMIM entry json file
      --out, -o <VALUE>      output directory
      --help, -h             displays the help menu
      --version, -v          displays the version


dotnet SAUtils.dll omim --m2g ExternalDataSources/OMIM/2021-06-14/MimToGeneSymbol.tsv --json ExternalDataSources/OMIM/2021-06-14/MimEntries.json.gz --out SupplementaryDatabase/63/
---------------------------------------------------------------------------
SAUtils                                             (c) 2023 Illumina, Inc.
Stromberg, Roy, Platzer, Siddiqui, Ouyang, et al         3.21.0-0-gd2a0e953
---------------------------------------------------------------------------


Time: 00:00:04.5
+ + \ No newline at end of file diff --git a/3.22/data-sources/phylop-json/index.html b/3.22/data-sources/phylop-json/index.html index 62d09f2a..dde78a8b 100644 --- a/3.22/data-sources/phylop-json/index.html +++ b/3.22/data-sources/phylop-json/index.html @@ -6,13 +6,13 @@ phylop-json | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

phylop-json

"variants":[
   {
      "vid":"2:48010488:A",
      "chromosome":"chr2",
      "begin":48010488,
      "end":48010488,
      "refAllele":"G",
      "altAllele":"A",
      "variantType":"SNV",
      "phylopScore":0.459
  }
]
FieldTypeNotes
phylopScorefloatrange: -14.08 to 6.424
- - +
Skip to main content
Version: 3.22

phylop-json

"variants":[
   {
      "vid":"2:48010488:A",
      "chromosome":"chr2",
      "begin":48010488,
      "end":48010488,
      "refAllele":"G",
      "altAllele":"A",
      "variantType":"SNV",
      "phylopScore":0.459
  }
]
FieldTypeNotes
phylopScorefloatrange: -14.08 to 6.424
+ + \ No newline at end of file diff --git a/3.22/data-sources/phylop/index.html b/3.22/data-sources/phylop/index.html index c1b437c7..00dba3ea 100644 --- a/3.22/data-sources/phylop/index.html +++ b/3.22/data-sources/phylop/index.html @@ -6,13 +6,13 @@ PhyloP | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

PhyloP

Overview

PhyloP (phylogenetic p-values) conservation scores are obtained from the [PHAST package] (http://compgen.bscb.cornell.edu/phast/) for multiple alignments of vertebrate genomes to the human genome. For GRCh38, the multiple alignments are against 19 mammals and for GRCh37, it is against 45 vertebrate genomes.

Publication

Siepel A, Bejerano G, Pedersen JS, Hinrichs AS, Hou M, Rosenbloom K, Clawson H, Spieth J, Hillier LW, Richards S, et al. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res. 2005 Aug;15(8):1034-50. (http://www.genome.org/cgi/doi/10.1101/gr.3715005)

WigFix File

The data is provided in WigFix files which is a text file that provides conservation scores for contiguous intervals in the following format:

fixedStep chrom=chr1 start=10918 step=1
0.064
0.058
0.064
0.058
0.064
0.064
fixedStep chrom=chr1 start=34045 step=1
0.111
0.100
0.111
0.111
0.100
0.111
0.111
0.111
0.100
0.111
-1.636

We convert them to binary files with indexes for fast query. Note that these are scores for genomic positions and are reported only for SNVs.

Download URL

GRCh37: http://hgdownload.cse.ucsc.edu/goldenpath/hg19/phyloP46way/vertebrate/

GRCh38: http://hgdownload.cse.ucsc.edu/goldenPath/hg38/phyloP20way/

JSON Output

Unlike other supplemetary datasources, phyloP scores are reported in the variants section.

"variants":[
   {
      "vid":"2:48010488:A",
      "chromosome":"chr2",
      "begin":48010488,
      "end":48010488,
      "refAllele":"G",
      "altAllele":"A",
      "variantType":"SNV",
      "phylopScore":0.459
  }
]
FieldTypeNotes
phylopScorefloatrange: -14.08 to 6.424
- - +
Skip to main content
Version: 3.22

PhyloP

Overview

PhyloP (phylogenetic p-values) conservation scores are obtained from the [PHAST package] (http://compgen.bscb.cornell.edu/phast/) for multiple alignments of vertebrate genomes to the human genome. For GRCh38, the multiple alignments are against 19 mammals and for GRCh37, it is against 45 vertebrate genomes.

Publication

Siepel A, Bejerano G, Pedersen JS, Hinrichs AS, Hou M, Rosenbloom K, Clawson H, Spieth J, Hillier LW, Richards S, et al. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res. 2005 Aug;15(8):1034-50. (http://www.genome.org/cgi/doi/10.1101/gr.3715005)

WigFix File

The data is provided in WigFix files which is a text file that provides conservation scores for contiguous intervals in the following format:

fixedStep chrom=chr1 start=10918 step=1
0.064
0.058
0.064
0.058
0.064
0.064
fixedStep chrom=chr1 start=34045 step=1
0.111
0.100
0.111
0.111
0.100
0.111
0.111
0.111
0.100
0.111
-1.636

We convert them to binary files with indexes for fast query. Note that these are scores for genomic positions and are reported only for SNVs.

Download URL

GRCh37: http://hgdownload.cse.ucsc.edu/goldenpath/hg19/phyloP46way/vertebrate/

GRCh38: http://hgdownload.cse.ucsc.edu/goldenPath/hg38/phyloP20way/

JSON Output

Unlike other supplemetary datasources, phyloP scores are reported in the variants section.

"variants":[
   {
      "vid":"2:48010488:A",
      "chromosome":"chr2",
      "begin":48010488,
      "end":48010488,
      "refAllele":"G",
      "altAllele":"A",
      "variantType":"SNV",
      "phylopScore":0.459
  }
]
FieldTypeNotes
phylopScorefloatrange: -14.08 to 6.424
+ + \ No newline at end of file diff --git a/3.22/data-sources/primate-ai-json/index.html b/3.22/data-sources/primate-ai-json/index.html index ce3823c3..50ccf37b 100644 --- a/3.22/data-sources/primate-ai-json/index.html +++ b/3.22/data-sources/primate-ai-json/index.html @@ -6,13 +6,13 @@ primate-ai-json | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

primate-ai-json

GRCh38

"primateAI-3D": [
  {
    "aminoAcidPosition": 2,
    "refAminoAcid": "V",
    "altAminoAcid": "M",
    "score": 0.616944,
    "scorePercentile": 0.52,
    "ensemblTranscriptId": "ENST00000335137.4",
    "refSeqTranscriptId": "NM_001005484.1"
  }
]
FieldTypeNotes
aminoAcidPositionintAmino Acid Position (1-based)
refAminoAcidstringReference Amino Acid
altAminoAcidstringAlternate Amino Acid
ensemblTranscriptIdstringTranscript ID (Ensembl)
refSeqTranscriptIdstringTranscript ID (RefSeq)
scorePercentilefloatrange: 0 - 1.0
scorefloatrange: 0 - 1.0

GRCh37

"primateAI": [
   {
      "hgnc":"TP53",
      "scorePercentile":0.3,
   }
]
FieldTypeNotes
hgncstringHGNC Gene Symbol
scorePercentilefloatrange: 0 - 1.0
- - +
Skip to main content
Version: 3.22

primate-ai-json

GRCh38

"primateAI-3D": [
  {
    "aminoAcidPosition": 2,
    "refAminoAcid": "V",
    "altAminoAcid": "M",
    "score": 0.616944,
    "scorePercentile": 0.52,
    "ensemblTranscriptId": "ENST00000335137.4",
    "refSeqTranscriptId": "NM_001005484.1"
  }
]
FieldTypeNotes
aminoAcidPositionintAmino Acid Position (1-based)
refAminoAcidstringReference Amino Acid
altAminoAcidstringAlternate Amino Acid
ensemblTranscriptIdstringTranscript ID (Ensembl)
refSeqTranscriptIdstringTranscript ID (RefSeq)
scorePercentilefloatrange: 0 - 1.0
scorefloatrange: 0 - 1.0

GRCh37

"primateAI": [
   {
      "hgnc":"TP53",
      "scorePercentile":0.3,
   }
]
FieldTypeNotes
hgncstringHGNC Gene Symbol
scorePercentilefloatrange: 0 - 1.0
+ + \ No newline at end of file diff --git a/3.22/data-sources/primate-ai/index.html b/3.22/data-sources/primate-ai/index.html index e8d3e512..1fccc5e4 100644 --- a/3.22/data-sources/primate-ai/index.html +++ b/3.22/data-sources/primate-ai/index.html @@ -6,8 +6,8 @@ Primate AI | IlluminaConnectedAnnotations - - + +
@@ -15,8 +15,8 @@ The model's innovative use of primate sequencing and structural data offers promising insights into variant interpretation and disease gene identification. The predictive score range between 0 and 1, with 0 being benign and 1 being most pathogenic.

For more details, refer to these publications:

Publication
  1. Hong Gao et al. ,The landscape of tolerated genetic variation in humans and primates. Science 380, eabn8153 (2023). https://doi.org/10.1126/science.abn8197
  2. Sundaram, L., Gao, H., Padigepati, S.R. et al. Predicting the clinical impact of human mutation with deep neural networks. Nat Genet 50, 1161–1170 (2018). https://doi.org/10.1038/s41588-018-0167-z
Professional data source

This is a Professional data source and is not available freely. Please contact annotation_support@illumina.com if you would like to obtain it.

Primate AI is available in two versions based on assembly:

  1. Primate AI 3D: Only available for GRCh38
  2. Primate AI: Only available for GRCh37

Both have different file structures, and information. Therefore, they are handled separately:

Primate AI 3D: GRCh38

Parsing

CSV File

,chr,pos,non_flipped_ref,non_flipped_alt,gene_name,change_position_1based,ref_aa,alt_aa,score_PAI3D,percentile_PAI3D,refseq
0,chr1,69094,G,A,ENST00000335137.4,2,V,M,0.6169436463713646,0.5200308441794135,NM_001005484.1
1,chr1,69094,G,C,ENST00000335137.4,2,V,L,0.5557043975591658,0.4271457250214688,NM_001005484.1
2,chr1,69094,G,T,ENST00000335137.4,2,V,L,0.5557043975591658,0.4271457391722522,NM_001005484.1

From the CSV file, all columns are parsed:

The fields gene_name and refseq define the Ensembl and RefSeq transcript IDs respectively. These transcripts are passed as-is and some of them might be unrecognized/deprecated by RefSeq/Ensembl.

Parsing Command

dotnet SAUtils.dll \
PrimateAi \
--r "${References}/Homo_sapiens.GRCh38.Nirvana.dat" \
--i "${ExternalDataSources}/PrimateAI/3D/PAI3D_wholeProteome_23_04_11.percentiles.pkg.refseq.csv.gz" \
--o "${SaUtilsOutput]"

Known Issues

Known Issues

Some transcript IDs defined in the data file are obsolete, retired, or updated. -They are not removed or modified by Illumina Connected Annotations, and are passed as-is from the PrimateAI-3D data source.

Example:

ENST00000643905.1 transcript is retired according to Ensembl

NM_182838.2 transcript is removed because it is a pseudo-gene according to RefSeq

Download URL

https://primad.basespace.illumina.com/

Primate AI: GRCh37

Parsing

TSV File

chr pos ref alt refAA   altAA   strand_1pos_0neg    trinucleotide_context   UCSC_gene   ExAC_coverage   primateDL_score
chr10   1046704 C   T   R   C   1   CCG uc001ift.3  45.49   0.849114537239
chr10   1046704 C   G   R   G   1   CCG uc001ift.3  45.49   0.795686006546

From the TSV file, we're mainly interested in the following columns:

We also use UCSC_gene to filter out variants that don't have matching gene models in Illumina Connected Annotations.

Pre-processing

Converting UCSC IDs

Primate AI only provides UCSC IDs. As an initial pre-processing step, we'll need to convert these to either Entrez or Ensembl Gene IDs.

The following queries are used to download the conversions from UCSC:

mysql -h genome-mysql.soe.ucsc.edu -u genome -A -P 3306 \
      -e "select * FROM knownToLocusLink;" hg19 > ucsc_locuslink.tsv

mysql -h genome-mysql.soe.ucsc.edu -u genome -A -P 3306 \
      -e "select knownToEnsembl.name, knownToEnsembl.value, ensGene.name2 FROM knownToEnsembl, ensGene WHERE knownToEnsembl.value = ensGene.name;" \
      hg19 > ucsc_ensembl.tsv

Running the Pre-Processor

The Primate AI pre-processor can be run as follows:

dotnet PrimateAiPreProcessor.dll UGA_develop.tsv PrimateAI_scores_v0.2.tsv.gz \
     ucsc_locuslink.tsv ucsc_ensembl.tsv PrimateAI_0.2_GRCh37.tsv.gz

During conversion, 0.5% of the UCSC Ids cannot be converted to either Entrez or Ensembl gene IDs. Once the gene IDs have been acquired, we check to see which are available in Illumina Connected Annotations.

The following Entrez Gene IDs were not found:

399753
401980
504189
504191
100293534

Here is the output from the pre-processor:

- loading UCSC to Entrez Gene ID dictionary... 73,432 genes loaded.
- loading UCSC to Ensembl Gene ID dictionary... 76,178 genes loaded.
- loading UGA gene ID to gene dictionary... 103,277 genes loaded.
- parsing Primate AI variants... 70,121,953 variants parsed.

# variants with unknown gene ID: 27,253 / 70,121,953
# genes with unknown gene ID:    109 / 19,614

# variants not in UGA: 2,036 / 70,121,953
# genes not in UGA:    6 / 19,614

Known Issues

Known Issues

The Primate AI data set provides raw scores, but the scores are biased according to gene context. I.e. a 0.4 means something different in TP53 than it does in KRAS.

As a result, the Primate AI team provided guidance on aggregating these scores and presenting them as percentiles with respect to the associated gene. According to their research, the 25th percentile is a good proxy for benign variants and the 75th percentile is a good proxy for pathogenic variants.

Download URL

https://basespace.illumina.com/s/cPgCSmecvhb4

JSON Output

GRCh38

"primateAI-3D": [
  {
    "aminoAcidPosition": 2,
    "refAminoAcid": "V",
    "altAminoAcid": "M",
    "score": 0.616944,
    "scorePercentile": 0.52,
    "ensemblTranscriptId": "ENST00000335137.4",
    "refSeqTranscriptId": "NM_001005484.1"
  }
]
FieldTypeNotes
aminoAcidPositionintAmino Acid Position (1-based)
refAminoAcidstringReference Amino Acid
altAminoAcidstringAlternate Amino Acid
ensemblTranscriptIdstringTranscript ID (Ensembl)
refSeqTranscriptIdstringTranscript ID (RefSeq)
scorePercentilefloatrange: 0 - 1.0
scorefloatrange: 0 - 1.0

GRCh37

"primateAI": [
   {
      "hgnc":"TP53",
      "scorePercentile":0.3,
   }
]
FieldTypeNotes
hgncstringHGNC Gene Symbol
scorePercentilefloatrange: 0 - 1.0
- - +They are not removed or modified by Illumina Connected Annotations, and are passed as-is from the PrimateAI-3D data source.

Example:

ENST00000643905.1 transcript is retired according to Ensembl

NM_182838.2 transcript is removed because it is a pseudo-gene according to RefSeq

Download URL

https://primad.basespace.illumina.com/

Primate AI: GRCh37

Parsing

TSV File

chr pos ref alt refAA   altAA   strand_1pos_0neg    trinucleotide_context   UCSC_gene   ExAC_coverage   primateDL_score
chr10   1046704 C   T   R   C   1   CCG uc001ift.3  45.49   0.849114537239
chr10   1046704 C   G   R   G   1   CCG uc001ift.3  45.49   0.795686006546

From the TSV file, we're mainly interested in the following columns:

We also use UCSC_gene to filter out variants that don't have matching gene models in Illumina Connected Annotations.

Pre-processing

Converting UCSC IDs

Primate AI only provides UCSC IDs. As an initial pre-processing step, we'll need to convert these to either Entrez or Ensembl Gene IDs.

The following queries are used to download the conversions from UCSC:

mysql -h genome-mysql.soe.ucsc.edu -u genome -A -P 3306 \
      -e "select * FROM knownToLocusLink;" hg19 > ucsc_locuslink.tsv

mysql -h genome-mysql.soe.ucsc.edu -u genome -A -P 3306 \
      -e "select knownToEnsembl.name, knownToEnsembl.value, ensGene.name2 FROM knownToEnsembl, ensGene WHERE knownToEnsembl.value = ensGene.name;" \
      hg19 > ucsc_ensembl.tsv

Running the Pre-Processor

The Primate AI pre-processor can be run as follows:

dotnet PrimateAiPreProcessor.dll UGA_develop.tsv PrimateAI_scores_v0.2.tsv.gz \
     ucsc_locuslink.tsv ucsc_ensembl.tsv PrimateAI_0.2_GRCh37.tsv.gz

During conversion, 0.5% of the UCSC Ids cannot be converted to either Entrez or Ensembl gene IDs. Once the gene IDs have been acquired, we check to see which are available in Illumina Connected Annotations.

The following Entrez Gene IDs were not found:

399753
401980
504189
504191
100293534

Here is the output from the pre-processor:

- loading UCSC to Entrez Gene ID dictionary... 73,432 genes loaded.
- loading UCSC to Ensembl Gene ID dictionary... 76,178 genes loaded.
- loading UGA gene ID to gene dictionary... 103,277 genes loaded.
- parsing Primate AI variants... 70,121,953 variants parsed.

# variants with unknown gene ID: 27,253 / 70,121,953
# genes with unknown gene ID:    109 / 19,614

# variants not in UGA: 2,036 / 70,121,953
# genes not in UGA:    6 / 19,614

Known Issues

Known Issues

The Primate AI data set provides raw scores, but the scores are biased according to gene context. I.e. a 0.4 means something different in TP53 than it does in KRAS.

As a result, the Primate AI team provided guidance on aggregating these scores and presenting them as percentiles with respect to the associated gene. According to their research, the 25th percentile is a good proxy for benign variants and the 75th percentile is a good proxy for pathogenic variants.

Download URL

https://basespace.illumina.com/s/cPgCSmecvhb4

JSON Output

GRCh38

"primateAI-3D": [
  {
    "aminoAcidPosition": 2,
    "refAminoAcid": "V",
    "altAminoAcid": "M",
    "score": 0.616944,
    "scorePercentile": 0.52,
    "ensemblTranscriptId": "ENST00000335137.4",
    "refSeqTranscriptId": "NM_001005484.1"
  }
]
FieldTypeNotes
aminoAcidPositionintAmino Acid Position (1-based)
refAminoAcidstringReference Amino Acid
altAminoAcidstringAlternate Amino Acid
ensemblTranscriptIdstringTranscript ID (Ensembl)
refSeqTranscriptIdstringTranscript ID (RefSeq)
scorePercentilefloatrange: 0 - 1.0
scorefloatrange: 0 - 1.0

GRCh37

"primateAI": [
   {
      "hgnc":"TP53",
      "scorePercentile":0.3,
   }
]
FieldTypeNotes
hgncstringHGNC Gene Symbol
scorePercentilefloatrange: 0 - 1.0
+ + \ No newline at end of file diff --git a/3.22/data-sources/revel-json/index.html b/3.22/data-sources/revel-json/index.html index 1e6564be..957b4c67 100644 --- a/3.22/data-sources/revel-json/index.html +++ b/3.22/data-sources/revel-json/index.html @@ -6,13 +6,13 @@ revel-json | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

revel-json

"revel":{ 
   "score":0.027
}
FieldTypeNotes
scorefloatRange: 0 - 1.0
- - +
Skip to main content
Version: 3.22

revel-json

"revel":{ 
   "score":0.027
}
FieldTypeNotes
scorefloatRange: 0 - 1.0
+ + \ No newline at end of file diff --git a/3.22/data-sources/revel/index.html b/3.22/data-sources/revel/index.html index 882178b7..41fc9eed 100644 --- a/3.22/data-sources/revel/index.html +++ b/3.22/data-sources/revel/index.html @@ -6,13 +6,13 @@ REVEL | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

REVEL

Overview

REVEL is an ensemble method for predicting the pathogenicity of missense variants based on a combination of scores from 13 individual tools: MutPred, FATHMM v2.3, VEST 3.0, PolyPhen-2, SIFT, PROVEAN, MutationAssessor, MutationTaster, LRT, GERP++, SiPhy, phyloP, and phastCons.

Publication

Ioannidis, N. M. et al. REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants. The American Journal of Human Genetics 99, 877-885 (2016). https://doi.org/10.1016/j.ajhg.2016.08.016

CSV File

Example

chr,hg19_pos,grch38_pos,ref,alt,aaref,aaalt,REVEL
1,35142,35142,G,A,T,M,0.027
1,35142,35142,G,C,T,R,0.035
1,35142,35142,G,T,T,K,0.043
1,35143,35143,T,A,T,S,0.018
1,35143,35143,T,C,T,A,0.034

Parsing

From the CSV file, we're mainly interested in the following columns:

  • chr
  • hg19_pos
  • grch38_pos
  • ref
  • alt
  • REVEL

Known Issues

Sorting

Since the input file contains positions for both GRCh37 and GRCh38, we split it into two TSV files (for the sake of better readability) with identical format. The positions for GRCh37 were sorted but not for GRCh38. So we re-sort the variants by position in the GRCh38 file.

Conflicting Scores

When there are multiple scores available for the same variant (i.e. the same position with the same alternative allele), we pick the highest score.

Download URL

https://sites.google.com/site/revelgenomics/downloads

JSON Output

"revel":{ 
   "score":0.027
}
FieldTypeNotes
scorefloatRange: 0 - 1.0
- - +
Skip to main content
Version: 3.22

REVEL

Overview

REVEL is an ensemble method for predicting the pathogenicity of missense variants based on a combination of scores from 13 individual tools: MutPred, FATHMM v2.3, VEST 3.0, PolyPhen-2, SIFT, PROVEAN, MutationAssessor, MutationTaster, LRT, GERP++, SiPhy, phyloP, and phastCons.

Publication

Ioannidis, N. M. et al. REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants. The American Journal of Human Genetics 99, 877-885 (2016). https://doi.org/10.1016/j.ajhg.2016.08.016

CSV File

Example

chr,hg19_pos,grch38_pos,ref,alt,aaref,aaalt,REVEL
1,35142,35142,G,A,T,M,0.027
1,35142,35142,G,C,T,R,0.035
1,35142,35142,G,T,T,K,0.043
1,35143,35143,T,A,T,S,0.018
1,35143,35143,T,C,T,A,0.034

Parsing

From the CSV file, we're mainly interested in the following columns:

  • chr
  • hg19_pos
  • grch38_pos
  • ref
  • alt
  • REVEL

Known Issues

Sorting

Since the input file contains positions for both GRCh37 and GRCh38, we split it into two TSV files (for the sake of better readability) with identical format. The positions for GRCh37 were sorted but not for GRCh38. So we re-sort the variants by position in the GRCh38 file.

Conflicting Scores

When there are multiple scores available for the same variant (i.e. the same position with the same alternative allele), we pick the highest score.

Download URL

https://sites.google.com/site/revelgenomics/downloads

JSON Output

"revel":{ 
   "score":0.027
}
FieldTypeNotes
scorefloatRange: 0 - 1.0
+ + \ No newline at end of file diff --git a/3.22/data-sources/splice-ai-json/index.html b/3.22/data-sources/splice-ai-json/index.html index 35a3937c..c3ea7a6f 100644 --- a/3.22/data-sources/splice-ai-json/index.html +++ b/3.22/data-sources/splice-ai-json/index.html @@ -6,13 +6,13 @@ splice-ai-json | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

splice-ai-json

"spliceAI":[ 
   {
      "hgnc":"BLCAP",
      "acceptorGainDistance":-3,
      "acceptorGainScore":0.3,
      "donorLossDistance":7,
      "donorLossScore":0.9
   },
   { 
      "hgnc":"NNAT",
      "acceptorGainDistance":-1,
      "acceptorGainScore":0.2,
      "donorGainDistance":-2,
      "donorGainScore":0.3
   }
]
FieldTypeNotes
hgncstringHGNC gene symbol
acceptorGainDistanceint± bp from current position
acceptorGainScorefloatrange: 0 - 1.0. 1 decimal place
acceptorLossDistanceint± bp from current position
acceptorLossScorefloatrange: 0 - 1.0. 1 decimal place
donorGainDistanceint± bp from current position
donorGainScorefloatrange: 0 - 1.0. 1 decimal place
donorLossDistanceint± bp from current position
donorLossScorefloatrange: 0 - 1.0. 1 decimal place
- - +
Skip to main content
Version: 3.22

splice-ai-json

"spliceAI":[ 
   {
      "hgnc":"BLCAP",
      "acceptorGainDistance":-3,
      "acceptorGainScore":0.3,
      "donorLossDistance":7,
      "donorLossScore":0.9
   },
   { 
      "hgnc":"NNAT",
      "acceptorGainDistance":-1,
      "acceptorGainScore":0.2,
      "donorGainDistance":-2,
      "donorGainScore":0.3
   }
]
FieldTypeNotes
hgncstringHGNC gene symbol
acceptorGainDistanceint± bp from current position
acceptorGainScorefloatrange: 0 - 1.0. 1 decimal place
acceptorLossDistanceint± bp from current position
acceptorLossScorefloatrange: 0 - 1.0. 1 decimal place
donorGainDistanceint± bp from current position
donorGainScorefloatrange: 0 - 1.0. 1 decimal place
donorLossDistanceint± bp from current position
donorLossScorefloatrange: 0 - 1.0. 1 decimal place
+ + \ No newline at end of file diff --git a/3.22/data-sources/splice-ai/index.html b/3.22/data-sources/splice-ai/index.html index fe4bd32a..231dd0a5 100644 --- a/3.22/data-sources/splice-ai/index.html +++ b/3.22/data-sources/splice-ai/index.html @@ -6,13 +6,13 @@ Splice AI | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

Splice AI

Overview

SpliceAI, a 32-layer deep neural network, predicts splicing from a pre-mRNA sequence.

Publication

K. Jaganathan, et al. Predicting splicing from primary sequence with deep learning. Cell, 176 (3) (2019), pp. 535-548 e24

Professional data source

This is a Professional data source and is not available freely. Please contact annotation_support@illumina.com if you would like to obtain it.

VCF File

Example

##fileformat=VCFv4.0
##assembly=GRCh37/hg19
##INFO=<ID=SYMBOL,Number=1,Type=String,Description="HGNC gene symbol">
##INFO=<ID=STRAND,Number=1,Type=String,Description="+ or - depending on whether the gene lies in the positive or negative strand">
##INFO=<ID=TYPE,Number=1,Type=String,Description="E or I depending on whether the variant position is exonic or intronic (GENCODE V24lift37 canonical annotation)">
##INFO=<ID=DIST,Number=1,Type=Integer,Description="Distance between the variant position and the closest splice site (GENCODE V24lift37 canonical annotation)">
##INFO=<ID=DS_AG,Number=1,Type=Float,Description="Delta score (acceptor gain)">
##INFO=<ID=DS_AL,Number=1,Type=Float,Description="Delta score (acceptor loss)">
##INFO=<ID=DS_DG,Number=1,Type=Float,Description="Delta score (donor gain)">
##INFO=<ID=DS_DL,Number=1,Type=Float,Description="Delta score (donor loss)">
##INFO=<ID=DP_AG,Number=1,Type=Integer,Description="Delta position (acceptor gain) relative to the variant position">
##INFO=<ID=DP_AL,Number=1,Type=Integer,Description="Delta position (acceptor loss) relative to the variant position">
##INFO=<ID=DP_DG,Number=1,Type=Integer,Description="Delta position (donor gain) relative to the variant position">
##INFO=<ID=DP_DL,Number=1,Type=Integer,Description="Delta position (donor loss) relative to the variant position">
#CHROM  POS ID  REF ALT QUAL    FILTER  INFO
10  92946   .   C   T   .   .   SYMBOL=TUBB8;STRAND=-;TYPE=E;DIST=-53;DS_AG=0.0000;DS_AL=0.0000;DS_DG=0.0000;DS_DL=0.0000;DP_AG=-26;DP_AL=-10;DP_DG=3;DP_DL=35
10  92946   .   C   G   .   .   SYMBOL=TUBB8;STRAND=-;TYPE=E;DIST=-53;DS_AG=0.0008;DS_AL=0.0000;DS_DG=0.0003;DS_DL=0.0000;DP_AG=34;DP_AL=-27;DP_DG=35;DP_DL=1
10  92946   .   C   A   .   .   SYMBOL=TUBB8;STRAND=-;TYPE=E;DIST=-53;DS_AG=0.0004;DS_AL=0.0000;DS_DG=0.0001;DS_DL=0.0000;DP_AG=-10;DP_AL=-48;DP_DG=35;DP_DL=-21
10  92947   .   A   C   .   .   SYMBOL=TUBB8;STRAND=-;TYPE=E;DIST=-54;DS_AG=0.0002;DS_AL=0.0000;DS_DG=0.0000;DS_DL=0.0000;DP_AG=-49;DP_AL=-11;DP_DG=0;DP_DL=34
10  92947   .   A   T   .   .   SYMBOL=TUBB8;STRAND=-;TYPE=E;DIST=-54;DS_AG=0.0002;DS_AL=0.0000;DS_DG=0.0000;DS_DL=0.0000;DP_AG=33;DP_AL=-11;DP_DG=-22;DP_DL=34
10  92947   .   A   G   .   .   SYMBOL=TUBB8;STRAND=-;TYPE=E;DIST=-54;DS_AG=0.0006;DS_AL=0.0000;DS_DG=0.0001;DS_DL=0.0000;DP_AG=33;DP_AL=-11;DP_DG=34;DP_DL=32

Parsing

From the VCF file, we're mainly interested in the following columns:

  • DS_AG - Δ score (acceptor gain)
  • DS_AL - Δ score (acceptor loss)
  • DS_DG - Δ score (donor gain)
  • DS_DL - Δ score (donor loss)
  • DP_AG - Δ position (acceptor gain) relative to the variant position
  • DP_AL - Δ position (acceptor loss) relative to the variant position
  • DP_DG - Δ position (donor gain) relative to the variant position
  • DP_DL - Δ position (donor loss) relative to the variant position

The Splice AI team suggests the following interpretation for the scores:

RangeConfidencePathogenicity
0 ≤ x < 0.1lowlikely benign
0.1 ≤ x ≤ 0.5mediumlikely pathogenic
x > 0.5highpathogenic

Pre-processing

Filtering

Splice AI provides a comprehensive list of entries throughout the genome. However, many of the entries have little value. I.e. observing low splice scores in intergenic regions. Not only do these extra entries require more storage, but the unused content has a negative impact on annotation speed.

As a result, Illumina Connected Annotations filters out all the values in the low confidence tier except for regions within 15 bp of nascent splice sites. For those regions, we found it useful to see if Splice AI predicted an interruption of the splicing mechanism.

Download URL

https://basespace.illumina.com/s/5u6ThOblecrh

JSON Output

"spliceAI":[ 
   {
      "hgnc":"BLCAP",
      "acceptorGainDistance":-3,
      "acceptorGainScore":0.3,
      "donorLossDistance":7,
      "donorLossScore":0.9
   },
   { 
      "hgnc":"NNAT",
      "acceptorGainDistance":-1,
      "acceptorGainScore":0.2,
      "donorGainDistance":-2,
      "donorGainScore":0.3
   }
]
FieldTypeNotes
hgncstringHGNC gene symbol
acceptorGainDistanceint± bp from current position
acceptorGainScorefloatrange: 0 - 1.0. 1 decimal place
acceptorLossDistanceint± bp from current position
acceptorLossScorefloatrange: 0 - 1.0. 1 decimal place
donorGainDistanceint± bp from current position
donorGainScorefloatrange: 0 - 1.0. 1 decimal place
donorLossDistanceint± bp from current position
donorLossScorefloatrange: 0 - 1.0. 1 decimal place
- - +
Skip to main content
Version: 3.22

Splice AI

Overview

SpliceAI, a 32-layer deep neural network, predicts splicing from a pre-mRNA sequence.

Publication

K. Jaganathan, et al. Predicting splicing from primary sequence with deep learning. Cell, 176 (3) (2019), pp. 535-548 e24

Professional data source

This is a Professional data source and is not available freely. Please contact annotation_support@illumina.com if you would like to obtain it.

VCF File

Example

##fileformat=VCFv4.0
##assembly=GRCh37/hg19
##INFO=<ID=SYMBOL,Number=1,Type=String,Description="HGNC gene symbol">
##INFO=<ID=STRAND,Number=1,Type=String,Description="+ or - depending on whether the gene lies in the positive or negative strand">
##INFO=<ID=TYPE,Number=1,Type=String,Description="E or I depending on whether the variant position is exonic or intronic (GENCODE V24lift37 canonical annotation)">
##INFO=<ID=DIST,Number=1,Type=Integer,Description="Distance between the variant position and the closest splice site (GENCODE V24lift37 canonical annotation)">
##INFO=<ID=DS_AG,Number=1,Type=Float,Description="Delta score (acceptor gain)">
##INFO=<ID=DS_AL,Number=1,Type=Float,Description="Delta score (acceptor loss)">
##INFO=<ID=DS_DG,Number=1,Type=Float,Description="Delta score (donor gain)">
##INFO=<ID=DS_DL,Number=1,Type=Float,Description="Delta score (donor loss)">
##INFO=<ID=DP_AG,Number=1,Type=Integer,Description="Delta position (acceptor gain) relative to the variant position">
##INFO=<ID=DP_AL,Number=1,Type=Integer,Description="Delta position (acceptor loss) relative to the variant position">
##INFO=<ID=DP_DG,Number=1,Type=Integer,Description="Delta position (donor gain) relative to the variant position">
##INFO=<ID=DP_DL,Number=1,Type=Integer,Description="Delta position (donor loss) relative to the variant position">
#CHROM  POS ID  REF ALT QUAL    FILTER  INFO
10  92946   .   C   T   .   .   SYMBOL=TUBB8;STRAND=-;TYPE=E;DIST=-53;DS_AG=0.0000;DS_AL=0.0000;DS_DG=0.0000;DS_DL=0.0000;DP_AG=-26;DP_AL=-10;DP_DG=3;DP_DL=35
10  92946   .   C   G   .   .   SYMBOL=TUBB8;STRAND=-;TYPE=E;DIST=-53;DS_AG=0.0008;DS_AL=0.0000;DS_DG=0.0003;DS_DL=0.0000;DP_AG=34;DP_AL=-27;DP_DG=35;DP_DL=1
10  92946   .   C   A   .   .   SYMBOL=TUBB8;STRAND=-;TYPE=E;DIST=-53;DS_AG=0.0004;DS_AL=0.0000;DS_DG=0.0001;DS_DL=0.0000;DP_AG=-10;DP_AL=-48;DP_DG=35;DP_DL=-21
10  92947   .   A   C   .   .   SYMBOL=TUBB8;STRAND=-;TYPE=E;DIST=-54;DS_AG=0.0002;DS_AL=0.0000;DS_DG=0.0000;DS_DL=0.0000;DP_AG=-49;DP_AL=-11;DP_DG=0;DP_DL=34
10  92947   .   A   T   .   .   SYMBOL=TUBB8;STRAND=-;TYPE=E;DIST=-54;DS_AG=0.0002;DS_AL=0.0000;DS_DG=0.0000;DS_DL=0.0000;DP_AG=33;DP_AL=-11;DP_DG=-22;DP_DL=34
10  92947   .   A   G   .   .   SYMBOL=TUBB8;STRAND=-;TYPE=E;DIST=-54;DS_AG=0.0006;DS_AL=0.0000;DS_DG=0.0001;DS_DL=0.0000;DP_AG=33;DP_AL=-11;DP_DG=34;DP_DL=32

Parsing

From the VCF file, we're mainly interested in the following columns:

  • DS_AG - Δ score (acceptor gain)
  • DS_AL - Δ score (acceptor loss)
  • DS_DG - Δ score (donor gain)
  • DS_DL - Δ score (donor loss)
  • DP_AG - Δ position (acceptor gain) relative to the variant position
  • DP_AL - Δ position (acceptor loss) relative to the variant position
  • DP_DG - Δ position (donor gain) relative to the variant position
  • DP_DL - Δ position (donor loss) relative to the variant position

The Splice AI team suggests the following interpretation for the scores:

RangeConfidencePathogenicity
0 ≤ x < 0.1lowlikely benign
0.1 ≤ x ≤ 0.5mediumlikely pathogenic
x > 0.5highpathogenic

Pre-processing

Filtering

Splice AI provides a comprehensive list of entries throughout the genome. However, many of the entries have little value. I.e. observing low splice scores in intergenic regions. Not only do these extra entries require more storage, but the unused content has a negative impact on annotation speed.

As a result, Illumina Connected Annotations filters out all the values in the low confidence tier except for regions within 15 bp of nascent splice sites. For those regions, we found it useful to see if Splice AI predicted an interruption of the splicing mechanism.

Download URL

https://basespace.illumina.com/s/5u6ThOblecrh

JSON Output

"spliceAI":[ 
   {
      "hgnc":"BLCAP",
      "acceptorGainDistance":-3,
      "acceptorGainScore":0.3,
      "donorLossDistance":7,
      "donorLossScore":0.9
   },
   { 
      "hgnc":"NNAT",
      "acceptorGainDistance":-1,
      "acceptorGainScore":0.2,
      "donorGainDistance":-2,
      "donorGainScore":0.3
   }
]
FieldTypeNotes
hgncstringHGNC gene symbol
acceptorGainDistanceint± bp from current position
acceptorGainScorefloatrange: 0 - 1.0. 1 decimal place
acceptorLossDistanceint± bp from current position
acceptorLossScorefloatrange: 0 - 1.0. 1 decimal place
donorGainDistanceint± bp from current position
donorGainScorefloatrange: 0 - 1.0. 1 decimal place
donorLossDistanceint± bp from current position
donorLossScorefloatrange: 0 - 1.0. 1 decimal place
+ + \ No newline at end of file diff --git a/3.22/data-sources/topmed-json/index.html b/3.22/data-sources/topmed-json/index.html index a5e668d9..3bf34e6d 100644 --- a/3.22/data-sources/topmed-json/index.html +++ b/3.22/data-sources/topmed-json/index.html @@ -6,13 +6,13 @@ topmed-json | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

topmed-json

"topmed":{ 
   "allAc":20,
   "allAn":125568,
   "allAf":0.000159,
   "allHc":0,
   "failedFilter":true
}
FieldTypeNotes
allAcintTOPMed allele count
allAnintTOPMed allele number. Non-zero integer.
allAffloatTOPMed allele frequency (computed by Illumina Connected Annotations)
allHcintTOPMed homozygous count
failedFilterboolTrue if this variant failed any filters
- - +
Skip to main content
Version: 3.22

topmed-json

"topmed":{ 
   "allAc":20,
   "allAn":125568,
   "allAf":0.000159,
   "allHc":0,
   "failedFilter":true
}
FieldTypeNotes
allAcintTOPMed allele count
allAnintTOPMed allele number. Non-zero integer.
allAffloatTOPMed allele frequency (computed by Illumina Connected Annotations)
allHcintTOPMed homozygous count
failedFilterboolTrue if this variant failed any filters
+ + \ No newline at end of file diff --git a/3.22/data-sources/topmed/index.html b/3.22/data-sources/topmed/index.html index e9e1bcdf..91229e5c 100644 --- a/3.22/data-sources/topmed/index.html +++ b/3.22/data-sources/topmed/index.html @@ -6,13 +6,13 @@ TOPMed | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

TOPMed

Overview

The Trans-Omics for Precision Medicine (TOPMed) program, sponsored by the National Institutes of Health (NIH) National Heart, Lung and Blood Institute (NHLBI), is part of a broader Precision Medicine Initiative, which aims to provide disease treatments tailored to an individual’s unique genes and environment. TOPMed contributes to this Initiative through the integration of whole-genome sequencing (WGS) and other omics (e.g., metabolic profiles, epigenomics, protein and RNA expression patterns) data with molecular, behavioral, imaging, environmental, and clinical data.

Publication

Kowalski, M.H., Qian, H., Hou, Z., Rosen, J.D., Tapia, A.L., Shan, Y., Jain, D., Argos, M., Arnett, D.K., Avery, C. and Barnes, K.C., 2019. Use of> 100,000 NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium whole genome sequences improves imputation quality and detection of rare variant associations in admixed African and Hispanic/Latino populations. PLoS genetics, 15(12), p.e1008500.

VCF extraction

We currently extract the following fields from TOPMed VCF file:

##INFO=<ID=AN,Number=1,Type=Integer,Description="Number of Alleles in Samples with Coverage">
##INFO=<ID=AC,Number=A,Type=Integer,Description="Alternate Allele Counts in Samples with Coverage">
##INFO=<ID=AF,Number=A,Type=Float,Description="Alternate Allele Frequencies">
##INFO=<ID=Het,Number=A,Type=Integer,Description="Number of samples with heterozygous genotype calls">
##INFO=<ID=Hom,Number=A,Type=Integer,Description="Number of samples with homozygous alternate genotype calls">

Example:

chr1    10132   TOPMed_freeze_5?chr1:10,132     T       C       255     SVM     VRT=1;NS=62784;AN=125568;AC=32;AF=0.000254842;Het=32;Hom=0      NA:FRQ  125568:0.000254842

GRCh37 liftover

The data is not available for GRCh37 on TOPMed website. We performed a liftover from GRCh38 to GRCh37 using dbSNP ids.

Download URL

https://bravo.sph.umich.edu/freeze5/hg38/download

JSON output

"topmed":{ 
   "allAc":20,
   "allAn":125568,
   "allAf":0.000159,
   "allHc":0,
   "failedFilter":true
}
FieldTypeNotes
allAcintTOPMed allele count
allAnintTOPMed allele number. Non-zero integer.
allAffloatTOPMed allele frequency (computed by Illumina Connected Annotations)
allHcintTOPMed homozygous count
failedFilterboolTrue if this variant failed any filters
- - +
Skip to main content
Version: 3.22

TOPMed

Overview

The Trans-Omics for Precision Medicine (TOPMed) program, sponsored by the National Institutes of Health (NIH) National Heart, Lung and Blood Institute (NHLBI), is part of a broader Precision Medicine Initiative, which aims to provide disease treatments tailored to an individual’s unique genes and environment. TOPMed contributes to this Initiative through the integration of whole-genome sequencing (WGS) and other omics (e.g., metabolic profiles, epigenomics, protein and RNA expression patterns) data with molecular, behavioral, imaging, environmental, and clinical data.

Publication

Kowalski, M.H., Qian, H., Hou, Z., Rosen, J.D., Tapia, A.L., Shan, Y., Jain, D., Argos, M., Arnett, D.K., Avery, C. and Barnes, K.C., 2019. Use of> 100,000 NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium whole genome sequences improves imputation quality and detection of rare variant associations in admixed African and Hispanic/Latino populations. PLoS genetics, 15(12), p.e1008500.

VCF extraction

We currently extract the following fields from TOPMed VCF file:

##INFO=<ID=AN,Number=1,Type=Integer,Description="Number of Alleles in Samples with Coverage">
##INFO=<ID=AC,Number=A,Type=Integer,Description="Alternate Allele Counts in Samples with Coverage">
##INFO=<ID=AF,Number=A,Type=Float,Description="Alternate Allele Frequencies">
##INFO=<ID=Het,Number=A,Type=Integer,Description="Number of samples with heterozygous genotype calls">
##INFO=<ID=Hom,Number=A,Type=Integer,Description="Number of samples with homozygous alternate genotype calls">

Example:

chr1    10132   TOPMed_freeze_5?chr1:10,132     T       C       255     SVM     VRT=1;NS=62784;AN=125568;AC=32;AF=0.000254842;Het=32;Hom=0      NA:FRQ  125568:0.000254842

GRCh37 liftover

The data is not available for GRCh37 on TOPMed website. We performed a liftover from GRCh38 to GRCh37 using dbSNP ids.

Download URL

https://bravo.sph.umich.edu/freeze5/hg38/download

JSON output

"topmed":{ 
   "allAc":20,
   "allAn":125568,
   "allAf":0.000159,
   "allHc":0,
   "failedFilter":true
}
FieldTypeNotes
allAcintTOPMed allele count
allAnintTOPMed allele number. Non-zero integer.
allAffloatTOPMed allele frequency (computed by Illumina Connected Annotations)
allHcintTOPMed homozygous count
failedFilterboolTrue if this variant failed any filters
+ + \ No newline at end of file diff --git a/3.22/file-formats/custom-annotations/index.html b/3.22/file-formats/custom-annotations/index.html index 7866f0f6..fb2e9212 100644 --- a/3.22/file-formats/custom-annotations/index.html +++ b/3.22/file-formats/custom-annotations/index.html @@ -6,8 +6,8 @@ Custom Annotations | IlluminaConnectedAnnotations - - + +
@@ -33,8 +33,8 @@ vid, chromosome, transcripts, etc.. The title should also not conflict with other data source keys like clinvar or gnomad.

For structural variants, you can't provide a title that conflicts with other keys in the position object. Some examples of this would be chromosome, svLength, cytogeneticBand, etc. The title should also not conflict with other data source keys like clingen or dgv.

caution

Care should be taken not to annotate using multiple custom annotations that all use the same title.

Genome Assemblies

The following genome assemblies can be specified:

Matching Criteria

The matching criteria instructs how Illumina Connected Annotations should match a VCF variant to the custom annotation.

The following matching criteria can be specified:

Categories

Categories are not used by Illumina Connected Annotations, but are often used by downstream tools. Categories provide hints for how those tools should filter or display -the annotation data.

When a category is specified, Illumina Connected Annotations will provide additional validation for those fields. The following table describes each category:

CategoryDescriptionValidation
AlleleCountallele counts for a specific populationSee the supported populations below
AlleleNumberallele numbers for a specific populationSee the supported populations below
AlleleFrequencyallele frequencies for a specific populationSee the supported populations below
PredictionACMG-style pathogenicity classifications• benign (B)
• likely benign (LB)
• VUS
• likely pathogenic (LP)
• pathogenic (P)
Filterfree text that signals downstream tools to add the column to the filterMax 20 characters
Descriptionfree-text descriptionMax 100 characters
Identifierany IDMax 50 characters
HomozygousCountcount of homozygous individuals for a specific populationSee the supported populations below
Scoreany score valueAny double-precision floating point number

Descriptions

Descriptions are used to add more context to the categories. For now, descriptions are mainly used to associate allele counts, numbers, and frequencies with their respective populations.

Populations

The following populations were specified in the HapMap project, 1000 Genomes Project, ExAC, and gnomAD.

Population CodeSuper-population CodeDescription
ACBAFRAfrican Caribbeans in Barbados
AFRAFRAfrican
ALLALLAll populations
AMRAMRAd Mixed American
ASJAshkenazi Jewish
ASWAFRAmericans of African Ancestry in SW USA
BEBSASBengali from Bangladesh
CDXEASChinese Dai in Xishuangbanna, China
CEUEURUtah Residents (CEPH) with Northern and Western European Ancestry
CHBEASHan Chinese in Beijing, China
CHSEASSouthern Han Chinese
CLMAMRColombians from Medellin, Colombia
EASEASEast Asian
ESNAFREsan in Nigeria
EUREUREuropean
FINEURFinnish in Finland
GBREURBritish in England and Scotland
GIHSASGujarati Indian from Houston, Texas
GWDAFRGambian in Western Divisions in the Gambia
IBSEURIberian population in Spain
ITUSASIndian Telugu from the UK
JPTEASJapanese in Tokyo, Japan
KHVEASKinh in Ho Chi Minh City, Vietnam
LWKAFRLuhya in Webuye, Kenya
MAGAFRMandinka in the Gambia
MKKAFRMaasai in Kinyawa, Kenya
MSLAFRMende in Sierra Leone
MXLAMRMexican Ancestry from Los Angeles, USA
NFEEUREuropean (Non-Finnish)
OTHOTHOther
PELAMRPeruvians from Lima, Peru
PJLSASPunjabi from Lahore, Pakistan
PURAMRPuerto Ricans from Puerto Rico
SASSASSouth Asian
STUSASSri Lankan Tamil from the UK
TSIEURToscani in Italia
YRIAFRYoruba in Ibadan, Nigeria

Data Types

Each custom annotation can be one of the following data types:

tip

For boolean variables, only keys with a true value will be output to the JSON object.

Using SAUtils

Illumina Connected Annotations includes a tool called SAUtils that converts various data sources into Illumina Connected Annotations's native binary format. The sub-commands customvar and customgene are used to specify a variant file or a gene file respectively.

Convert Variant File

dotnet bin/Release/netcoreapp2.1/SAUtils.dll customvar \
     -r Data/References/Homo_sapiens.GRCh37.Nirvana.dat \
     -i MyDataSource.tsv \
     -o SupplementaryAnnotation

Convert Gene File

dotnet bin/Release/netcoreapp2.1/SAUtils.dll customgene \
     -r Data/References/Homo_sapiens.GRCh37.Nirvana.dat \
     -c Data/Cache \
     -i MyDataSource.tsv \
     -o SupplementaryAnnotation
- - +the annotation data.

When a category is specified, Illumina Connected Annotations will provide additional validation for those fields. The following table describes each category:

CategoryDescriptionValidation
AlleleCountallele counts for a specific populationSee the supported populations below
AlleleNumberallele numbers for a specific populationSee the supported populations below
AlleleFrequencyallele frequencies for a specific populationSee the supported populations below
PredictionACMG-style pathogenicity classifications• benign (B)
• likely benign (LB)
• VUS
• likely pathogenic (LP)
• pathogenic (P)
Filterfree text that signals downstream tools to add the column to the filterMax 20 characters
Descriptionfree-text descriptionMax 100 characters
Identifierany IDMax 50 characters
HomozygousCountcount of homozygous individuals for a specific populationSee the supported populations below
Scoreany score valueAny double-precision floating point number

Descriptions

Descriptions are used to add more context to the categories. For now, descriptions are mainly used to associate allele counts, numbers, and frequencies with their respective populations.

Populations

The following populations were specified in the HapMap project, 1000 Genomes Project, ExAC, and gnomAD.

Population CodeSuper-population CodeDescription
ACBAFRAfrican Caribbeans in Barbados
AFRAFRAfrican
ALLALLAll populations
AMRAMRAd Mixed American
ASJAshkenazi Jewish
ASWAFRAmericans of African Ancestry in SW USA
BEBSASBengali from Bangladesh
CDXEASChinese Dai in Xishuangbanna, China
CEUEURUtah Residents (CEPH) with Northern and Western European Ancestry
CHBEASHan Chinese in Beijing, China
CHSEASSouthern Han Chinese
CLMAMRColombians from Medellin, Colombia
EASEASEast Asian
ESNAFREsan in Nigeria
EUREUREuropean
FINEURFinnish in Finland
GBREURBritish in England and Scotland
GIHSASGujarati Indian from Houston, Texas
GWDAFRGambian in Western Divisions in the Gambia
IBSEURIberian population in Spain
ITUSASIndian Telugu from the UK
JPTEASJapanese in Tokyo, Japan
KHVEASKinh in Ho Chi Minh City, Vietnam
LWKAFRLuhya in Webuye, Kenya
MAGAFRMandinka in the Gambia
MKKAFRMaasai in Kinyawa, Kenya
MSLAFRMende in Sierra Leone
MXLAMRMexican Ancestry from Los Angeles, USA
NFEEUREuropean (Non-Finnish)
OTHOTHOther
PELAMRPeruvians from Lima, Peru
PJLSASPunjabi from Lahore, Pakistan
PURAMRPuerto Ricans from Puerto Rico
SASSASSouth Asian
STUSASSri Lankan Tamil from the UK
TSIEURToscani in Italia
YRIAFRYoruba in Ibadan, Nigeria

Data Types

Each custom annotation can be one of the following data types:

tip

For boolean variables, only keys with a true value will be output to the JSON object.

Using SAUtils

Illumina Connected Annotations includes a tool called SAUtils that converts various data sources into Illumina Connected Annotations's native binary format. The sub-commands customvar and customgene are used to specify a variant file or a gene file respectively.

Convert Variant File

dotnet bin/Release/netcoreapp2.1/SAUtils.dll customvar \
     -r Data/References/Homo_sapiens.GRCh37.Nirvana.dat \
     -i MyDataSource.tsv \
     -o SupplementaryAnnotation

Convert Gene File

dotnet bin/Release/netcoreapp2.1/SAUtils.dll customgene \
     -r Data/References/Homo_sapiens.GRCh37.Nirvana.dat \
     -c Data/Cache \
     -i MyDataSource.tsv \
     -o SupplementaryAnnotation
+ + \ No newline at end of file diff --git a/3.22/file-formats/illumina-annotator-json-file-format/index.html b/3.22/file-formats/illumina-annotator-json-file-format/index.html index 566d5fa4..e0f0cdab 100644 --- a/3.22/file-formats/illumina-annotator-json-file-format/index.html +++ b/3.22/file-formats/illumina-annotator-json-file-format/index.html @@ -6,13 +6,13 @@ Illumina Connected Annotations JSON File Format | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

Illumina Connected Annotations JSON File Format

Overview

Conventions

In the Illumina Connected Annotations JSON representation, we try to maximize the amount of useful information that is relayed in the output file. As such, we have several conventions that are useful to know about:

  • With boolean key/value pairs, we only output the keys that have a true value. I.e. there's no reason to display "isStructuralVariant":false a few million times when annotating a small variant VCF.
  • When transferring data from the VCF file to the JSON (e.g. for allele depths (AD)), it is common to use a period (.) as a placeholder for missing data in the VCF file. Illumina Connected Annotations treats periods like empty or null strings and therefore will not output those entries.

JSON Layout

info

In general, each position corresponds to a row in the original VCF file.

For each gene that was referenced in the transcripts found in the positions section, there will be additional gene-level annotation in the gene section.

Parsing

info

We've put together a new section that discusses how to parse our JSON files easily using examples in a Python Jupyter notebook and a R version as well. In addition, we have information about how to quickly dump content from our JSON file using a tabix-like utility called JASIX.

{
   "header":{
      "annotator":"IlluminaConnectedAnnotations 3.0.0-alpha.5+g6c52e247",
      "creationTime":"2017-06-14 15:53:13",
      "genomeAssembly":"GRCh37",
      "dataSources":[
         {
            "name":"OMIM",
            "version":"unknown",
            "description":"An Online Catalog of Human Genes and Genetic Disorders",
            "releaseDate":"2017-05-03"
         },
         {
            "name":"VEP",
            "version":"84",
            "description":"BothRefSeqAndEnsembl",
            "releaseDate":"2017-01-16"
         },
         {
            "name":"ClinVar",
            "version":"20170503",
            "description":"A freely accessible, public archive of reports of the relationships among human variations and phenotypes, with supporting evidence",
            "releaseDate":"2017-05-03"
         },
         {
            "name":"phyloP",
            "version":"hg19",
            "description":"46 way conservation score between humans and 45 other vertebrates",
            "releaseDate":"2009-11-10"
         }
      ],
      "samples":[
         "NA12878",
         "NA12891",
         "NA12892"
      ]
   },
FieldTypeNotes
annotatorstringthe name of the annotator and the current version
creationTimestringyyyy-MM-dd hh:mm:ss
genomeAssemblystringsee possible values below
schemaVersionintegerincremented whenever the core structure of the JSON file introduces breaking changes
dataVersionstring
dataSourcesobject arraysee Data Source entry below
samplesstring arraythe order of these sample names will be used throughout the JSON file when enumerating samples

Data Source

FieldTypeNotes
namestring
versionstring
descriptionstringoptional description of the data source
releaseDatestringyyyy-MM-dd

Genome Assemblies

  • GRCh37
  • GRCh38
  • hg19
  • SARSCoV2

Positions

"positions":[
   {
      "chromosome":"chr2",
      "position":48010488,
      "repeatUnit":"GGCCCC",
      "refRepeatCount":3,
      "svEnd":48020488,
      "refAllele":"G",
      "altAlleles":[
         "A",
         "GT"
      ],
      "quality":461,
      "filters":[
         "PASS"
      ],
      "ciPos":[
         -170,
         170
      ],
      "ciEnd":[
         -175,
         175
      ],
      "svLength":1000,
      "strandBias":1.23,
      "jointSomaticNormalQuality":29,
      "cytogeneticBand":"2p16.3",
FieldTypeVariant TypeNotes
chromosomestringallexactly as displayed in the vcf
positionintegerallexactly as displayed in the vcf (1-based notation). Range: 1 - 250 million
repeatUnitstringSTRprovided by ExpansionHunter
refRepeatCountintegerSTRprovided by ExpansionHunter
svEndintegerSV
refAllelestringallexactly as displayed in the vcf
altAllelestring arrayallexactly as displayed in the vcf
qualityfloatallexactly as displayed in the vcf (Normally an integer, but some variant callers using floating point. Has been observed as high as 500k)
filtersstring arrayallexactly as displayed in the vcf
ciPosinteger arraySV
ciEndinteger arraySV
svLengthintegerSV
strandBiasfloatsmall variantprovided by GATK (from SB)
jointSomaticNormalQualityintegerSVprovided by the Manta variant caller (SOMATICSCORE)
cytogeneticBandstringalle.g. 17p13.1

ClinGen

"clingen":[
   {
      "chromosome":"17",
      "begin":525,
      "end":14667519,
      "variantType":"copy_number_gain",
      "id":"nsv996083",
      "clinicalInterpretation":"pathogenic",
      "observedGains":1,
      "validated":true,
      "phenotypes":[
         "Intrauterine growth retardation"
      ],
      "phenotypeIds":[
         "HP:0001511",
         "MedGen:C1853481"
      ],
      "reciprocalOverlap":0.00131
   },
   {
      "chromosome":"17",
      "begin":45835,
      "end":7600330,
      "variantType":"copy_number_loss",
      "id":"nsv869419",
      "clinicalInterpretation":"pathogenic",
      "observedLosses":1,
      "validated":true,
      "phenotypes":[
         "Developmental delay AND/OR other significant developmental or morphological phenotypes"
      ],
      "reciprocalOverlap":0.00254
   }
]
FieldTypeNotes
clingenobject array
chromosomestringEnsembl-style chromosome names
begininteger1-based position
endinteger1-based position
variantTypestringAny of the sequence alterations defined here.
idstringIdentifier from the data source. Alternatively a VID
clinicalInterpretationstringsee possible values below
observedGainsintegerRange: 0 - (231 - 1). Only used if copy_number_variation, copy_number_loss, or copy_number_gain.
observedLossesintegerRange: 0 - (231 - 1). Only used if copy_number_variation, copy_number_loss, or copy_number_gain.
validatedboolean
phenotypesstring arrayDescription of the phenotype.
phenotypeIdsstring arrayDescription of the phenotype IDs.
reciprocalOverlapfloating pointRange: 0 - 1. E.g. 0.57 would indicate a 57% reciprocal overlap. Specified up to 5 decimal places (Not reported for Insertions).

clinicalInterpretation

  • benign
  • curated benign
  • curated pathogenic
  • likely benign
  • likely pathogenic
  • path gain
  • path loss
  • pathogenic
  • uncertain
"clingenDosageSensitivityMap": [{
    "chromosome": "15",
    "begin": 30900686,
    "end": 32153204,
    "haploinsufficiency": "sufficient evidence suggesting dosage sensitivity is associated with clinical phenotype",
    "triplosensitivity": "little evidence suggesting dosage sensitivity is associated with clinical phenotype",
    "reciprocalOverlap": 0.00147,
    "annotationOverlap": 0.33994
},
{
    "chromosome": "15",
    "begin": 31727418,
    "end": 32153204,
    "haploinsufficiency": "sufficient evidence suggesting dosage sensitivity is associated with clinical phenotype",
    "triplosensitivity": "dosage sensitivity unlikely",
    "reciprocalOverlap": 0.00147,
    "annotationOverlap": 1
}]
FieldTypeNotes
clingenDosageSensitivityMapobject array
chromosomestringEnsembl-style chromosome names
begininteger1-based position
endinteger1-based position
haploinsufficiencystringsee possible values below
triplosensitivitystring(same as haploinsufficiency) 
reciprocalOverlapfloating pointRange: 0 - 1. E.g. 0.57 would indicate a 57% reciprocal overlap. Specified up to 5 decimal places (Not reported for Insertions).
annotationOverlapfloating pointRange: 0 - 1. E.g. 0.57 would indicate a 57% reciprocal overlap. Specified up to 5 decimal places (Not reported for Insertions).

haploinsufficiency and triplosensitivity

  • no evidence to suggest that dosage sensitivity is associated with clinical phenotype
  • little evidence suggesting dosage sensitivity is associated with clinical phenotype
  • emerging evidence suggesting dosage sensitivity is associated with clinical phenotype
  • sufficient evidence suggesting dosage sensitivity is associated with clinical phenotype
  • gene associated with autosomal recessive phenotype
  • dosage sensitivity unlikely

1000 Genomes (SV)

"oneKg":[
   {
      "chromosome":"1",
      "begin":1595369,
      "end":1612441,
      "variantType": "copy_number_variation",
      "id": "esv3635753;esv3635754;esv3635755;esv3635756;esv3635757",
      "allAn": 5008,
      "allAc": 2702,
      "allAf": 0.539537,
      "afrAf": 0.6052,
      "amrAf": 0.3675,
      "eurAf": 0.5357,
      "easAf": 0.5368,
      "sasAf": 0.5797,
      "reciprocalOverlap": 0.07555
   }
],
FieldTypeNotes
chromosomestring
begininteger
endinteger
variantTypestring
idstring
allAnintegerallele number for all populations. Non-zero integer.
allAcintegerallele count for all populations. Integer.
allAffloating pointallele frequency for all populations. Range: 0 - 1.0
afrAffloating pointallele frequency for the African super population. Range: 0 - 1.0
amrAffloating pointallele frequency for the Ad Mixed American super population. Range: 0 - 1.0
eurAffloating pointallele frequency for the European super population. Range: 0 - 1.0
easAffloating pointallele frequency for the East Asian super population. Range: 0 - 1.0
sasAffloating pointallele frequency for the South Asian super population. Range: 0 - 1.0
reciprocalOverlapfloating pointrange: 0 - 1.

gnomAD (SV)

"gnomAD-preview": [
  {
    "chromosome": "1",
    "begin": 40001,
    "end": 47200,
    "variantId": "gnomAD-SV_v2.1_DUP_1_1",
    "variantType": "duplication",
    "failedFilter": true,
    "allAf": 0.068963,
    "afrAf": 0.135694,
    "amrAf": 0.022876,
    "easAf": 0.01101,
    "eurAf": 0.007846,
    "othAf": 0.017544,
    "femaleAf": 0.065288,
    "maleAf": 0.07255,
    "allAc": 943,
    "afrAc": 866,
    "amrAc": 21,
    "easAc": 17,
    "eurAc": 37,
    "othAc": 2,
    "femaleAc": 442,
    "maleAc": 499,
    "allAn": 13674,
    "afrAn": 6382,
    "amrAn": 918,
    "easAn": 1544,
    "eurAn": 4716,
    "othAn": 114,
    "femaleAn": 6770,
    "maleAn": 6878,
    "allHc": 91,
    "afrHc": 90,
    "amrHc": 1,
    "easHc": 0,
    "eurHc": 0,
    "othHc": 55,
    "femaleHc": 44,
    "maleHc": 47,
    "reciprocalOverlap": 0.01839,
    "annotationOverlap": 0.16667
  }
]

FieldTypeNotes
chromosomestringchromosome number
beginintegerposition interval start
endintegerposition internal end
variantTypestringstructural variant type
variantIdstringgnomAD ID
allAffloating pointallele frequency for all populations. Range: 0 - 1.0
afrAffloating pointallele frequency for the African super population. Range: 0 - 1.0
amrAffloating pointallele frequency for the Ad Mixed American super population. Range: 0 - 1.0
easAffloating pointallele frequency for the East Asian super population. Range: 0 - 1.0
eurAffloating pointallele frequency for the European super population. Range: 0 - 1.0
othAffloating pointallele frequency for all other populations. Range: 0 - 1.0
femaleAffloating pointallele frequency for female population. Range: 0 - 1.0
maleAffloating pointallele frequency for male population. Range: 0 - 1.0
allAcintegerallele count for all populations.
afrAcintegerallele count for the African super population.
amrAcintegerallele count for the Ad Mixed American super population.
easAcintegerallele count for the East Asian super population.
eurAcintegerallele count for the European super population.
othAcintegerallele count for all other populations.
maleAcintegerallele count for male population.
femaleAcintegerallele count for female population.
allAnintegerallele number for all populations.
afrAnintegerallele number for the African super population.
amrAnintegerallele number for the Ad Mixed American super population.
easAnintegerallele number for the East Asian super population.
eurAnintegerallele number for the European super population.
othAnintegerallele number for all other populations.
femaleAnintegerallele number for female population.
maleAnintegerallele number for male population.
allHcintegercount of homozygous individuals for all populations.
afrHcintegercount of homozygous individuals for the African / African American population.
amrHcintegercount of homozygous individuals for the Latino population.
easHcintegercount of homozygous individuals for the East Asian population.
eurAcintegercount of homozygous individuals for the European super population.
othHcintegercount of homozygous individuals for all other populations.
maleHcintegercount of homozygous individuals for male population.
femaleHcintegercount of homozygous individuals for female population.
failedFilterbooleanTrue if this variant failed any filters (Note: we do not list the failed filters)
reciprocalOverlapfloating pointReciprocal overlap. Range: 0 - 1.0
annotationOverlapfloating pointReciprocal overlap. Range: 0 - 1.0

Note: Following fields are not available in GRCh38 because the source file does not contain this information:

Field
femaleAf
maleAf
maleAc
femaleAc
femaleAn
maleAn
allHc
afrHc
amrHc
easHc
eurAc
othHc
maleHc
femaleHc
failedFilter

MITOMAP (SV)

"mitomap":[ 
   { 
      "chromosome":"MT",
      "begin":3166,
      "end":14152,
      "variantType":"deletion",
      "reciprocalOverlap":0.18068,
      "annotationOverlap":0.42405
   }
]
FieldTypeNotes
chromosomestring
begininteger
endinteger
variantTypestring array
reciprocalOverlapfloatRange: 0 - 1. Specified up to 5 decimal places
annotationOverlapfloatRange: 0 - 1. Specified up to 5 decimal places

Samples

"samples":[
   {
      "genotype":"0/1",
      "variantFrequencies":[
         0.333,
         0.5
      ],
      "totalDepth":57,
      "genotypeQuality":12,
      "copyNumber":3,
      "repeatUnitCounts":[
         10,
         20
      ],
      "alleleDepths":[
         10,
         20,
         30
      ],
      "failedFilter":true,
      "splitReadCounts":[
         10,
         20
      ],
      "pairedEndReadCounts":[
         10,
         20
      ],
      "isDeNovo":true,
      "diseaseAffectedStatuses":[
         "-"
      ],
      "artifactAdjustedQualityScore":89.3,
      "likelihoodRatioQualityScore":78.2,
      "heteroplasmyPercentile":[
         23.13,
         12.65
      ]
   }
]
FieldTypeVCFNotes
genotypestringGT
variantFrequenciesfloat arrayVF, ADrange: 0 - 1.0. One value per alternate allele
totalDepthintegerDPnon-negative integer values
genotypeQualityintegerGQnon-negative integer values. Typically maxes out at 99
copyNumberintegerCNnon-negative integer values
minorHaplotypeCopyNumberintegerMCNnon-negative integer values
repeatUnitCountsinteger arrayREPCNExpansionHunter-specific
alleleDepthsinteger arrayADnon-negative integer values
failedFilterboolFT
splitReadCountsinteger arraySRManta-specific
pairedEndReadCountsinteger arrayPRManta-specific
isDeNovoboolDN
deNovoQualityfloatDQ
diseaseAffectedStatusesstring arrayDSTExpansionHunter-specific
artifactAdjustedQualityScorefloatAQPEPE-specific. Range: 0 - 100.0
likelihoodRatioQualityScorefloatLQPEPE-specific. Range: 0 - 100.0
lossOfHeterozygosityboolCN, MCN
somaticQualityfloatSQ
heteroplasmyPercentilefloatVFrange: 0 - 100. 2 decimal places. One value per alternate allele
binCountintegerBCnon-negative integer values
Empty Samples

If a sample does not contain any entries, we will create a sample object that contains the isEmpty key. This ensures that sample ordering is preserved while indicating that a sample is intentionally empty.

"samples":[
   {
      "isEmpty":true
   }
],

Variants

"variants":[
   {
      "vid":"2:48010488:A",
      "chromosome":"chr2",
      "begin":48010488,
      "end":48010488,
      "isReferenceMinorAllele":true,
      "isStructuralVariant":true,
      "refAllele":"G",
      "altAllele":"A",
      "variantType":"SNV",
      "isDecomposedVariant":true,
      "isRecomposedVariant":true,
      "linkedVids":["2:48010488:GTA:ATC"],
      "hgvsg":"NC_000002.11:g.48010488G>A",
      "phylopScore":0.459
FieldTypeNotes
vidstringsee Variant Identifiers
chromosomestring
beginint1-based non-negative integer values. Range: 1 - 250 million
endint1-based non-negative integer values. Range: 1 - 250 million
isReferenceMinorAllelebooltrue when this is a reference minor allele
isStructuralVariantbooltrue when the variant is a structural variant
inLowComplexityRegionbooltrue when the variant lies in a low complexity region (gnomAD low complexity regions)
refAllelestringparsimonious representation of the reference allele
altAllelestringparsimonious representation of the alternate allele.
variantTypestringuses Sequence Ontology sequence alterations
isDecomposedVariantbooltrue when the decomposed variant has been used to create another recomposed variant
isRecomposedVariantbooltrue when the variant is recomposed from two or more decomposed variants
linkedVidsstring arraylist of VIDs for variants connecting decomposed and recomposed variants
hgvsgstringHGVS g. notation
phylopScorefloatphyloP conservation score. Range: -14.08 to 6.424
Reference Minor Alleles

Illumina Connected Annotations supports annotating reference minor alleles. In such a case, refAllele will be replaced by the global major allele and altAllele will be replaced with the original reference allele.

Flagging Decomposed & Recomposed Variants

When two or more decomposed variants are recomposed into an MNV, the decomposed variants will be marked with "isDecomposedVariant":true.

Similarly, the recomposed variant will be shown as a new VCF position. This recomposed variant will be flagged with "isRecomposedVariant":true.

Transcripts

"transcripts":[
   {
      "transcript":"ENST00000445503.1",
      "source":"Ensembl",
      "bioType":"nonsense_mediated_decay",
      "codons":"gGg/gAg",
      "aminoAcids":"G/E",
      "cdnaPos":"268",
      "cdsPos":"116",
      "exons":"1/9",
      "introns":"1/8",
      "proteinPos":"39",
      "geneId":"ENSG00000116062",
      "hgnc":"MSH6",
      "consequence":[
         "missense_variant",
         "NMD_transcript_variant"
      ],
      "hgvsc":"ENST00000445503.1:c.116G>A",
      "hgvsp":"ENSP00000405294.1:p.(Gly39Glu)",
      "geneFusion":{
         "exon":6,
         "intron":5,
         "fusions":[
            {
               "hgvsc":"ETV6{ENST00000396373.4}:c.1_1009+3402_RUNX1{ENST00000437180.1}:c.58+568_1443",
               "exon":3,
               "intron":2
            },
            {
               "hgvsc":"ETV6{ENST00000396373.4}:c.1_1009+3402_RUNX1{ENST00000300305.3}:c.58+568_1443",
               "exon":2,
               "intron":1
            }
         ]
      },
      "isCanonical":true,
      "polyPhenScore":0.95,
      "polyPhenPrediction":"probably damaging",
      "proteinId":"ENSP00000405294.1",
      "siftScore":0.61,
      "siftPrediction":"tolerated",
      "completeOverlap":true
   }
]
FieldTypeNotes
transcriptstringtranscript ID. e.g. ENST00000445503.1
sourcestringRefSeq / Ensembl
bioTypestringdescriptions of the biotypes from Ensembl
codonsstring
aminoAcidsstring
cdnaPosstring
cdsPosstring
exonsstringexons affected by the variant
intronsstringintrons affected by the variant
proteinPosstring
geneIdstringgene ID. e.g. ENSG00000116062
hgncstringgene symbol. e.g. MSH6
consequencestring arraySequence Ontology Consequences
hgvscstringHGVS coding nomenclature
hgvspstringHGVS protein nomenclature
geneFusionobjectsee Gene Fusions entry below
isCanonicalbooltrue when this is a canonical transcript
isManeSelectbooltrue when this is a MANE select transcript
polyPhenScorefloatrange: 0 - 1.0
polyPhenPredictionstringsee possible values below
proteinIdstringprotein ID. E.g. ENSP00000405294.1
siftScorefloatrange: 0 - 1.0
siftPredictionstringsee possible values below
completeOverlapbooltrue when this transcript is completely overlapped by the variant
cancerHotspotsstring arraysee Cancer Hotspots entry below
MANE Select

MANE select tags are only available for RefSeq transcripts on GRCh38.

PolyPhen

  • probably damaging
  • possibly damaging
  • benign
  • unknown

SIFT

  • tolerated
  • deleterious
  • tolerated - low confidence
  • deleterious - low confidence

Amino Acid Conservation

"aminoAcidConservation": {
    "scores": [0.34]
}
FieldTypeNotes
aminoAcidConservationobject
scoresobject array of doublespercent conserved with respect to human amino acid residue. Range: 0.01 - 1.00

Gene Fusions

FieldTypeNotes
exonintactual exon where the breakpoint was located
intronintactual intron where the breakpoint was located
fusionsobject arraysee Fusion entry below

Fusion

FieldTypeNotes
exonintactual exon where the other breakpoint was located
intronintactual intron where the other breakpoint was located
hgvscstringHGVS coding nomenclature describing the two genes and the transcripts that are fused along with

Cancer Hotspots

FieldTypeNotes
residuestring
numSamplesinthow many samples are associated with a variant at the same amino acid position
numAltAminoAcidSamplesinthow many samples are associated with a variant with the same position and alternate amino acid position
qValuedouble

Regulatory Regions

"regulatoryRegions":[
   {
      "id":"ENSR00001542175",
      "type":"promoter",
      "consequence":[
         "regulatory_region_variant"
      ]
   }
]
FieldTypeNotes
idstring
typestringsee possible values below
consequencestring arraysee possible values below

Regulatory Types

  • CTCF_binding_site
  • enhancer
  • open_chromatin_region
  • promoter
  • promoter_flanking_region
  • TF_binding_site

Regulatory Consequences

  • regulatory_region_variant
  • regulatory_region_ablation
  • regulatory_region_amplification
  • regulatory_region_truncation

ClinVar

small variants:

"clinvar":[
   {
      "id":"VCV000036581.3",
      "reviewStatus":"reviewed by expert panel",
      "significance":[
         "benign"
      ],
      "refAllele":"G",
      "altAllele":"A",
      "lastUpdatedDate":"2020-03-01",
      "isAlleleSpecific":true
   },
   {
      "id":"RCV000030258.4",
      "variationId":"VCV000036581.3",
      "reviewStatus":"reviewed by expert panel",
      "alleleOrigins":[
         "germline"
      ],
      "refAllele":"G",
      "altAllele":"A",
      "phenotypes":[
         "Lynch syndrome"
      ],
      "medGenIds":[
         "C1333990"
      ],
      "omimIds":[
         "120435"
      ],
      "significance":[
         "benign"
      ],
      "lastUpdatedDate":"2017-05-01",
      "isAlleleSpecific":true
   }
]

large variants:

"clinvar":[
   {
      "chromosome":"1", 
      "begin":629025, 
      "end":8537745, 
      "variantType":"copy_number_loss", 
      "id":"RCV000051993.4", 
      "variationId":"VCV000058242.1", 
      "reviewStatus":"criteria provided, single submitter", 
      "alleleOrigins":[
         "not provided"
      ], 
      "phenotypes":[
         "See cases"
      ], 
      "significance":[
         "pathogenic"
      ], 
      "lastUpdatedDate":"2022-04-21", 
      "pubMedIds":[
         "21844811"
      ]
   },
   {
      "id":"VCV000058242.1",
      "reviewStatus":"criteria provided, single submitter",
      "significance":[
         "pathogenic"
      ],
      "lastUpdatedDate":"2022-04-21"
   },
        ......
]
FieldTypeNotes
idstringClinVar ID
variationIdstringClinVar VCV ID
variantTypestringvariant type
reviewStatusstringsee possible values below
alleleOriginsstring arraysee possible values below
refAllelestring
altAllelestring
phenotypesstring array
medGenIdsstring arrayMedGen IDs
omimIdsstring arrayOMIM IDs
orphanetIdsstring arrayOrphanet IDs
significancestring arraysee possible values below
lastUpdatedDatestringyyyy-MM-dd
pubMedIdsstring arrayPubMed IDs
isAlleleSpecificbooltrue when the current variant alternate allele matches the ClinVar alternate allele

reviewStatus:

  • no assertion provided
  • no assertion criteria provided
  • criteria provided, single submitter
  • practice guideline
  • classified by multiple submitters
  • criteria provided, conflicting interpretations
  • criteria provided, multiple submitters, no conflicts
  • no interpretation for the single variant

alleleOrigins:

  • unknown
  • other
  • germline
  • somatic
  • inherited
  • paternal
  • maternal
  • de-novo
  • biparental
  • uniparental
  • not-tested
  • tested-inconclusive

significance:

  • uncertain significance
  • not provided
  • benign
  • likely benign
  • likely pathogenic
  • pathogenic
  • drug response
  • histocompatibility
  • association
  • risk factor
  • protective
  • affects
  • conflicting data from submitters
  • other
  • no interpretation for the single variant
  • conflicting interpretations of pathogenicity

1000 Genomes

"oneKg":{
   "allAf":0.200879,
   "afrAf":0.210287,
   "amrAf":0.139769,
   "easAf":0.275794,
   "eurAf":0.181909,
   "sasAf":0.173824,
   "allAn":5008,
   "afrAn":1322,
   "amrAn":694,
   "easAn":1008,
   "eurAn":1006,
   "sasAn":978,
   "allAc":1006,
   "afrAc":278,
   "amrAc":97,
   "easAc":278,
   "eurAc":183,
   "sasAc":170
}
FieldTypeNotes
allAffloatallele frequency for all populations. Range: 0 - 1.0
allAcintallele count for all populations. Integer.
allAnintallele number for all populations. Non-zero integer.
afrAffloatallele frequency for the African super population. Range: 0 - 1.0
afrAcintallele count for the African super population. Integer.
afrAnintallele number for the African super population. Non-zero integer.
amrAffloatallele frequency for the Ad Mixed American super population. Range: 0 - 1.0
amrAcintallele count for the Ad Mixed American super population. Integer.
amrAnintallele number for the Ad Mixed American super population. Non-zero integer.
easAffloatallele frequency for the East Asian super population. Range: 0 - 1.0
easAcintallele count for the East Asian super population. Integer.
easAnintallele number for the East Asian super population. Non-zero integer.
eurAffloatallele frequency for the European super population. Range: 0 - 1.0
eurAcintallele count for the European super population. Integer.
eurAnintallele number for the European super population. Non-zero integer.
sasAffloatallele frequency for the South Asian super population. Range: 0 - 1.0
sasAcintallele count for the South Asian super population. Integer.
sasAnintallele number for the South Asian super population. Non-zero integer.

DANN

"dannScore": 0.27
FieldTypeNotes
dannScorefloatRange: 0 - 1.0

dbSNP

"dbsnp":[
   "rs1042821"
]
FieldTypeNotes
dbsnpstring arraydbSNP rsIDs

DECIPHER

"decipher":[
  {
    "chromosome":"1",
    "begin":13516,
    "end":91073,
    "numDeletions":27,
    "deletionFrequency":0.675,
    "numDuplications":27,
    "duplicationFrequency":0.675,
    "sampleSize":40,
    "reciprocalOverlap": 0.27555,
    "annotationOverlap": 0.5901
  }
],
FieldTypeNotes
chromosomeintEnsembl-style chromosome names
beginint1-based position
endint1-based position
numDeletionsint# of observed deletions
deletionFrequencyfloatdeletion frequency
numDuplicationsint# of observed duplications
duplicationFrequencyfloatduplication frequency
sampleSizeinttotal # of samples
reciprocalOverlapfloatRange: 0 - 1. E.g. 0.57 would indicate a 57% reciprocal overlap
annotationOverlapfloatRange: 0 - 1. E.g. 0.57 would indicate a 57% annotation overlap

GERP

"gerpScore": 1.27
FieldTypeNotes
gerpScorefloatRange: -∞ to +∞

GME Variome

"gmeVariome":{
   "allAc":10,
   "allAn":202,
   "allAf":0.049504,
   "failedFilter":true
}
FieldTypeNotes
allAcintGME allele count
allAnintGME allele number
allAffloatGME allele frequency
failedFilterboolTrue if this variant failed any filters

gnomAD

"gnomad":{ 
   "coverage":20,
   "allAf":0.190317,
   "maleAf":0.193,
   "femaleAf": 0.1935,
   "afrAf":0.222876,
   "amrAf":0.121394,
   "easAf":0.239802,
   "finAf":0.136833,
   "nfeAf":0.181282,
   "asjAf":0.258278,
   "othAf":0.186094,
   "allAn":30796,
   "maleAn":15096,
   "femaleAn":15700
   "afrAn":8664,
   "amrAn":832,
   "easAn":1618,
   "finAn":3486,
   "nfeAn":14916,
   "asjAn":302,
   "othAn":978,
   "allAc":5861,
   "maleAc":2930,
   "femaleAc": 2931,
   "afrAc":1931,
   "amrAc":101,
   "easAc":388,
   "finAc":477,
   "nfeAc":2704,
   "asjAc":78,
   "othAc":182,
   "allHc":561,
   "afrHc":208,
   "amrHc":6,
   "easHc":42,
   "finHc":31,
   "nfeHc":242,
   "asjHc":13,
   "othHc":19,
   "maleHc":280,
   "femaleHc":281,
   "controlsAllAf":0.190317,
   "controlsAllAn":30796,
   "controlsAllAc":5861,
   "lowComplexityRegion":true,
   "failedFilter":true
}
FieldTypeNotes
coverageintaverage coverage (non-negative integer values)
allAffloatallele frequency for all populations. Range: 0 - 1.0
maleAffloatallele frequency for male population. Range: 0 - 1.0
femaleAffloatallele frequency for female population. Range: 0 - 1.0
controlsAllAffloatallele frequency for the controls subset. Range: 0 - 1.0
allAcintallele count for all populations. Integer.
maleAcintallele count for male population. Integer.
femaleAcintallele count for female population. Integer.
controlsAllAcintallele count for the controls subset. Integer.
allAnintallele number for all populations. Non-zero integer.
maleAnintallele number for male population. Non-zero integer.
femaleAnintallele number for female population. Non-zero integer.
controlsAllAnintallele number for the controls subset. Non-zero integer.
allHcintcount of homozygous individuals for all populations. Non-negative integer.
maleHcintcount of homozygous individuals for male population. Non-negative integer.
femaleHcintcount of homozygous individuals for female population. Non-negative integer.
afrAffloatallele frequency for the African / African American population. Range: 0 - 1.0
afrAcintallele count for the African / African American population. Integer.
afrAnintallele number for the African / African American population. Non-zero integer.
afrHcintcount of homozygous individuals for African / African American population. Non-negative integer.
amrAffloatallele frequency for the Latino population. Range: 0 - 1.0
amrAcintallele count for the Latino population. Integer.
amrAnintallele number for the Latino population. Non-zero integer.
amrHcintcount of homozygous individuals for Latino population. Non-negative integer.
easAffloatallele frequency for the East Asian population. Range: 0 - 1.0
easAcintallele count for the East Asian population. Integer.
easAnintallele number for the East Asian population. Non-zero integer.
easHcintcount of homozygous individuals for East Asian population. Non-negative integer.
finAffloatallele frequency for the Finnish population. Range: 0 - 1.0
finAcintallele count for the Finnish population. Integer.
finAnintallele number for the Finnish population. Non-zero integer.
finHcintcount of homozygous individuals for Finnish population. Non-negative integer
nfeAffloatallele frequency for the Non-Finnish European population. Range: 0 - 1.0
nfeAcintallele count for the Non-Finnish European population. Integer.
nfeAnintallele number for the Non-Finnish European population. Non-zero integer.
nfeHcintcount of homozygous individuals for Non-Finnish European population. Non-negative integer
othAffloatallele frequency for the Other population. Range: 0 - 1.0
othAcintallele count for the Other population. Integer.
othAnintallele number for the Other population. Non-zero integer.
othHcintcount of homozygous individuals for Other population. Non-negative integer
asjAffloatallele frequency for the Ashkenazi Jewish population. Range: 0 - 1.0
asjAcintallele count for the Ashkenazi Jewish population Integer.
asjAnintallele number for the Ashkenazi Jewish population. Non-zero integer.
asjHcintcount of homozygous individuals for the Ashkenazi Jewish population. Non-negative integer
sasAffloatallele frequency for the South Asian population. Range: 0 - 1.0
sasAcintallele count for the South Asian population Integer.
sasAnintallele number for the South Asian population. Non-zero integer.
sasHcintcount of homozygous individuals for the South Asian population. Non-negative integer.
failedFilterboolTrue if this variant failed any filters (Note: we do not list the failed filters)
lowComplexityRegionboolTrue if this variant is located in a low complexity region.

MITOMAP

"mitomap":[ 
   { 
      "refAllele":"G",
      "altAllele":"A",
      "diseases":[ 
         "Bipolar disorder",
         "Melanoma"
      ],
      "hasHomoplasmy":false,
      "hasHeteroplasmy":true,
      "status":"Reported",
      "clinicalSignificance":"confirmed pathogenic",
      "scorePercentile":83.30,
      "numGenBankFullLengthSeqs":2,
      "pubMedIds":["2316527","6299878","6301949"],
      "isAlleleSpecific":true
   }
]
FieldTypeNotes
refAllelestring
altAllelestring
diseasesstring arrayassociated diseases
hasHomoplasmyboolean
hasHeteroplasmyboolean
statusstringrecord status
clinicalSignificancestringpredicted pathogenicity
scorePercentilefloatMitoTIP score
numGenBankFullLengthSeqsinteger# of GenBank full-length sequences
pubMedIdsstring array
isAlleleSpecificbooleantrue when the current variant alternate allele matches the MITOMAP alternate allele

Primate AI

GRCh38

"primateAI-3D": [
  {
    "aminoAcidPosition": 2,
    "refAminoAcid": "V",
    "altAminoAcid": "M",
    "score": 0.616944,
    "scorePercentile": 0.52,
    "ensemblTranscriptId": "ENST00000335137.4",
    "refSeqTranscriptId": "NM_001005484.1"
  }
]
FieldTypeNotes
aminoAcidPositionintAmino Acid Position (1-based)
refAminoAcidstringReference Amino Acid
altAminoAcidstringAlternate Amino Acid
ensemblTranscriptIdstringTranscript ID (Ensembl)
refSeqTranscriptIdstringTranscript ID (RefSeq)
scorePercentilefloatrange: 0 - 1.0
scorefloatrange: 0 - 1.0

GRCh37

"primateAI": [
   {
      "hgnc":"TP53",
      "scorePercentile":0.3,
   }
]
FieldTypeNotes
hgncstringHGNC Gene Symbol
scorePercentilefloatrange: 0 - 1.0

REVEL

"revel":{ 
   "score":0.027
}
FieldTypeNotes
scorefloatRange: 0 - 1.0

Splice AI

"spliceAI":[ 
   {
      "hgnc":"BLCAP",
      "acceptorGainDistance":-3,
      "acceptorGainScore":0.3,
      "donorLossDistance":7,
      "donorLossScore":0.9
   },
   { 
      "hgnc":"NNAT",
      "acceptorGainDistance":-1,
      "acceptorGainScore":0.2,
      "donorGainDistance":-2,
      "donorGainScore":0.3
   }
]
FieldTypeNotes
hgncstringHGNC gene symbol
acceptorGainDistanceint± bp from current position
acceptorGainScorefloatrange: 0 - 1.0. 1 decimal place
acceptorLossDistanceint± bp from current position
acceptorLossScorefloatrange: 0 - 1.0. 1 decimal place
donorGainDistanceint± bp from current position
donorGainScorefloatrange: 0 - 1.0. 1 decimal place
donorLossDistanceint± bp from current position
donorLossScorefloatrange: 0 - 1.0. 1 decimal place

TOPMed

"topmed":{ 
   "allAc":20,
   "allAn":125568,
   "allAf":0.000159,
   "allHc":0,
   "failedFilter":true
}
FieldTypeNotes
allAcintTOPMed allele count
allAnintTOPMed allele number. Non-zero integer.
allAffloatTOPMed allele frequency (computed by Illumina Connected Annotations)
allHcintTOPMed homozygous count
failedFilterboolTrue if this variant failed any filters

Genes

Illumina Connected Annotations repots gene annotations for all genes that have an overlapping variant with the exception of flanking variants (i.e. variants that only cause upstream_gene_variant or downstream_gene_variant).

"genes":[
   {
      "name":"MSH6",
      "hgncId":7329,
      "summary":"This gene encodes a member of the DNA mismatch repair MutS family. In E. coli, the MutS protein helps in the recognition of mismatched nucleotides prior to their repair. A highly conserved region of approximately 150 aa, called the Walker-A adenine nucleotide binding motif, exists in MutS homologs. The encoded protein heterodimerizes with MSH2 to form a mismatch recognition complex that functions as a bidirectional molecular switch that exchanges ADP and ATP as DNA mismatches are bound and dissociated. Mutations in this gene may be associated with hereditary nonpolyposis colon cancer, colorectal cancer, and endometrial cancer. Transcripts variants encoding different isoforms have been described. [provided by RefSeq, Jul 2013]",
      /* this is where gene-level data sources can be found e.g. OMIM */
   }
]
FieldTypeNotes
namestringHGNC gene symbol
hgncIdintHGNC ID
summarystringshort description of the gene from OMIM

OMIM

"omim":[ 
   { 
      "mimNumber":600678,
      "geneName":"MutS, E. coli, homolog of, 6",
      "description":"The transcription factor p53 responds to diverse cellular stresses to regulate target genes that induce cell cycle arrest, apoptosis, senescence, DNA repair, or changes in metabolism. In addition, p53 appears to induce apoptosis through nontranscriptional cytoplasmic processes. In unstressed cells, p53 is kept inactive essentially through the actions of the ubiquitin ligase MDM2, which inhibits p53 transcriptional activity and ubiquitinates p53 to promote its degradation. Numerous posttranslational modifications modulate p53 activity, most notably phosphorylation and acetylation. Several less abundant p53 isoforms also modulate p53 activity. Activity of p53 is ubiquitously lost in human cancer either by mutation of the p53 gene itself or by loss of cell signaling upstream or downstream of p53 (Toledo and Wahl, 2006; Bourdon, 2007; Vousden and Lane, 2007)",
      "phenotypes":[ 
         { 
            "mimNumber":614350,
            "phenotype":"Colorectal cancer, hereditary nonpolyposis, type 5",
            "description":"Hereditary nonpolyposis colorectal cancer type 5 is a cancer predisposition syndrome ...",
            "mapping":"molecular basis of the disorder is known",
            "inheritances":[ 
               "Autosomal dominant"
            ]
         },
         { 
            "mimNumber":608089,
            "phenotype":"Endometrial cancer, familial",
            "mapping":"molecular basis of the disorder is known"
         },
         { 
            "mimNumber":276300,
            "phenotype":"Mismatch repair cancer syndrome",
            "description":"Constitutional mismatch repair deficiency is a rare childhood cancer predisposition syndrome ...",
            "mapping":"molecular basis of the disorder is known",
            "inheritances":[ 
               "Autosomal recessive"
            ],
            "comments"     : [
                "contribute to susceptibility to multifactorial disorders or to susceptibility to infection",
                "unconfirmed or possibly spurious mapping"
            ]
         }
      ]
   }
]
FieldTypeNotes
mimNumberintOMIM ID for gene
geneNamestringgene name
descriptionstring
phenotypesobject arraysee Phenotype entry below

Phenotype

FieldTypeNotes
mimNumberint
phenotypestring
descriptionstring
mappingstringsee possible values below
inheritancestring arraysee possible values below
commentsstring arraysee possible values below

Mapping

  1. disorder was positioned by mapping of the wild type gene
  2. disease phenotype itself was mapped
  3. molecular basis of the disorder is known
  4. disorder is a chromosome deletion or duplication syndrome

Inheritance

  • autosomal recessive
  • autosomal dominant

Comments

  • contributes to the susceptibility to multifactorial disorders
  • variations that lead to apparently abnormal laboratory test values
  • unconfirmed mapping

gnomAD LoF Gene Metrics

"gnomAD":{ 
   "pLi":1.00e0,
   "pNull":8.94e-40,
   "pRec":1.84e-16,
   "synZ":-8.44e-2,
   "misZ":5.96e-1,
   "loeuf":1.13e0
}
FieldTypeNotes
pLifloatprobability of being intolerant of a single loss-of-function variant (like haploinsufficient genes, observed ~ 0.1*expected)
pNullfloatprobability of being completely tolerant of loss of function variation (observed = expected)
pRecfloatprobability of being intolerant of two loss of function variants (like recessive genes, observed ~ 0.5*expected)
synZfloatcorrected synonymous Z score
misZfloatcorrected missense Z score
loeuffloatloss of function observed/expected upper bound fraction (LOEUF)

ClinGen Disease Validity

"clingenGeneValidity":[
   {
      "diseaseId":"MONDO_0007893",
      "disease":"Noonan syndrome with multiple lentigines",
      "classification":"no reported evidence",
      "classificationDate":"2018-06-07"
   },
   {
      "diseaseId":"MONDO_0015280",
      "disease":"cardiofaciocutaneous syndrome",
      "classification":"no reported evidence",
      "classificationDate":"2018-06-07"
   }
]
FieldTypeNotes
clingenGeneValidityobject
diseaseIdstringMonarch Disease Ontology ID (MONDO)
diseasestringdisease label
classificationstringsee below for possible values
classificationDatestringyyyy-MM-dd

classification

  • no reported evidence
  • disputed
  • limited
  • moderate
  • definitive
  • strong
  • refuted
  • no known disease relationship

COSMIC Cancer Gene Census

   {
  "name": "PRDM16",
  "hgncId": 14000,
  "ncbiGeneId": "63976",
  "ensemblGeneId": "ENSG00000142611",
  "cosmic": {
    "roleInCancer": [
      "oncogene",
      "fusion"
    ]
  }
}
FieldTypeNotes
roleInCancerstring arrayPossible roles in caner
- - +
Skip to main content
Version: 3.22

Illumina Connected Annotations JSON File Format

Overview

Conventions

In the Illumina Connected Annotations JSON representation, we try to maximize the amount of useful information that is relayed in the output file. As such, we have several conventions that are useful to know about:

  • With boolean key/value pairs, we only output the keys that have a true value. I.e. there's no reason to display "isStructuralVariant":false a few million times when annotating a small variant VCF.
  • When transferring data from the VCF file to the JSON (e.g. for allele depths (AD)), it is common to use a period (.) as a placeholder for missing data in the VCF file. Illumina Connected Annotations treats periods like empty or null strings and therefore will not output those entries.

JSON Layout

info

In general, each position corresponds to a row in the original VCF file.

For each gene that was referenced in the transcripts found in the positions section, there will be additional gene-level annotation in the gene section.

Parsing

info

We've put together a new section that discusses how to parse our JSON files easily using examples in a Python Jupyter notebook and a R version as well. In addition, we have information about how to quickly dump content from our JSON file using a tabix-like utility called JASIX.

{
   "header":{
      "annotator":"IlluminaConnectedAnnotations 3.0.0-alpha.5+g6c52e247",
      "creationTime":"2017-06-14 15:53:13",
      "genomeAssembly":"GRCh37",
      "dataSources":[
         {
            "name":"OMIM",
            "version":"unknown",
            "description":"An Online Catalog of Human Genes and Genetic Disorders",
            "releaseDate":"2017-05-03"
         },
         {
            "name":"VEP",
            "version":"84",
            "description":"BothRefSeqAndEnsembl",
            "releaseDate":"2017-01-16"
         },
         {
            "name":"ClinVar",
            "version":"20170503",
            "description":"A freely accessible, public archive of reports of the relationships among human variations and phenotypes, with supporting evidence",
            "releaseDate":"2017-05-03"
         },
         {
            "name":"phyloP",
            "version":"hg19",
            "description":"46 way conservation score between humans and 45 other vertebrates",
            "releaseDate":"2009-11-10"
         }
      ],
      "samples":[
         "NA12878",
         "NA12891",
         "NA12892"
      ]
   },
FieldTypeNotes
annotatorstringthe name of the annotator and the current version
creationTimestringyyyy-MM-dd hh:mm:ss
genomeAssemblystringsee possible values below
schemaVersionintegerincremented whenever the core structure of the JSON file introduces breaking changes
dataVersionstring
dataSourcesobject arraysee Data Source entry below
samplesstring arraythe order of these sample names will be used throughout the JSON file when enumerating samples

Data Source

FieldTypeNotes
namestring
versionstring
descriptionstringoptional description of the data source
releaseDatestringyyyy-MM-dd

Genome Assemblies

  • GRCh37
  • GRCh38
  • hg19
  • SARSCoV2

Positions

"positions":[
   {
      "chromosome":"chr2",
      "position":48010488,
      "repeatUnit":"GGCCCC",
      "refRepeatCount":3,
      "svEnd":48020488,
      "refAllele":"G",
      "altAlleles":[
         "A",
         "GT"
      ],
      "quality":461,
      "filters":[
         "PASS"
      ],
      "ciPos":[
         -170,
         170
      ],
      "ciEnd":[
         -175,
         175
      ],
      "svLength":1000,
      "strandBias":1.23,
      "jointSomaticNormalQuality":29,
      "cytogeneticBand":"2p16.3",
FieldTypeVariant TypeNotes
chromosomestringallexactly as displayed in the vcf
positionintegerallexactly as displayed in the vcf (1-based notation). Range: 1 - 250 million
repeatUnitstringSTRprovided by ExpansionHunter
refRepeatCountintegerSTRprovided by ExpansionHunter
svEndintegerSV
refAllelestringallexactly as displayed in the vcf
altAllelestring arrayallexactly as displayed in the vcf
qualityfloatallexactly as displayed in the vcf (Normally an integer, but some variant callers using floating point. Has been observed as high as 500k)
filtersstring arrayallexactly as displayed in the vcf
ciPosinteger arraySV
ciEndinteger arraySV
svLengthintegerSV
strandBiasfloatsmall variantprovided by GATK (from SB)
jointSomaticNormalQualityintegerSVprovided by the Manta variant caller (SOMATICSCORE)
cytogeneticBandstringalle.g. 17p13.1

ClinGen

"clingen":[
   {
      "chromosome":"17",
      "begin":525,
      "end":14667519,
      "variantType":"copy_number_gain",
      "id":"nsv996083",
      "clinicalInterpretation":"pathogenic",
      "observedGains":1,
      "validated":true,
      "phenotypes":[
         "Intrauterine growth retardation"
      ],
      "phenotypeIds":[
         "HP:0001511",
         "MedGen:C1853481"
      ],
      "reciprocalOverlap":0.00131
   },
   {
      "chromosome":"17",
      "begin":45835,
      "end":7600330,
      "variantType":"copy_number_loss",
      "id":"nsv869419",
      "clinicalInterpretation":"pathogenic",
      "observedLosses":1,
      "validated":true,
      "phenotypes":[
         "Developmental delay AND/OR other significant developmental or morphological phenotypes"
      ],
      "reciprocalOverlap":0.00254
   }
]
FieldTypeNotes
clingenobject array
chromosomestringEnsembl-style chromosome names
begininteger1-based position
endinteger1-based position
variantTypestringAny of the sequence alterations defined here.
idstringIdentifier from the data source. Alternatively a VID
clinicalInterpretationstringsee possible values below
observedGainsintegerRange: 0 - (231 - 1). Only used if copy_number_variation, copy_number_loss, or copy_number_gain.
observedLossesintegerRange: 0 - (231 - 1). Only used if copy_number_variation, copy_number_loss, or copy_number_gain.
validatedboolean
phenotypesstring arrayDescription of the phenotype.
phenotypeIdsstring arrayDescription of the phenotype IDs.
reciprocalOverlapfloating pointRange: 0 - 1. E.g. 0.57 would indicate a 57% reciprocal overlap. Specified up to 5 decimal places (Not reported for Insertions).

clinicalInterpretation

  • benign
  • curated benign
  • curated pathogenic
  • likely benign
  • likely pathogenic
  • path gain
  • path loss
  • pathogenic
  • uncertain
"clingenDosageSensitivityMap": [{
    "chromosome": "15",
    "begin": 30900686,
    "end": 32153204,
    "haploinsufficiency": "sufficient evidence suggesting dosage sensitivity is associated with clinical phenotype",
    "triplosensitivity": "little evidence suggesting dosage sensitivity is associated with clinical phenotype",
    "reciprocalOverlap": 0.00147,
    "annotationOverlap": 0.33994
},
{
    "chromosome": "15",
    "begin": 31727418,
    "end": 32153204,
    "haploinsufficiency": "sufficient evidence suggesting dosage sensitivity is associated with clinical phenotype",
    "triplosensitivity": "dosage sensitivity unlikely",
    "reciprocalOverlap": 0.00147,
    "annotationOverlap": 1
}]
FieldTypeNotes
clingenDosageSensitivityMapobject array
chromosomestringEnsembl-style chromosome names
begininteger1-based position
endinteger1-based position
haploinsufficiencystringsee possible values below
triplosensitivitystring(same as haploinsufficiency) 
reciprocalOverlapfloating pointRange: 0 - 1. E.g. 0.57 would indicate a 57% reciprocal overlap. Specified up to 5 decimal places (Not reported for Insertions).
annotationOverlapfloating pointRange: 0 - 1. E.g. 0.57 would indicate a 57% reciprocal overlap. Specified up to 5 decimal places (Not reported for Insertions).

haploinsufficiency and triplosensitivity

  • no evidence to suggest that dosage sensitivity is associated with clinical phenotype
  • little evidence suggesting dosage sensitivity is associated with clinical phenotype
  • emerging evidence suggesting dosage sensitivity is associated with clinical phenotype
  • sufficient evidence suggesting dosage sensitivity is associated with clinical phenotype
  • gene associated with autosomal recessive phenotype
  • dosage sensitivity unlikely

1000 Genomes (SV)

"oneKg":[
   {
      "chromosome":"1",
      "begin":1595369,
      "end":1612441,
      "variantType": "copy_number_variation",
      "id": "esv3635753;esv3635754;esv3635755;esv3635756;esv3635757",
      "allAn": 5008,
      "allAc": 2702,
      "allAf": 0.539537,
      "afrAf": 0.6052,
      "amrAf": 0.3675,
      "eurAf": 0.5357,
      "easAf": 0.5368,
      "sasAf": 0.5797,
      "reciprocalOverlap": 0.07555
   }
],
FieldTypeNotes
chromosomestring
begininteger
endinteger
variantTypestring
idstring
allAnintegerallele number for all populations. Non-zero integer.
allAcintegerallele count for all populations. Integer.
allAffloating pointallele frequency for all populations. Range: 0 - 1.0
afrAffloating pointallele frequency for the African super population. Range: 0 - 1.0
amrAffloating pointallele frequency for the Ad Mixed American super population. Range: 0 - 1.0
eurAffloating pointallele frequency for the European super population. Range: 0 - 1.0
easAffloating pointallele frequency for the East Asian super population. Range: 0 - 1.0
sasAffloating pointallele frequency for the South Asian super population. Range: 0 - 1.0
reciprocalOverlapfloating pointrange: 0 - 1.

gnomAD (SV)

"gnomAD-preview": [
  {
    "chromosome": "1",
    "begin": 40001,
    "end": 47200,
    "variantId": "gnomAD-SV_v2.1_DUP_1_1",
    "variantType": "duplication",
    "failedFilter": true,
    "allAf": 0.068963,
    "afrAf": 0.135694,
    "amrAf": 0.022876,
    "easAf": 0.01101,
    "eurAf": 0.007846,
    "othAf": 0.017544,
    "femaleAf": 0.065288,
    "maleAf": 0.07255,
    "allAc": 943,
    "afrAc": 866,
    "amrAc": 21,
    "easAc": 17,
    "eurAc": 37,
    "othAc": 2,
    "femaleAc": 442,
    "maleAc": 499,
    "allAn": 13674,
    "afrAn": 6382,
    "amrAn": 918,
    "easAn": 1544,
    "eurAn": 4716,
    "othAn": 114,
    "femaleAn": 6770,
    "maleAn": 6878,
    "allHc": 91,
    "afrHc": 90,
    "amrHc": 1,
    "easHc": 0,
    "eurHc": 0,
    "othHc": 55,
    "femaleHc": 44,
    "maleHc": 47,
    "reciprocalOverlap": 0.01839,
    "annotationOverlap": 0.16667
  }
]

FieldTypeNotes
chromosomestringchromosome number
beginintegerposition interval start
endintegerposition internal end
variantTypestringstructural variant type
variantIdstringgnomAD ID
allAffloating pointallele frequency for all populations. Range: 0 - 1.0
afrAffloating pointallele frequency for the African super population. Range: 0 - 1.0
amrAffloating pointallele frequency for the Ad Mixed American super population. Range: 0 - 1.0
easAffloating pointallele frequency for the East Asian super population. Range: 0 - 1.0
eurAffloating pointallele frequency for the European super population. Range: 0 - 1.0
othAffloating pointallele frequency for all other populations. Range: 0 - 1.0
femaleAffloating pointallele frequency for female population. Range: 0 - 1.0
maleAffloating pointallele frequency for male population. Range: 0 - 1.0
allAcintegerallele count for all populations.
afrAcintegerallele count for the African super population.
amrAcintegerallele count for the Ad Mixed American super population.
easAcintegerallele count for the East Asian super population.
eurAcintegerallele count for the European super population.
othAcintegerallele count for all other populations.
maleAcintegerallele count for male population.
femaleAcintegerallele count for female population.
allAnintegerallele number for all populations.
afrAnintegerallele number for the African super population.
amrAnintegerallele number for the Ad Mixed American super population.
easAnintegerallele number for the East Asian super population.
eurAnintegerallele number for the European super population.
othAnintegerallele number for all other populations.
femaleAnintegerallele number for female population.
maleAnintegerallele number for male population.
allHcintegercount of homozygous individuals for all populations.
afrHcintegercount of homozygous individuals for the African / African American population.
amrHcintegercount of homozygous individuals for the Latino population.
easHcintegercount of homozygous individuals for the East Asian population.
eurAcintegercount of homozygous individuals for the European super population.
othHcintegercount of homozygous individuals for all other populations.
maleHcintegercount of homozygous individuals for male population.
femaleHcintegercount of homozygous individuals for female population.
failedFilterbooleanTrue if this variant failed any filters (Note: we do not list the failed filters)
reciprocalOverlapfloating pointReciprocal overlap. Range: 0 - 1.0
annotationOverlapfloating pointReciprocal overlap. Range: 0 - 1.0

Note: Following fields are not available in GRCh38 because the source file does not contain this information:

Field
femaleAf
maleAf
maleAc
femaleAc
femaleAn
maleAn
allHc
afrHc
amrHc
easHc
eurAc
othHc
maleHc
femaleHc
failedFilter

MITOMAP (SV)

"mitomap":[ 
   { 
      "chromosome":"MT",
      "begin":3166,
      "end":14152,
      "variantType":"deletion",
      "reciprocalOverlap":0.18068,
      "annotationOverlap":0.42405
   }
]
FieldTypeNotes
chromosomestring
begininteger
endinteger
variantTypestring array
reciprocalOverlapfloatRange: 0 - 1. Specified up to 5 decimal places
annotationOverlapfloatRange: 0 - 1. Specified up to 5 decimal places

Samples

"samples":[
   {
      "genotype":"0/1",
      "variantFrequencies":[
         0.333,
         0.5
      ],
      "totalDepth":57,
      "genotypeQuality":12,
      "copyNumber":3,
      "repeatUnitCounts":[
         10,
         20
      ],
      "alleleDepths":[
         10,
         20,
         30
      ],
      "failedFilter":true,
      "splitReadCounts":[
         10,
         20
      ],
      "pairedEndReadCounts":[
         10,
         20
      ],
      "isDeNovo":true,
      "diseaseAffectedStatuses":[
         "-"
      ],
      "artifactAdjustedQualityScore":89.3,
      "likelihoodRatioQualityScore":78.2,
      "heteroplasmyPercentile":[
         23.13,
         12.65
      ]
   }
]
FieldTypeVCFNotes
genotypestringGT
variantFrequenciesfloat arrayVF, ADrange: 0 - 1.0. One value per alternate allele
totalDepthintegerDPnon-negative integer values
genotypeQualityintegerGQnon-negative integer values. Typically maxes out at 99
copyNumberintegerCNnon-negative integer values
minorHaplotypeCopyNumberintegerMCNnon-negative integer values
repeatUnitCountsinteger arrayREPCNExpansionHunter-specific
alleleDepthsinteger arrayADnon-negative integer values
failedFilterboolFT
splitReadCountsinteger arraySRManta-specific
pairedEndReadCountsinteger arrayPRManta-specific
isDeNovoboolDN
deNovoQualityfloatDQ
diseaseAffectedStatusesstring arrayDSTExpansionHunter-specific
artifactAdjustedQualityScorefloatAQPEPE-specific. Range: 0 - 100.0
likelihoodRatioQualityScorefloatLQPEPE-specific. Range: 0 - 100.0
lossOfHeterozygosityboolCN, MCN
somaticQualityfloatSQ
heteroplasmyPercentilefloatVFrange: 0 - 100. 2 decimal places. One value per alternate allele
binCountintegerBCnon-negative integer values
Empty Samples

If a sample does not contain any entries, we will create a sample object that contains the isEmpty key. This ensures that sample ordering is preserved while indicating that a sample is intentionally empty.

"samples":[
   {
      "isEmpty":true
   }
],

Variants

"variants":[
   {
      "vid":"2:48010488:A",
      "chromosome":"chr2",
      "begin":48010488,
      "end":48010488,
      "isReferenceMinorAllele":true,
      "isStructuralVariant":true,
      "refAllele":"G",
      "altAllele":"A",
      "variantType":"SNV",
      "isDecomposedVariant":true,
      "isRecomposedVariant":true,
      "linkedVids":["2:48010488:GTA:ATC"],
      "hgvsg":"NC_000002.11:g.48010488G>A",
      "phylopScore":0.459
FieldTypeNotes
vidstringsee Variant Identifiers
chromosomestring
beginint1-based non-negative integer values. Range: 1 - 250 million
endint1-based non-negative integer values. Range: 1 - 250 million
isReferenceMinorAllelebooltrue when this is a reference minor allele
isStructuralVariantbooltrue when the variant is a structural variant
inLowComplexityRegionbooltrue when the variant lies in a low complexity region (gnomAD low complexity regions)
refAllelestringparsimonious representation of the reference allele
altAllelestringparsimonious representation of the alternate allele.
variantTypestringuses Sequence Ontology sequence alterations
isDecomposedVariantbooltrue when the decomposed variant has been used to create another recomposed variant
isRecomposedVariantbooltrue when the variant is recomposed from two or more decomposed variants
linkedVidsstring arraylist of VIDs for variants connecting decomposed and recomposed variants
hgvsgstringHGVS g. notation
phylopScorefloatphyloP conservation score. Range: -14.08 to 6.424
Reference Minor Alleles

Illumina Connected Annotations supports annotating reference minor alleles. In such a case, refAllele will be replaced by the global major allele and altAllele will be replaced with the original reference allele.

Flagging Decomposed & Recomposed Variants

When two or more decomposed variants are recomposed into an MNV, the decomposed variants will be marked with "isDecomposedVariant":true.

Similarly, the recomposed variant will be shown as a new VCF position. This recomposed variant will be flagged with "isRecomposedVariant":true.

Transcripts

"transcripts":[
   {
      "transcript":"ENST00000445503.1",
      "source":"Ensembl",
      "bioType":"nonsense_mediated_decay",
      "codons":"gGg/gAg",
      "aminoAcids":"G/E",
      "cdnaPos":"268",
      "cdsPos":"116",
      "exons":"1/9",
      "introns":"1/8",
      "proteinPos":"39",
      "geneId":"ENSG00000116062",
      "hgnc":"MSH6",
      "consequence":[
         "missense_variant",
         "NMD_transcript_variant"
      ],
      "hgvsc":"ENST00000445503.1:c.116G>A",
      "hgvsp":"ENSP00000405294.1:p.(Gly39Glu)",
      "geneFusion":{
         "exon":6,
         "intron":5,
         "fusions":[
            {
               "hgvsc":"ETV6{ENST00000396373.4}:c.1_1009+3402_RUNX1{ENST00000437180.1}:c.58+568_1443",
               "exon":3,
               "intron":2
            },
            {
               "hgvsc":"ETV6{ENST00000396373.4}:c.1_1009+3402_RUNX1{ENST00000300305.3}:c.58+568_1443",
               "exon":2,
               "intron":1
            }
         ]
      },
      "isCanonical":true,
      "polyPhenScore":0.95,
      "polyPhenPrediction":"probably damaging",
      "proteinId":"ENSP00000405294.1",
      "siftScore":0.61,
      "siftPrediction":"tolerated",
      "completeOverlap":true
   }
]
FieldTypeNotes
transcriptstringtranscript ID. e.g. ENST00000445503.1
sourcestringRefSeq / Ensembl
bioTypestringdescriptions of the biotypes from Ensembl
codonsstring
aminoAcidsstring
cdnaPosstring
cdsPosstring
exonsstringexons affected by the variant
intronsstringintrons affected by the variant
proteinPosstring
geneIdstringgene ID. e.g. ENSG00000116062
hgncstringgene symbol. e.g. MSH6
consequencestring arraySequence Ontology Consequences
hgvscstringHGVS coding nomenclature
hgvspstringHGVS protein nomenclature
geneFusionobjectsee Gene Fusions entry below
isCanonicalbooltrue when this is a canonical transcript
isManeSelectbooltrue when this is a MANE select transcript
polyPhenScorefloatrange: 0 - 1.0
polyPhenPredictionstringsee possible values below
proteinIdstringprotein ID. E.g. ENSP00000405294.1
siftScorefloatrange: 0 - 1.0
siftPredictionstringsee possible values below
completeOverlapbooltrue when this transcript is completely overlapped by the variant
cancerHotspotsstring arraysee Cancer Hotspots entry below
MANE Select

MANE select tags are only available for RefSeq transcripts on GRCh38.

PolyPhen

  • probably damaging
  • possibly damaging
  • benign
  • unknown

SIFT

  • tolerated
  • deleterious
  • tolerated - low confidence
  • deleterious - low confidence

Amino Acid Conservation

"aminoAcidConservation": {
    "scores": [0.34]
}
FieldTypeNotes
aminoAcidConservationobject
scoresobject array of doublespercent conserved with respect to human amino acid residue. Range: 0.01 - 1.00

Gene Fusions

FieldTypeNotes
exonintactual exon where the breakpoint was located
intronintactual intron where the breakpoint was located
fusionsobject arraysee Fusion entry below

Fusion

FieldTypeNotes
exonintactual exon where the other breakpoint was located
intronintactual intron where the other breakpoint was located
hgvscstringHGVS coding nomenclature describing the two genes and the transcripts that are fused along with

Cancer Hotspots

FieldTypeNotes
residuestring
numSamplesinthow many samples are associated with a variant at the same amino acid position
numAltAminoAcidSamplesinthow many samples are associated with a variant with the same position and alternate amino acid position
qValuedouble

Regulatory Regions

"regulatoryRegions":[
   {
      "id":"ENSR00001542175",
      "type":"promoter",
      "consequence":[
         "regulatory_region_variant"
      ]
   }
]
FieldTypeNotes
idstring
typestringsee possible values below
consequencestring arraysee possible values below

Regulatory Types

  • CTCF_binding_site
  • enhancer
  • open_chromatin_region
  • promoter
  • promoter_flanking_region
  • TF_binding_site

Regulatory Consequences

  • regulatory_region_variant
  • regulatory_region_ablation
  • regulatory_region_amplification
  • regulatory_region_truncation

ClinVar

small variants:

"clinvar":[
   {
      "id":"VCV000036581.3",
      "reviewStatus":"reviewed by expert panel",
      "significance":[
         "benign"
      ],
      "refAllele":"G",
      "altAllele":"A",
      "lastUpdatedDate":"2020-03-01",
      "isAlleleSpecific":true
   },
   {
      "id":"RCV000030258.4",
      "variationId":"VCV000036581.3",
      "reviewStatus":"reviewed by expert panel",
      "alleleOrigins":[
         "germline"
      ],
      "refAllele":"G",
      "altAllele":"A",
      "phenotypes":[
         "Lynch syndrome"
      ],
      "medGenIds":[
         "C1333990"
      ],
      "omimIds":[
         "120435"
      ],
      "significance":[
         "benign"
      ],
      "lastUpdatedDate":"2017-05-01",
      "isAlleleSpecific":true
   }
]

large variants:

"clinvar":[
   {
      "chromosome":"1", 
      "begin":629025, 
      "end":8537745, 
      "variantType":"copy_number_loss", 
      "id":"RCV000051993.4", 
      "variationId":"VCV000058242.1", 
      "reviewStatus":"criteria provided, single submitter", 
      "alleleOrigins":[
         "not provided"
      ], 
      "phenotypes":[
         "See cases"
      ], 
      "significance":[
         "pathogenic"
      ], 
      "lastUpdatedDate":"2022-04-21", 
      "pubMedIds":[
         "21844811"
      ]
   },
   {
      "id":"VCV000058242.1",
      "reviewStatus":"criteria provided, single submitter",
      "significance":[
         "pathogenic"
      ],
      "lastUpdatedDate":"2022-04-21"
   },
        ......
]
FieldTypeNotes
idstringClinVar ID
variationIdstringClinVar VCV ID
variantTypestringvariant type
reviewStatusstringsee possible values below
alleleOriginsstring arraysee possible values below
refAllelestring
altAllelestring
phenotypesstring array
medGenIdsstring arrayMedGen IDs
omimIdsstring arrayOMIM IDs
orphanetIdsstring arrayOrphanet IDs
significancestring arraysee possible values below
lastUpdatedDatestringyyyy-MM-dd
pubMedIdsstring arrayPubMed IDs
isAlleleSpecificbooltrue when the current variant alternate allele matches the ClinVar alternate allele

reviewStatus:

  • no assertion provided
  • no assertion criteria provided
  • criteria provided, single submitter
  • practice guideline
  • classified by multiple submitters
  • criteria provided, conflicting interpretations
  • criteria provided, multiple submitters, no conflicts
  • no interpretation for the single variant

alleleOrigins:

  • unknown
  • other
  • germline
  • somatic
  • inherited
  • paternal
  • maternal
  • de-novo
  • biparental
  • uniparental
  • not-tested
  • tested-inconclusive

significance:

  • uncertain significance
  • not provided
  • benign
  • likely benign
  • likely pathogenic
  • pathogenic
  • drug response
  • histocompatibility
  • association
  • risk factor
  • protective
  • affects
  • conflicting data from submitters
  • other
  • no interpretation for the single variant
  • conflicting interpretations of pathogenicity

1000 Genomes

"oneKg":{
   "allAf":0.200879,
   "afrAf":0.210287,
   "amrAf":0.139769,
   "easAf":0.275794,
   "eurAf":0.181909,
   "sasAf":0.173824,
   "allAn":5008,
   "afrAn":1322,
   "amrAn":694,
   "easAn":1008,
   "eurAn":1006,
   "sasAn":978,
   "allAc":1006,
   "afrAc":278,
   "amrAc":97,
   "easAc":278,
   "eurAc":183,
   "sasAc":170
}
FieldTypeNotes
allAffloatallele frequency for all populations. Range: 0 - 1.0
allAcintallele count for all populations. Integer.
allAnintallele number for all populations. Non-zero integer.
afrAffloatallele frequency for the African super population. Range: 0 - 1.0
afrAcintallele count for the African super population. Integer.
afrAnintallele number for the African super population. Non-zero integer.
amrAffloatallele frequency for the Ad Mixed American super population. Range: 0 - 1.0
amrAcintallele count for the Ad Mixed American super population. Integer.
amrAnintallele number for the Ad Mixed American super population. Non-zero integer.
easAffloatallele frequency for the East Asian super population. Range: 0 - 1.0
easAcintallele count for the East Asian super population. Integer.
easAnintallele number for the East Asian super population. Non-zero integer.
eurAffloatallele frequency for the European super population. Range: 0 - 1.0
eurAcintallele count for the European super population. Integer.
eurAnintallele number for the European super population. Non-zero integer.
sasAffloatallele frequency for the South Asian super population. Range: 0 - 1.0
sasAcintallele count for the South Asian super population. Integer.
sasAnintallele number for the South Asian super population. Non-zero integer.

DANN

"dannScore": 0.27
FieldTypeNotes
dannScorefloatRange: 0 - 1.0

dbSNP

"dbsnp":[
   "rs1042821"
]
FieldTypeNotes
dbsnpstring arraydbSNP rsIDs

DECIPHER

"decipher":[
  {
    "chromosome":"1",
    "begin":13516,
    "end":91073,
    "numDeletions":27,
    "deletionFrequency":0.675,
    "numDuplications":27,
    "duplicationFrequency":0.675,
    "sampleSize":40,
    "reciprocalOverlap": 0.27555,
    "annotationOverlap": 0.5901
  }
],
FieldTypeNotes
chromosomeintEnsembl-style chromosome names
beginint1-based position
endint1-based position
numDeletionsint# of observed deletions
deletionFrequencyfloatdeletion frequency
numDuplicationsint# of observed duplications
duplicationFrequencyfloatduplication frequency
sampleSizeinttotal # of samples
reciprocalOverlapfloatRange: 0 - 1. E.g. 0.57 would indicate a 57% reciprocal overlap
annotationOverlapfloatRange: 0 - 1. E.g. 0.57 would indicate a 57% annotation overlap

GERP

"gerpScore": 1.27
FieldTypeNotes
gerpScorefloatRange: -∞ to +∞

GME Variome

"gmeVariome":{
   "allAc":10,
   "allAn":202,
   "allAf":0.049504,
   "failedFilter":true
}
FieldTypeNotes
allAcintGME allele count
allAnintGME allele number
allAffloatGME allele frequency
failedFilterboolTrue if this variant failed any filters

gnomAD

"gnomad":{ 
   "coverage":20,
   "allAf":0.190317,
   "maleAf":0.193,
   "femaleAf": 0.1935,
   "afrAf":0.222876,
   "amrAf":0.121394,
   "easAf":0.239802,
   "finAf":0.136833,
   "nfeAf":0.181282,
   "asjAf":0.258278,
   "othAf":0.186094,
   "allAn":30796,
   "maleAn":15096,
   "femaleAn":15700
   "afrAn":8664,
   "amrAn":832,
   "easAn":1618,
   "finAn":3486,
   "nfeAn":14916,
   "asjAn":302,
   "othAn":978,
   "allAc":5861,
   "maleAc":2930,
   "femaleAc": 2931,
   "afrAc":1931,
   "amrAc":101,
   "easAc":388,
   "finAc":477,
   "nfeAc":2704,
   "asjAc":78,
   "othAc":182,
   "allHc":561,
   "afrHc":208,
   "amrHc":6,
   "easHc":42,
   "finHc":31,
   "nfeHc":242,
   "asjHc":13,
   "othHc":19,
   "maleHc":280,
   "femaleHc":281,
   "controlsAllAf":0.190317,
   "controlsAllAn":30796,
   "controlsAllAc":5861,
   "lowComplexityRegion":true,
   "failedFilter":true
}
FieldTypeNotes
coverageintaverage coverage (non-negative integer values)
allAffloatallele frequency for all populations. Range: 0 - 1.0
maleAffloatallele frequency for male population. Range: 0 - 1.0
femaleAffloatallele frequency for female population. Range: 0 - 1.0
controlsAllAffloatallele frequency for the controls subset. Range: 0 - 1.0
allAcintallele count for all populations. Integer.
maleAcintallele count for male population. Integer.
femaleAcintallele count for female population. Integer.
controlsAllAcintallele count for the controls subset. Integer.
allAnintallele number for all populations. Non-zero integer.
maleAnintallele number for male population. Non-zero integer.
femaleAnintallele number for female population. Non-zero integer.
controlsAllAnintallele number for the controls subset. Non-zero integer.
allHcintcount of homozygous individuals for all populations. Non-negative integer.
maleHcintcount of homozygous individuals for male population. Non-negative integer.
femaleHcintcount of homozygous individuals for female population. Non-negative integer.
afrAffloatallele frequency for the African / African American population. Range: 0 - 1.0
afrAcintallele count for the African / African American population. Integer.
afrAnintallele number for the African / African American population. Non-zero integer.
afrHcintcount of homozygous individuals for African / African American population. Non-negative integer.
amrAffloatallele frequency for the Latino population. Range: 0 - 1.0
amrAcintallele count for the Latino population. Integer.
amrAnintallele number for the Latino population. Non-zero integer.
amrHcintcount of homozygous individuals for Latino population. Non-negative integer.
easAffloatallele frequency for the East Asian population. Range: 0 - 1.0
easAcintallele count for the East Asian population. Integer.
easAnintallele number for the East Asian population. Non-zero integer.
easHcintcount of homozygous individuals for East Asian population. Non-negative integer.
finAffloatallele frequency for the Finnish population. Range: 0 - 1.0
finAcintallele count for the Finnish population. Integer.
finAnintallele number for the Finnish population. Non-zero integer.
finHcintcount of homozygous individuals for Finnish population. Non-negative integer
nfeAffloatallele frequency for the Non-Finnish European population. Range: 0 - 1.0
nfeAcintallele count for the Non-Finnish European population. Integer.
nfeAnintallele number for the Non-Finnish European population. Non-zero integer.
nfeHcintcount of homozygous individuals for Non-Finnish European population. Non-negative integer
othAffloatallele frequency for the Other population. Range: 0 - 1.0
othAcintallele count for the Other population. Integer.
othAnintallele number for the Other population. Non-zero integer.
othHcintcount of homozygous individuals for Other population. Non-negative integer
asjAffloatallele frequency for the Ashkenazi Jewish population. Range: 0 - 1.0
asjAcintallele count for the Ashkenazi Jewish population Integer.
asjAnintallele number for the Ashkenazi Jewish population. Non-zero integer.
asjHcintcount of homozygous individuals for the Ashkenazi Jewish population. Non-negative integer
sasAffloatallele frequency for the South Asian population. Range: 0 - 1.0
sasAcintallele count for the South Asian population Integer.
sasAnintallele number for the South Asian population. Non-zero integer.
sasHcintcount of homozygous individuals for the South Asian population. Non-negative integer.
failedFilterboolTrue if this variant failed any filters (Note: we do not list the failed filters)
lowComplexityRegionboolTrue if this variant is located in a low complexity region.

MITOMAP

"mitomap":[ 
   { 
      "refAllele":"G",
      "altAllele":"A",
      "diseases":[ 
         "Bipolar disorder",
         "Melanoma"
      ],
      "hasHomoplasmy":false,
      "hasHeteroplasmy":true,
      "status":"Reported",
      "clinicalSignificance":"confirmed pathogenic",
      "scorePercentile":83.30,
      "numGenBankFullLengthSeqs":2,
      "pubMedIds":["2316527","6299878","6301949"],
      "isAlleleSpecific":true
   }
]
FieldTypeNotes
refAllelestring
altAllelestring
diseasesstring arrayassociated diseases
hasHomoplasmyboolean
hasHeteroplasmyboolean
statusstringrecord status
clinicalSignificancestringpredicted pathogenicity
scorePercentilefloatMitoTIP score
numGenBankFullLengthSeqsinteger# of GenBank full-length sequences
pubMedIdsstring array
isAlleleSpecificbooleantrue when the current variant alternate allele matches the MITOMAP alternate allele

Primate AI

GRCh38

"primateAI-3D": [
  {
    "aminoAcidPosition": 2,
    "refAminoAcid": "V",
    "altAminoAcid": "M",
    "score": 0.616944,
    "scorePercentile": 0.52,
    "ensemblTranscriptId": "ENST00000335137.4",
    "refSeqTranscriptId": "NM_001005484.1"
  }
]
FieldTypeNotes
aminoAcidPositionintAmino Acid Position (1-based)
refAminoAcidstringReference Amino Acid
altAminoAcidstringAlternate Amino Acid
ensemblTranscriptIdstringTranscript ID (Ensembl)
refSeqTranscriptIdstringTranscript ID (RefSeq)
scorePercentilefloatrange: 0 - 1.0
scorefloatrange: 0 - 1.0

GRCh37

"primateAI": [
   {
      "hgnc":"TP53",
      "scorePercentile":0.3,
   }
]
FieldTypeNotes
hgncstringHGNC Gene Symbol
scorePercentilefloatrange: 0 - 1.0

REVEL

"revel":{ 
   "score":0.027
}
FieldTypeNotes
scorefloatRange: 0 - 1.0

Splice AI

"spliceAI":[ 
   {
      "hgnc":"BLCAP",
      "acceptorGainDistance":-3,
      "acceptorGainScore":0.3,
      "donorLossDistance":7,
      "donorLossScore":0.9
   },
   { 
      "hgnc":"NNAT",
      "acceptorGainDistance":-1,
      "acceptorGainScore":0.2,
      "donorGainDistance":-2,
      "donorGainScore":0.3
   }
]
FieldTypeNotes
hgncstringHGNC gene symbol
acceptorGainDistanceint± bp from current position
acceptorGainScorefloatrange: 0 - 1.0. 1 decimal place
acceptorLossDistanceint± bp from current position
acceptorLossScorefloatrange: 0 - 1.0. 1 decimal place
donorGainDistanceint± bp from current position
donorGainScorefloatrange: 0 - 1.0. 1 decimal place
donorLossDistanceint± bp from current position
donorLossScorefloatrange: 0 - 1.0. 1 decimal place

TOPMed

"topmed":{ 
   "allAc":20,
   "allAn":125568,
   "allAf":0.000159,
   "allHc":0,
   "failedFilter":true
}
FieldTypeNotes
allAcintTOPMed allele count
allAnintTOPMed allele number. Non-zero integer.
allAffloatTOPMed allele frequency (computed by Illumina Connected Annotations)
allHcintTOPMed homozygous count
failedFilterboolTrue if this variant failed any filters

Genes

Illumina Connected Annotations repots gene annotations for all genes that have an overlapping variant with the exception of flanking variants (i.e. variants that only cause upstream_gene_variant or downstream_gene_variant).

"genes":[
   {
      "name":"MSH6",
      "hgncId":7329,
      "summary":"This gene encodes a member of the DNA mismatch repair MutS family. In E. coli, the MutS protein helps in the recognition of mismatched nucleotides prior to their repair. A highly conserved region of approximately 150 aa, called the Walker-A adenine nucleotide binding motif, exists in MutS homologs. The encoded protein heterodimerizes with MSH2 to form a mismatch recognition complex that functions as a bidirectional molecular switch that exchanges ADP and ATP as DNA mismatches are bound and dissociated. Mutations in this gene may be associated with hereditary nonpolyposis colon cancer, colorectal cancer, and endometrial cancer. Transcripts variants encoding different isoforms have been described. [provided by RefSeq, Jul 2013]",
      /* this is where gene-level data sources can be found e.g. OMIM */
   }
]
FieldTypeNotes
namestringHGNC gene symbol
hgncIdintHGNC ID
summarystringshort description of the gene from OMIM

OMIM

"omim":[ 
   { 
      "mimNumber":600678,
      "geneName":"MutS, E. coli, homolog of, 6",
      "description":"The transcription factor p53 responds to diverse cellular stresses to regulate target genes that induce cell cycle arrest, apoptosis, senescence, DNA repair, or changes in metabolism. In addition, p53 appears to induce apoptosis through nontranscriptional cytoplasmic processes. In unstressed cells, p53 is kept inactive essentially through the actions of the ubiquitin ligase MDM2, which inhibits p53 transcriptional activity and ubiquitinates p53 to promote its degradation. Numerous posttranslational modifications modulate p53 activity, most notably phosphorylation and acetylation. Several less abundant p53 isoforms also modulate p53 activity. Activity of p53 is ubiquitously lost in human cancer either by mutation of the p53 gene itself or by loss of cell signaling upstream or downstream of p53 (Toledo and Wahl, 2006; Bourdon, 2007; Vousden and Lane, 2007)",
      "phenotypes":[ 
         { 
            "mimNumber":614350,
            "phenotype":"Colorectal cancer, hereditary nonpolyposis, type 5",
            "description":"Hereditary nonpolyposis colorectal cancer type 5 is a cancer predisposition syndrome ...",
            "mapping":"molecular basis of the disorder is known",
            "inheritances":[ 
               "Autosomal dominant"
            ]
         },
         { 
            "mimNumber":608089,
            "phenotype":"Endometrial cancer, familial",
            "mapping":"molecular basis of the disorder is known"
         },
         { 
            "mimNumber":276300,
            "phenotype":"Mismatch repair cancer syndrome",
            "description":"Constitutional mismatch repair deficiency is a rare childhood cancer predisposition syndrome ...",
            "mapping":"molecular basis of the disorder is known",
            "inheritances":[ 
               "Autosomal recessive"
            ],
            "comments"     : [
                "contribute to susceptibility to multifactorial disorders or to susceptibility to infection",
                "unconfirmed or possibly spurious mapping"
            ]
         }
      ]
   }
]
FieldTypeNotes
mimNumberintOMIM ID for gene
geneNamestringgene name
descriptionstring
phenotypesobject arraysee Phenotype entry below

Phenotype

FieldTypeNotes
mimNumberint
phenotypestring
descriptionstring
mappingstringsee possible values below
inheritancestring arraysee possible values below
commentsstring arraysee possible values below

Mapping

  1. disorder was positioned by mapping of the wild type gene
  2. disease phenotype itself was mapped
  3. molecular basis of the disorder is known
  4. disorder is a chromosome deletion or duplication syndrome

Inheritance

  • autosomal recessive
  • autosomal dominant

Comments

  • contributes to the susceptibility to multifactorial disorders
  • variations that lead to apparently abnormal laboratory test values
  • unconfirmed mapping

gnomAD LoF Gene Metrics

"gnomAD":{ 
   "pLi":1.00e0,
   "pNull":8.94e-40,
   "pRec":1.84e-16,
   "synZ":-8.44e-2,
   "misZ":5.96e-1,
   "loeuf":1.13e0
}
FieldTypeNotes
pLifloatprobability of being intolerant of a single loss-of-function variant (like haploinsufficient genes, observed ~ 0.1*expected)
pNullfloatprobability of being completely tolerant of loss of function variation (observed = expected)
pRecfloatprobability of being intolerant of two loss of function variants (like recessive genes, observed ~ 0.5*expected)
synZfloatcorrected synonymous Z score
misZfloatcorrected missense Z score
loeuffloatloss of function observed/expected upper bound fraction (LOEUF)

ClinGen Disease Validity

"clingenGeneValidity":[
   {
      "diseaseId":"MONDO_0007893",
      "disease":"Noonan syndrome with multiple lentigines",
      "classification":"no reported evidence",
      "classificationDate":"2018-06-07"
   },
   {
      "diseaseId":"MONDO_0015280",
      "disease":"cardiofaciocutaneous syndrome",
      "classification":"no reported evidence",
      "classificationDate":"2018-06-07"
   }
]
FieldTypeNotes
clingenGeneValidityobject
diseaseIdstringMonarch Disease Ontology ID (MONDO)
diseasestringdisease label
classificationstringsee below for possible values
classificationDatestringyyyy-MM-dd

classification

  • no reported evidence
  • disputed
  • limited
  • moderate
  • definitive
  • strong
  • refuted
  • no known disease relationship

COSMIC Cancer Gene Census

   {
  "name": "PRDM16",
  "hgncId": 14000,
  "ncbiGeneId": "63976",
  "ensemblGeneId": "ENSG00000142611",
  "cosmic": {
    "roleInCancer": [
      "oncogene",
      "fusion"
    ]
  }
}
FieldTypeNotes
roleInCancerstring arrayPossible roles in caner
+ + \ No newline at end of file diff --git a/3.22/index.html b/3.22/index.html index 5a54b19e..209ee02d 100644 --- a/3.22/index.html +++ b/3.22/index.html @@ -6,16 +6,16 @@ Introduction | IlluminaConnectedAnnotations - - + +
Skip to main content
Version: 3.22

Illumina Connected Annotations provides translational research-grade annotation of genomic variants (SNVs, MNVs, insertions, deletions, indels, STRs, gene fusions, and SVs (including CNVs). It can be run as a stand-alone package, or integrated into larger software tools that require variant annotation.

The input to Illumina Connected Annotations are VCFs and the output is a structured JSON representation of all annotation and sample information (as extracted from the VCF). Illumina Connected Annotations handles multiple alternate alleles and multiple samples with ease.

The software is being developed under a rigorous SDLC and testing process to ensure accuracy of the results and enable embedding in other software. Illumina Connected Annotations uses a continuous integration pipeline where millions of variant annotations are monitored against baseline values daily.

What does Illumina Connected Annotations annotate?

We use Sequence Ontology consequences to describe how each variant impacts a given transcript:

The transcript and gene models are obtained from RefSeq and Ensembl. The current officially supported versions are:

Data SourceVersionRelease Date
RefSeqGCF_000001405.40-RS_2023_032023-03-21
Ensembl1102023-04-27

In addition, it uses external data sources to provide additional context for each variant. Illumina Connected Annotations provides annotations from the following sources divided into 2 tiers: Professional and basic. -The basic tier can be accessed free of charge. The professional tier requires a license. For access, please contact annotation_support@illumina.com.

Data SourceAvailabilityLatest Supported Version
Primate AI-3DProfessional1.0
Splice AIProfessional1.3
COSMICProfessional96
OMIMProfessional20231105
ClinVarBasic20231028
1000 Genomes ProjectBasicPhase 3 v3plus
DANNBasic20200205
dbSNPBasic156
DECIPHERBasic201509
GERPBasic20110522
GME VariomeBasic20160618
gnomADBasic3.1.2
MITOMAPBasic20200819
REVELBasic20200205
TOPMedBasicfreeze 5
Cancer HotspotsBasic2017
FusionCatcherBasic1.33
ClinGenBasic20231105
MultiZ 100 wayBasic20171006

Download

Please visit Illumina Connected Annotations.

- - +The basic tier can be accessed free of charge. The professional tier requires a license. For access, please contact annotation_support@illumina.com.

Data SourceAvailabilityLatest Supported Version
Primate AI-3DProfessional1.0
Splice AIProfessional1.3
COSMICProfessional96
OMIMProfessional20231105
ClinVarBasic20231028
1000 Genomes ProjectBasicPhase 3 v3plus
DANNBasic20200205
dbSNPBasic156
DECIPHERBasic201509
GERPBasic20110522
GME VariomeBasic20160618
gnomADBasic3.1.2
MITOMAPBasic20200819
REVELBasic20200205
TOPMedBasicfreeze 5
Cancer HotspotsBasic2017
FusionCatcherBasic1.33
ClinGenBasic20231105
MultiZ 100 wayBasic20171006

Download

Please visit Illumina Connected Annotations.

+ + \ No newline at end of file diff --git a/3.22/introduction/dependencies/index.html b/3.22/introduction/dependencies/index.html index ebe630c9..21480c55 100644 --- a/3.22/introduction/dependencies/index.html +++ b/3.22/introduction/dependencies/index.html @@ -6,13 +6,13 @@ Dependencies | IlluminaConnectedAnnotations - - + +
-
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Version: 3.22

Dependencies

All of the following dependencies have been included in this repository.

NameLicenseUsage
Amazon.LambdaApacheAWS extensions for .NET CLI
AWSSDKApacheAWS Lambda, S3, SNS support
Json.NETMITJASIX utility
libdeflateMITBlockCompression library
MoqBSDMocking framework for unit tests
NDesk.OptionsMIT/X11CommandLine library
xUnitApacheUnit testing framework
zlib-ngzlibBlockCompression library
zstdBSDBlockCompression library
- - +
Skip to main content
Version: 3.22

Dependencies

All of the following dependencies have been included in this repository.

NameLicenseUsage
Amazon.LambdaApacheAWS extensions for .NET CLI
AWSSDKApacheAWS Lambda, S3, SNS support
Json.NETMITJASIX utility
libdeflateMITBlockCompression library
MoqBSDMocking framework for unit tests
NDesk.OptionsMIT/X11CommandLine library
xUnitApacheUnit testing framework
zlib-ngzlibBlockCompression library
zstdBSDBlockCompression library
+ + \ No newline at end of file diff --git a/3.22/introduction/getting-started/index.html b/3.22/introduction/getting-started/index.html index 0a66e7f4..0f175270 100644 --- a/3.22/introduction/getting-started/index.html +++ b/3.22/introduction/getting-started/index.html @@ -6,13 +6,13 @@ Getting Started | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

Getting Started

Illumina Connected Annotations is written in C# using .NET Core (an amazing runtime environment that currently runs on Windows, Linux, Mac OS X, and in Docker images). Once .NET Core has been downloaded, all you need to do is grab the source, compile it, and grab the data files.

tip

Illumina Connected Annotations currently uses .NET6.0. Please make sure that you have the most current runtime from the .NET Core downloads page.

Getting Illumina Connected Annotations

Latest Release

Please visit Illumina Connected Annotations. to obtain the latest release.

mkdir -p IlluminaConnectedAnnotations/Data
cd IlluminaConnectedAnnotations
unzip IlluminaConnectedAnnotations-3.22.0-0-gc13dcb61-net6.0.zip

Quick Start

If you want to get started right away, we've created a script that unzips the Illumina Connected Annotations build, downloads the annotation data, and starts annotating a test file:

bash ./TestIlluminaConnectedAnnotations.sh IlluminaConnectedAnnotationsBuild.zip

We have verified that this script works on Windows (using Git Bash or WSL), Linux, and Mac OS X.

Docker

Obtain the docker image in a zip file (e.g. IlluminaConnectedAnnotations-3.22.0-0-gc13dcb61-net6.0-docker.tar.gz), and load it as follows

docker load < IlluminaConnectedAnnotations-3.22.0-0-gc13dcb61-net6.0-docker.tar.gz

If you want to build your own docker image, it is really easy to do. You just need to have Illumina Connected Annotations zip file and then download the Dockerfile and this script.

Put both files (create_docker_image.sh and Dockerfile) inside the same folder.

In terminal, execute command below inside the folder where you put those scripts:

chmod +x create_docker_image.sh
./create_docker_image.sh [path to zip file] [image tag]

After you run the script, the docker image will be available in your local machine with image name illumina-connected-annotations:[image tag specified].

For Docker, we have special instructions for running the Downloader:

docker run --rm -it -v local/data/folder:/scratch illumina-connected-annotations:v3.22.0 Downloader --ga GRCh37 -o /scratch

Similarly, we have special instructions for running IlluminaConnectedAnnotations (Here's a toy VCF in case you need it):

docker run --rm -it -v local/data/folder:/scratch illumina-connected-annotations:v3.22.0 IlluminaConnectedAnnotations -c /scratch/Cache/ \
     -r /scratch/References/Homo_sapiens.GRCh37.Nirvana.dat \
     --sd /scratch/SupplementaryAnnotation/GRCh37 \
     -i /scratch/HiSeq.10000.vcf.gz -o /scratch/HiSeq
caution

Please note that since our data files are usually accessed through a Docker volume, there is a noticeable performance penalty when running Illumina Connected Annotations in Docker.

tip

For convenience, the user is encouraged to create aliases for the docker commands. For example:

alias IlluminaConnectedAnnotations="docker run --rm -it -v local/data/folder:/scratch illumina-connected-annotations:v3.22.0 IlluminaConnectedAnnotations"

Downloading the data files

To download the latest data sources (or update the ones that you already have), use the following command to automate the download from S3:

dotnet bin/Release/net6.0/Downloader.dll \
     --ga GRCh37 \
     -o Data
  • the --ga argument specifies the genome assembly which can be GRCh37, GRCh38, or both.
  • the -o argument specifies the output directory
Glitches in the Matrix

Every once in a while, the download process does not go smoothly. Perhaps the internet connection cut out or you ran out of disk space. The Downloader attempts to detect these situations by checking the file sizes at the very end. If you see that a file was marked truncated, try fixing the root cause and running the downloader again.

tip

From time to time, you can re-run the Downloader to get the latest annotation files. It will only download the files that changed.

Download a test VCF file

Here's a toy VCF file you can play around with:

curl -O https://illumina.github.io/IlluminaConnectedAnnotationsDocumentation/files/HiSeq.10000.vcf.gz

Running Illumina Connected Annotations

Once you have downloaded the data sets, use the following command to annotate your VCF:

dotnet Annotator.dll \
     -c Data/Cache \
     --sd Data/SupplementaryAnnotation/GRCh37 \
     -r Data/References/Homo_sapiens.GRCh37.Nirvana.dat \
     -i HiSeq.10000.vcf.gz \
     -o HiSeq.10000
  • the -c argument specifies the cache directory
  • the --sd argument specifies the supplementary annotation directory
  • the -r argument specifies the compressed reference path
  • the -i argument specifies the input VCF path
  • the -o argument specifies the output filename prefix

When running Illumina Connected Annotations, performance metrics are shown as it evaluates each chromosome in the input VCF file:

---------------------------------------------------------------------------
Illumina Connected Annotations                      (c) 2023 Illumina, Inc.
                                                                     3.22.0
---------------------------------------------------------------------------

Initialization                                         Time     Positions/s
---------------------------------------------------------------------------
Cache                                               00:00:00.0
SA Position Scan                                    00:00:00.0      153,634

Reference                                Preload    Annotation   Variants/s
---------------------------------------------------------------------------
chr1                                    00:00:00.2  00:00:00.8       11,873

Summary                                                Time         Percent
---------------------------------------------------------------------------
Initialization                                      00:00:00.0        1.5 %
Preload                                             00:00:00.2        4.9 %
Annotation                                          00:00:00.8       18.5 %

Time: 00:00:04.4

The output will be a JSON file called HiSeq.10000.json.gz. Here's the full JSON file.

The Illumina Connected Annotations command line

The full command line options can be viewed by using the -h option or no options

dotnet Annotator.dll
---------------------------------------------------------------------------
Illumina Connected Annotations                      (c) 2023 Illumina, Inc.
                                                                     3.22.0
---------------------------------------------------------------------------

USAGE: dotnet Annotator.dll -i <vcf path> -c <cache dir> --sd <sa dir> -r <ref path> -o <base output filename>
Annotates a set of variants

OPTIONS:
      --cache, -c <directory>
                             input cache directory
      --in, -i <path>        input VCF path
      --out, -o <file path>  output file path
      --ref, -r <path>       input compressed reference sequence path
      --sd <directory>       input supplementary annotation directory
      --sources, -s <VALUE>  annotation data sources to be used (comma
                               separated list of supported tags)
      --force-mt             forces to annotate mitochondrial variants
      --legacy-vids          enables support for legacy VIDs
      --enable-dq            report DQ from VCF samples field
      --enable-bidirectional-fusions
                             enables support for bidirectional gene fusions
      --str <VALUE>          user provided STR annotation TSV file
      --vcf-info <VALUE>     additional vcf info field keys (comma separated)
                               desired in the output
      --vcf-sample-info <VALUE>
                             additional vcf format field keys (comma separated)
                                desired in the output
      --help, -h             displays the help menu
      --version, -v          displays the version

Supplementary annotation version: 69, Reference version: 7

Specifying annotation sources

By default, Illumina Connected Annotations will use all available data sources. However, the user can customize the set of sources using the --sources|-s option. If an unknown source is specified, a warning message will be printed.

dotnet Annotator.dll \
     -c Data/Cache/GRCh37 \
     --sd Data/SupplementaryAnnotation/GRCh37 \
     -r Data/References/Homo_sapiens.GRCh37.Nirvana.dat \
     -i HiSeq.10000.vcf.gz \
     -o HiSeq.10000 \
     -s omim,gnomad,ense
 ---------------------------------------------------------------------------
 Illumina Connected Annotations                      (c) 2023 Illumina, Inc.
                                                                      3.22.0
 ---------------------------------------------------------------------------

 WARNING: Unknown tag in data-sources: ense.
 Available values are: aminoAcidConservation,primateAI,dbsnp,spliceAI,revel,cosmic,clinvar,gnomad,
 mitomap,oneKg,gmeVariome,topmed,clingen,decipher,gnomAD-preview,clingenDosageSensitivityMap,
 gerpScore,dannScore,omim,clingenGeneValidity,phylopScore,lowComplexityRegion,refMinor,
 heteroplasmy,Ensembl,RefSeq

 Initialization                                         Time     Positions/s
 ---------------------------------------------------------------------------
 SA Position Scan                                    00:00:00.3      307,966
 ....
 ..

The list of available values is compiled from the files provided (using -c and --sd options).

- - +
Skip to main content
Version: 3.22

Getting Started

Illumina Connected Annotations is written in C# using .NET Core (an amazing runtime environment that currently runs on Windows, Linux, Mac OS X, and in Docker images). Once .NET Core has been downloaded, all you need to do is grab the source, compile it, and grab the data files.

tip

Illumina Connected Annotations currently uses .NET6.0. Please make sure that you have the most current runtime from the .NET Core downloads page.

Getting Illumina Connected Annotations

Latest Release

Please visit Illumina Connected Annotations. to obtain the latest release.

mkdir -p IlluminaConnectedAnnotations/Data
cd IlluminaConnectedAnnotations
unzip IlluminaConnectedAnnotations-3.22.0-0-gc13dcb61-net6.0.zip

Quick Start

If you want to get started right away, we've created a script that unzips the Illumina Connected Annotations build, downloads the annotation data, and starts annotating a test file:

bash ./TestIlluminaConnectedAnnotations.sh IlluminaConnectedAnnotationsBuild.zip

We have verified that this script works on Windows (using Git Bash or WSL), Linux, and Mac OS X.

Docker

Obtain the docker image in a zip file (e.g. IlluminaConnectedAnnotations-3.22.0-0-gc13dcb61-net6.0-docker.tar.gz), and load it as follows

docker load < IlluminaConnectedAnnotations-3.22.0-0-gc13dcb61-net6.0-docker.tar.gz

If you want to build your own docker image, it is really easy to do. You just need to have Illumina Connected Annotations zip file and then download the Dockerfile and this script.

Put both files (create_docker_image.sh and Dockerfile) inside the same folder.

In terminal, execute command below inside the folder where you put those scripts:

chmod +x create_docker_image.sh
./create_docker_image.sh [path to zip file] [image tag]

After you run the script, the docker image will be available in your local machine with image name illumina-connected-annotations:[image tag specified].

For Docker, we have special instructions for running the Downloader:

docker run --rm -it -v local/data/folder:/scratch illumina-connected-annotations:v3.22.0 Downloader --ga GRCh37 -o /scratch

Similarly, we have special instructions for running IlluminaConnectedAnnotations (Here's a toy VCF in case you need it):

docker run --rm -it -v local/data/folder:/scratch illumina-connected-annotations:v3.22.0 IlluminaConnectedAnnotations -c /scratch/Cache/ \
     -r /scratch/References/Homo_sapiens.GRCh37.Nirvana.dat \
     --sd /scratch/SupplementaryAnnotation/GRCh37 \
     -i /scratch/HiSeq.10000.vcf.gz -o /scratch/HiSeq
caution

Please note that since our data files are usually accessed through a Docker volume, there is a noticeable performance penalty when running Illumina Connected Annotations in Docker.

tip

For convenience, the user is encouraged to create aliases for the docker commands. For example:

alias IlluminaConnectedAnnotations="docker run --rm -it -v local/data/folder:/scratch illumina-connected-annotations:v3.22.0 IlluminaConnectedAnnotations"

Downloading the data files

To download the latest data sources (or update the ones that you already have), use the following command to automate the download from S3:

dotnet bin/Release/net6.0/Downloader.dll \
     --ga GRCh37 \
     -o Data
  • the --ga argument specifies the genome assembly which can be GRCh37, GRCh38, or both.
  • the -o argument specifies the output directory
Glitches in the Matrix

Every once in a while, the download process does not go smoothly. Perhaps the internet connection cut out or you ran out of disk space. The Downloader attempts to detect these situations by checking the file sizes at the very end. If you see that a file was marked truncated, try fixing the root cause and running the downloader again.

tip

From time to time, you can re-run the Downloader to get the latest annotation files. It will only download the files that changed.

Download a test VCF file

Here's a toy VCF file you can play around with:

curl -O https://illumina.github.io/IlluminaConnectedAnnotationsDocumentation/files/HiSeq.10000.vcf.gz

Running Illumina Connected Annotations

Once you have downloaded the data sets, use the following command to annotate your VCF:

dotnet Annotator.dll \
     -c Data/Cache \
     --sd Data/SupplementaryAnnotation/GRCh37 \
     -r Data/References/Homo_sapiens.GRCh37.Nirvana.dat \
     -i HiSeq.10000.vcf.gz \
     -o HiSeq.10000
  • the -c argument specifies the cache directory
  • the --sd argument specifies the supplementary annotation directory
  • the -r argument specifies the compressed reference path
  • the -i argument specifies the input VCF path
  • the -o argument specifies the output filename prefix

When running Illumina Connected Annotations, performance metrics are shown as it evaluates each chromosome in the input VCF file:

---------------------------------------------------------------------------
Illumina Connected Annotations                      (c) 2023 Illumina, Inc.
                                                                     3.22.0
---------------------------------------------------------------------------

Initialization                                         Time     Positions/s
---------------------------------------------------------------------------
Cache                                               00:00:00.0
SA Position Scan                                    00:00:00.0      153,634

Reference                                Preload    Annotation   Variants/s
---------------------------------------------------------------------------
chr1                                    00:00:00.2  00:00:00.8       11,873

Summary                                                Time         Percent
---------------------------------------------------------------------------
Initialization                                      00:00:00.0        1.5 %
Preload                                             00:00:00.2        4.9 %
Annotation                                          00:00:00.8       18.5 %

Time: 00:00:04.4

The output will be a JSON file called HiSeq.10000.json.gz. Here's the full JSON file.

The Illumina Connected Annotations command line

The full command line options can be viewed by using the -h option or no options

dotnet Annotator.dll
---------------------------------------------------------------------------
Illumina Connected Annotations                      (c) 2023 Illumina, Inc.
                                                                     3.22.0
---------------------------------------------------------------------------

USAGE: dotnet Annotator.dll -i <vcf path> -c <cache dir> --sd <sa dir> -r <ref path> -o <base output filename>
Annotates a set of variants

OPTIONS:
      --cache, -c <directory>
                             input cache directory
      --in, -i <path>        input VCF path
      --out, -o <file path>  output file path
      --ref, -r <path>       input compressed reference sequence path
      --sd <directory>       input supplementary annotation directory
      --sources, -s <VALUE>  annotation data sources to be used (comma
                               separated list of supported tags)
      --force-mt             forces to annotate mitochondrial variants
      --legacy-vids          enables support for legacy VIDs
      --enable-dq            report DQ from VCF samples field
      --enable-bidirectional-fusions
                             enables support for bidirectional gene fusions
      --str <VALUE>          user provided STR annotation TSV file
      --vcf-info <VALUE>     additional vcf info field keys (comma separated)
                               desired in the output
      --vcf-sample-info <VALUE>
                             additional vcf format field keys (comma separated)
                                desired in the output
      --help, -h             displays the help menu
      --version, -v          displays the version

Supplementary annotation version: 69, Reference version: 7

Specifying annotation sources

By default, Illumina Connected Annotations will use all available data sources. However, the user can customize the set of sources using the --sources|-s option. If an unknown source is specified, a warning message will be printed.

dotnet Annotator.dll \
     -c Data/Cache/GRCh37 \
     --sd Data/SupplementaryAnnotation/GRCh37 \
     -r Data/References/Homo_sapiens.GRCh37.Nirvana.dat \
     -i HiSeq.10000.vcf.gz \
     -o HiSeq.10000 \
     -s omim,gnomad,ense
 ---------------------------------------------------------------------------
 Illumina Connected Annotations                      (c) 2023 Illumina, Inc.
                                                                      3.22.0
 ---------------------------------------------------------------------------

 WARNING: Unknown tag in data-sources: ense.
 Available values are: aminoAcidConservation,primateAI,dbsnp,spliceAI,revel,cosmic,clinvar,gnomad,
 mitomap,oneKg,gmeVariome,topmed,clingen,decipher,gnomAD-preview,clingenDosageSensitivityMap,
 gerpScore,dannScore,omim,clingenGeneValidity,phylopScore,lowComplexityRegion,refMinor,
 heteroplasmy,Ensembl,RefSeq

 Initialization                                         Time     Positions/s
 ---------------------------------------------------------------------------
 SA Position Scan                                    00:00:00.3      307,966
 ....
 ..

The list of available values is compiled from the files provided (using -c and --sd options).

+ + \ No newline at end of file diff --git a/3.22/introduction/parsing-json/index.html b/3.22/introduction/parsing-json/index.html index fec5f2e8..97937a36 100644 --- a/3.22/introduction/parsing-json/index.html +++ b/3.22/introduction/parsing-json/index.html @@ -6,13 +6,13 @@ Parsing Illumina Connected Annotations JSON | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

Parsing Illumina Connected Annotations JSON

Parsing JSON

Our JSON files are organized similarly to original VCF variants:

Illumina Connected Annotations JSON files can get very large and sometimes we receive feedback that a bioinformatician tried to read the JSON file into Python or R resulting in a program that ran out of available RAM. This happens because those parsers try to load everything into memory all at once.

To get around those issues, we play some clever tricks with newlines that enables our users to parse our JSON files quickly and efficiently.

Organization

Our JSON file is arranged as follows:

  • the header section is located on the first line
  • each line after that corresponds to a position (same as a row in a VCF file)
    • until you reach the genes section ],"genes":[
  • each line after that corresponds to a gene
    • until you reach the end ]}

Knowing this, you can load each position line as an independent JSON object and extract the information you need.

Jupyter Notebook

To demonstrate this, we have put together a Jupyter notebook demonstrating how to do this in Python and a R version as well.

JASIX

One of the tools that we really like in the VCF ecosystem is tabix. Unfortunately, tabix only works for tab-delimited file formats. As a result, we created a similar tool for Illumina Connected Annotations JSON files called JASIX.

Here's an example of how you might use JASIX:

dotnet bin/Release/net6.0/Jasix.dll -i dragen.json.gz -q chr1:942450-942455
  • the -i argument specifies the Illumina Connected Annotations JSON path
  • the -q argument specifies a genomic range (you can use as many of these as you want)

JASIX also includes additional options for showing the Illumina Connected Annotations header or for extracting different sections (like the genes section).

The output from JASIX is compliant JSON object shown in pretty-printed form:

{"positions":[
{
  "chromosome": "chr1",
  "position": 942451,
  "refAllele": "T",
  "altAlleles": [
    "C"
  ],
  "quality": 484.23,
  "filters": [
    "PASS"
  ],
  "cytogeneticBand": "1p36.33",
  "samples": [
    {
      "genotype": "1/1",
      "variantFrequencies": [
        1
      ],
      "totalDepth": 21,
      "genotypeQuality": 60,
      "alleleDepths": [
        0,
        21
      ]
    },
    {
      "genotype": "1/1",
      "variantFrequencies": [
        1
      ],
      "totalDepth": 32,
      "genotypeQuality": 93,
      "alleleDepths": [
        0,
        32
      ]
    },
    {
      "genotype": "1/1",
      "variantFrequencies": [
        1
      ],
      "totalDepth": 36,
      "genotypeQuality": 105,
      "alleleDepths": [
        0,
        36
      ]
    }
  ],
  "variants": [
    {
      "vid": "1-942451-T-C",
      "chromosome": "chr1",
      "begin": 942451,
      "end": 942451,
      "refAllele": "T",
      "altAllele": "C",
      "variantType": "SNV",
      "hgvsg": "NC_000001.11:g.942451T>C",
      "phylopScore": -0.1,
      "clinvar": [
        {
          "id": "VCV000836156.1",
          "reviewStatus": "criteria provided, single submitter",
          "significance": [
            "uncertain significance"
          ],
          "refAllele": "T",
          "altAllele": "T",
          "lastUpdatedDate": "2020-08-20"
        },
        {
          "id": "RCV001037211.1",
          "variationId": 836156,
          "reviewStatus": "criteria provided, single submitter",
          "alleleOrigins": [
            "germline"
          ],
          "refAllele": "T",
          "altAllele": "T",
          "phenotypes": [
            "not provided"
          ],
          "medGenIds": [
            "CN517202"
          ],
          "significance": [
            "uncertain significance"
          ],
          "lastUpdatedDate": "2020-08-20",
          "pubMedIds": [
            "28492532"
          ]
        }
      ],
      "dbsnp": [
        "rs6672356"
      ],
      "gnomad": {
        "coverage": 25,
        "allAf": 0.999855,
        "allAn": 123742,
        "allAc": 123724,
        "allHc": 61853,
        "afrAf": 0.999416,
        "afrAn": 10278,
        "afrAc": 10272,
        "afrHc": 5133,
        "amrAf": 0.99995,
        "amrAn": 20008,
        "amrAc": 20007,
        "amrHc": 10003,
        "easAf": 1,
        "easAn": 6054,
        "easAc": 6054,
        "easHc": 3027,
        "finAf": 1,
        "finAn": 8696,
        "finAc": 8696,
        "finHc": 4348,
        "nfeAf": 0.999899,
        "nfeAn": 49590,
        "nfeAc": 49585,
        "nfeHc": 24790,
        "asjAf": 1,
        "asjAn": 7208,
        "asjAc": 7208,
        "asjHc": 3604,
        "sasAf": 0.99967,
        "sasAn": 18160,
        "sasAc": 18154,
        "sasHc": 9074,
        "othAf": 1,
        "othAn": 3748,
        "othAc": 3748,
        "othHc": 1874,
        "maleAf": 0.9999,
        "maleAn": 69780,
        "maleAc": 69773,
        "maleHc": 34883,
        "femaleAf": 0.999796,
        "femaleAn": 53962,
        "femaleAc": 53951,
        "femaleHc": 26970,
        "controlsAllAf": 0.999815,
        "controlsAllAn": 48654,
        "controlsAllAc": 48645
      },
      "oneKg": {
        "allAf": 1,
        "afrAf": 1,
        "amrAf": 1,
        "easAf": 1,
        "eurAf": 1,
        "sasAf": 1,
        "allAn": 5008,
        "afrAn": 1322,
        "amrAn": 694,
        "easAn": 1008,
        "eurAn": 1006,
        "sasAn": 978,
        "allAc": 5008,
        "afrAc": 1322,
        "amrAc": 694,
        "easAc": 1008,
        "eurAc": 1006,
        "sasAc": 978
      },
      "primateAI": [
        {
          "hgnc": "SAMD11",
          "scorePercentile": 0.87
        }
      ],
      "revel": {
        "score": 0.145
      },
      "topmed": {
        "allAf": 0.999809,
        "allAn": 125568,
        "allAc": 125544,
        "allHc": 62760
      },
      "transcripts": [
        {
          "transcript": "ENST00000420190.6",
          "source": "Ensembl",
          "bioType": "protein_coding",
          "geneId": "ENSG00000187634",
          "hgnc": "SAMD11",
          "consequence": [
            "downstream_gene_variant"
          ],
          "proteinId": "ENSP00000411579.2"
        },
        {
          "transcript": "ENST00000342066.7",
          "source": "Ensembl",
          "bioType": "protein_coding",
          "codons": "Tgg/Cgg",
          "aminoAcids": "W/R",
          "cdnaPos": "1110",
          "cdsPos": "1027",
          "exons": "10/14",
          "proteinPos": "343",
          "geneId": "ENSG00000187634",
          "hgnc": "SAMD11",
          "consequence": [
            "missense_variant"
          ],
          "hgvsc": "ENST00000342066.7:c.1027T>C",
          "hgvsp": "ENSP00000342313.3:p.(Trp343Arg)",
          "polyPhenScore": 0,
          "polyPhenPrediction": "benign",
          "proteinId": "ENSP00000342313.3",
          "siftScore": 1,
          "siftPrediction": "tolerated"
        },
        {
          "transcript": "ENST00000618181.4",
          "source": "Ensembl",
          "bioType": "protein_coding",
          "codons": "Tgg/Cgg",
          "aminoAcids": "W/R",
          "cdnaPos": "732",
          "cdsPos": "652",
          "exons": "7/11",
          "proteinPos": "218",
          "geneId": "ENSG00000187634",
          "hgnc": "SAMD11",
          "consequence": [
            "missense_variant"
          ],
          "hgvsc": "ENST00000618181.4:c.652T>C",
          "hgvsp": "ENSP00000480870.1:p.(Trp218Arg)",
          "polyPhenScore": 0,
          "polyPhenPrediction": "benign",
          "proteinId": "ENSP00000480870.1",
          "siftScore": 1,
          "siftPrediction": "tolerated"
        },
        {
          "transcript": "ENST00000622503.4",
          "source": "Ensembl",
          "bioType": "protein_coding",
          "codons": "Tgg/Cgg",
          "aminoAcids": "W/R",
          "cdnaPos": "1110",
          "cdsPos": "1030",
          "exons": "10/14",
          "proteinPos": "344",
          "geneId": "ENSG00000187634",
          "hgnc": "SAMD11",
          "consequence": [
            "missense_variant"
          ],
          "hgvsc": "ENST00000622503.4:c.1030T>C",
          "hgvsp": "ENSP00000482138.1:p.(Trp344Arg)",
          "isCanonical": true,
          "polyPhenScore": 0,
          "polyPhenPrediction": "benign",
          "proteinId": "ENSP00000482138.1",
          "siftScore": 1,
          "siftPrediction": "tolerated"
        },
        {
          "transcript": "ENST00000618323.4",
          "source": "Ensembl",
          "bioType": "protein_coding",
          "codons": "cTg/cCg",
          "aminoAcids": "L/P",
          "cdnaPos": "712",
          "cdsPos": "632",
          "exons": "8/12",
          "proteinPos": "211",
          "geneId": "ENSG00000187634",
          "hgnc": "SAMD11",
          "consequence": [
            "missense_variant"
          ],
          "hgvsc": "ENST00000618323.4:c.632T>C",
          "hgvsp": "ENSP00000480678.1:p.(Leu211Pro)",
          "polyPhenScore": 0,
          "polyPhenPrediction": "unknown",
          "proteinId": "ENSP00000480678.1",
          "siftScore": 0.03,
          "siftPrediction": "deleterious - low confidence"
        },
        {
          "transcript": "ENST00000616016.4",
          "source": "Ensembl",
          "bioType": "protein_coding",
          "codons": "ccT/ccC",
          "aminoAcids": "P",
          "cdnaPos": "944",
          "cdsPos": "864",
          "exons": "9/13",
          "proteinPos": "288",
          "geneId": "ENSG00000187634",
          "hgnc": "SAMD11",
          "consequence": [
            "synonymous_variant"
          ],
          "hgvsc": "ENST00000616016.4:c.864T>C",
          "hgvsp": "ENST00000616016.4:c.864T>C(p.(Pro288=))",
          "proteinId": "ENSP00000478421.1"
        },
        {
          "transcript": "ENST00000618779.4",
          "source": "Ensembl",
          "bioType": "protein_coding",
          "codons": "Tgg/Cgg",
          "aminoAcids": "W/R",
          "cdnaPos": "921",
          "cdsPos": "841",
          "exons": "9/13",
          "proteinPos": "281",
          "geneId": "ENSG00000187634",
          "hgnc": "SAMD11",
          "consequence": [
            "missense_variant"
          ],
          "hgvsc": "ENST00000618779.4:c.841T>C",
          "hgvsp": "ENSP00000484256.1:p.(Trp281Arg)",
          "polyPhenScore": 0,
          "polyPhenPrediction": "benign",
          "proteinId": "ENSP00000484256.1",
          "siftScore": 1,
          "siftPrediction": "tolerated"
        },
        {
          "transcript": "ENST00000616125.4",
          "source": "Ensembl",
          "bioType": "protein_coding",
          "codons": "Tgg/Cgg",
          "aminoAcids": "W/R",
          "cdnaPos": "783",
          "cdsPos": "703",
          "exons": "8/12",
          "proteinPos": "235",
          "geneId": "ENSG00000187634",
          "hgnc": "SAMD11",
          "consequence": [
            "missense_variant"
          ],
          "hgvsc": "ENST00000616125.4:c.703T>C",
          "hgvsp": "ENSP00000484643.1:p.(Trp235Arg)",
          "polyPhenScore": 0,
          "polyPhenPrediction": "benign",
          "proteinId": "ENSP00000484643.1",
          "siftScore": 1,
          "siftPrediction": "tolerated"
        },
        {
          "transcript": "ENST00000620200.4",
          "source": "Ensembl",
          "bioType": "protein_coding",
          "codons": "cTg/cCg",
          "aminoAcids": "L/P",
          "cdnaPos": "427",
          "cdsPos": "347",
          "exons": "5/9",
          "proteinPos": "116",
          "geneId": "ENSG00000187634",
          "hgnc": "SAMD11",
          "consequence": [
            "missense_variant"
          ],
          "hgvsc": "ENST00000620200.4:c.347T>C",
          "hgvsp": "ENSP00000484820.1:p.(Leu116Pro)",
          "polyPhenScore": 0,
          "polyPhenPrediction": "unknown",
          "proteinId": "ENSP00000484820.1",
          "siftScore": 0.16,
          "siftPrediction": "tolerated - low confidence"
        },
        {
          "transcript": "ENST00000617307.4",
          "source": "Ensembl",
          "bioType": "protein_coding",
          "codons": "Tgg/Cgg",
          "aminoAcids": "W/R",
          "cdnaPos": "867",
          "cdsPos": "787",
          "exons": "9/13",
          "proteinPos": "263",
          "geneId": "ENSG00000187634",
          "hgnc": "SAMD11",
          "consequence": [
            "missense_variant"
          ],
          "hgvsc": "ENST00000617307.4:c.787T>C",
          "hgvsp": "ENSP00000482090.1:p.(Trp263Arg)",
          "polyPhenScore": 0,
          "polyPhenPrediction": "benign",
          "proteinId": "ENSP00000482090.1",
          "siftScore": 1,
          "siftPrediction": "tolerated"
        },
        {
          "transcript": "NM_152486.2",
          "source": "RefSeq",
          "bioType": "protein_coding",
          "codons": "Cgg/Cgg",
          "aminoAcids": "R",
          "cdnaPos": "1107",
          "cdsPos": "1027",
          "exons": "10/14",
          "proteinPos": "343",
          "geneId": "148398",
          "hgnc": "SAMD11",
          "consequence": [
            "synonymous_variant"
          ],
          "hgvsc": "NM_152486.2:c.1027T>C",
          "hgvsp": "NM_152486.2:c.1027T>C(p.(Arg343=))",
          "isCanonical": true,
          "proteinId": "NP_689699.2"
        },
        {
          "transcript": "ENST00000341065.8",
          "source": "Ensembl",
          "bioType": "protein_coding",
          "codons": "Tgg/Cgg",
          "aminoAcids": "W/R",
          "cdnaPos": "750",
          "cdsPos": "751",
          "exons": "8/12",
          "proteinPos": "251",
          "geneId": "ENSG00000187634",
          "hgnc": "SAMD11",
          "consequence": [
            "missense_variant"
          ],
          "hgvsc": "ENST00000341065.8:c.750T>C",
          "hgvsp": "ENSP00000349216.4:p.(Trp251Arg)",
          "polyPhenScore": 0,
          "polyPhenPrediction": "benign",
          "proteinId": "ENSP00000349216.4",
          "siftScore": 1,
          "siftPrediction": "tolerated"
        },
        {
          "transcript": "ENST00000455979.1",
          "source": "Ensembl",
          "bioType": "protein_coding",
          "codons": "Tgg/Cgg",
          "aminoAcids": "W/R",
          "cdnaPos": "507",
          "cdsPos": "508",
          "exons": "4/7",
          "proteinPos": "170",
          "geneId": "ENSG00000187634",
          "hgnc": "SAMD11",
          "consequence": [
            "missense_variant"
          ],
          "hgvsc": "ENST00000455979.1:c.507T>C",
          "hgvsp": "ENSP00000412228.1:p.(Trp170Arg)",
          "polyPhenScore": 0,
          "polyPhenPrediction": "benign",
          "proteinId": "ENSP00000412228.1",
          "siftScore": 1,
          "siftPrediction": "tolerated"
        },
        {
          "transcript": "ENST00000478729.1",
          "source": "Ensembl",
          "bioType": "processed_transcript",
          "geneId": "ENSG00000187634",
          "hgnc": "SAMD11",
          "consequence": [
            "downstream_gene_variant"
          ]
        },
        {
          "transcript": "ENST00000474461.1",
          "source": "Ensembl",
          "bioType": "retained_intron",
          "cdnaPos": "389",
          "exons": "3/4",
          "geneId": "ENSG00000187634",
          "hgnc": "SAMD11",
          "consequence": [
            "non_coding_transcript_exon_variant"
          ],
          "hgvsc": "ENST00000474461.1:n.389T>C"
        },
        {
          "transcript": "ENST00000466827.1",
          "source": "Ensembl",
          "bioType": "retained_intron",
          "cdnaPos": "191",
          "exons": "2/2",
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        },
        {
          "transcript": "ENST00000464948.1",
          "source": "Ensembl",
          "bioType": "retained_intron",
          "cdnaPos": "286",
          "exons": "1/2",
          "geneId": "ENSG00000187634",
          "hgnc": "SAMD11",
          "consequence": [
            "non_coding_transcript_exon_variant"
          ],
          "hgvsc": "ENST00000464948.1:n.286T>C"
        },
        {
          "transcript": "NM_015658.3",
          "source": "RefSeq",
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          "consequence": [
            "downstream_gene_variant"
          ],
          "isCanonical": true,
          "proteinId": "NP_056473.2"
        },
        {
          "transcript": "ENST00000483767.5",
          "source": "Ensembl",
          "bioType": "retained_intron",
          "geneId": "ENSG00000188976",
          "hgnc": "NOC2L",
          "consequence": [
            "downstream_gene_variant"
          ]
        },
        {
          "transcript": "ENST00000327044.6",
          "source": "Ensembl",
          "bioType": "protein_coding",
          "geneId": "ENSG00000188976",
          "hgnc": "NOC2L",
          "consequence": [
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          ],
          "isCanonical": true,
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        },
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          "source": "Ensembl",
          "bioType": "retained_intron",
          "geneId": "ENSG00000188976",
          "hgnc": "NOC2L",
          "consequence": [
            "downstream_gene_variant"
          ]
        },
        {
          "transcript": "ENST00000496938.1",
          "source": "Ensembl",
          "bioType": "processed_transcript",
          "geneId": "ENSG00000188976",
          "hgnc": "NOC2L",
          "consequence": [
            "downstream_gene_variant"
          ]
        }
      ]
    }
  ]
}
]}
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Version: 3.22

Parsing Illumina Connected Annotations JSON

Parsing JSON

Our JSON files are organized similarly to original VCF variants:

Illumina Connected Annotations JSON files can get very large and sometimes we receive feedback that a bioinformatician tried to read the JSON file into Python or R resulting in a program that ran out of available RAM. This happens because those parsers try to load everything into memory all at once.

To get around those issues, we play some clever tricks with newlines that enables our users to parse our JSON files quickly and efficiently.

Organization

Our JSON file is arranged as follows:

  • the header section is located on the first line
  • each line after that corresponds to a position (same as a row in a VCF file)
    • until you reach the genes section ],"genes":[
  • each line after that corresponds to a gene
    • until you reach the end ]}

Knowing this, you can load each position line as an independent JSON object and extract the information you need.

Jupyter Notebook

To demonstrate this, we have put together a Jupyter notebook demonstrating how to do this in Python and a R version as well.

JASIX

One of the tools that we really like in the VCF ecosystem is tabix. Unfortunately, tabix only works for tab-delimited file formats. As a result, we created a similar tool for Illumina Connected Annotations JSON files called JASIX.

Here's an example of how you might use JASIX:

dotnet bin/Release/net6.0/Jasix.dll -i dragen.json.gz -q chr1:942450-942455
  • the -i argument specifies the Illumina Connected Annotations JSON path
  • the -q argument specifies a genomic range (you can use as many of these as you want)

JASIX also includes additional options for showing the Illumina Connected Annotations header or for extracting different sections (like the genes section).

The output from JASIX is compliant JSON object shown in pretty-printed form:

{"positions":[
{
  "chromosome": "chr1",
  "position": 942451,
  "refAllele": "T",
  "altAlleles": [
    "C"
  ],
  "quality": 484.23,
  "filters": [
    "PASS"
  ],
  "cytogeneticBand": "1p36.33",
  "samples": [
    {
      "genotype": "1/1",
      "variantFrequencies": [
        1
      ],
      "totalDepth": 21,
      "genotypeQuality": 60,
      "alleleDepths": [
        0,
        21
      ]
    },
    {
      "genotype": "1/1",
      "variantFrequencies": [
        1
      ],
      "totalDepth": 32,
      "genotypeQuality": 93,
      "alleleDepths": [
        0,
        32
      ]
    },
    {
      "genotype": "1/1",
      "variantFrequencies": [
        1
      ],
      "totalDepth": 36,
      "genotypeQuality": 105,
      "alleleDepths": [
        0,
        36
      ]
    }
  ],
  "variants": [
    {
      "vid": "1-942451-T-C",
      "chromosome": "chr1",
      "begin": 942451,
      "end": 942451,
      "refAllele": "T",
      "altAllele": "C",
      "variantType": "SNV",
      "hgvsg": "NC_000001.11:g.942451T>C",
      "phylopScore": -0.1,
      "clinvar": [
        {
          "id": "VCV000836156.1",
          "reviewStatus": "criteria provided, single submitter",
          "significance": [
            "uncertain significance"
          ],
          "refAllele": "T",
          "altAllele": "T",
          "lastUpdatedDate": "2020-08-20"
        },
        {
          "id": "RCV001037211.1",
          "variationId": 836156,
          "reviewStatus": "criteria provided, single submitter",
          "alleleOrigins": [
            "germline"
          ],
          "refAllele": "T",
          "altAllele": "T",
          "phenotypes": [
            "not provided"
          ],
          "medGenIds": [
            "CN517202"
          ],
          "significance": [
            "uncertain significance"
          ],
          "lastUpdatedDate": "2020-08-20",
          "pubMedIds": [
            "28492532"
          ]
        }
      ],
      "dbsnp": [
        "rs6672356"
      ],
      "gnomad": {
        "coverage": 25,
        "allAf": 0.999855,
        "allAn": 123742,
        "allAc": 123724,
        "allHc": 61853,
        "afrAf": 0.999416,
        "afrAn": 10278,
        "afrAc": 10272,
        "afrHc": 5133,
        "amrAf": 0.99995,
        "amrAn": 20008,
        "amrAc": 20007,
        "amrHc": 10003,
        "easAf": 1,
        "easAn": 6054,
        "easAc": 6054,
        "easHc": 3027,
        "finAf": 1,
        "finAn": 8696,
        "finAc": 8696,
        "finHc": 4348,
        "nfeAf": 0.999899,
        "nfeAn": 49590,
        "nfeAc": 49585,
        "nfeHc": 24790,
        "asjAf": 1,
        "asjAn": 7208,
        "asjAc": 7208,
        "asjHc": 3604,
        "sasAf": 0.99967,
        "sasAn": 18160,
        "sasAc": 18154,
        "sasHc": 9074,
        "othAf": 1,
        "othAn": 3748,
        "othAc": 3748,
        "othHc": 1874,
        "maleAf": 0.9999,
        "maleAn": 69780,
        "maleAc": 69773,
        "maleHc": 34883,
        "femaleAf": 0.999796,
        "femaleAn": 53962,
        "femaleAc": 53951,
        "femaleHc": 26970,
        "controlsAllAf": 0.999815,
        "controlsAllAn": 48654,
        "controlsAllAc": 48645
      },
      "oneKg": {
        "allAf": 1,
        "afrAf": 1,
        "amrAf": 1,
        "easAf": 1,
        "eurAf": 1,
        "sasAf": 1,
        "allAn": 5008,
        "afrAn": 1322,
        "amrAn": 694,
        "easAn": 1008,
        "eurAn": 1006,
        "sasAn": 978,
        "allAc": 5008,
        "afrAc": 1322,
        "amrAc": 694,
        "easAc": 1008,
        "eurAc": 1006,
        "sasAc": 978
      },
      "primateAI": [
        {
          "hgnc": "SAMD11",
          "scorePercentile": 0.87
        }
      ],
      "revel": {
        "score": 0.145
      },
      "topmed": {
        "allAf": 0.999809,
        "allAn": 125568,
        "allAc": 125544,
        "allHc": 62760
      },
      "transcripts": [
        {
          "transcript": "ENST00000420190.6",
          "source": "Ensembl",
          "bioType": "protein_coding",
          "geneId": "ENSG00000187634",
          "hgnc": "SAMD11",
          "consequence": [
            "downstream_gene_variant"
          ],
          "proteinId": "ENSP00000411579.2"
        },
        {
          "transcript": "ENST00000342066.7",
          "source": "Ensembl",
          "bioType": "protein_coding",
          "codons": "Tgg/Cgg",
          "aminoAcids": "W/R",
          "cdnaPos": "1110",
          "cdsPos": "1027",
          "exons": "10/14",
          "proteinPos": "343",
          "geneId": "ENSG00000187634",
          "hgnc": "SAMD11",
          "consequence": [
            "missense_variant"
          ],
          "hgvsc": "ENST00000342066.7:c.1027T>C",
          "hgvsp": "ENSP00000342313.3:p.(Trp343Arg)",
          "polyPhenScore": 0,
          "polyPhenPrediction": "benign",
          "proteinId": "ENSP00000342313.3",
          "siftScore": 1,
          "siftPrediction": "tolerated"
        },
        {
          "transcript": "ENST00000618181.4",
          "source": "Ensembl",
          "bioType": "protein_coding",
          "codons": "Tgg/Cgg",
          "aminoAcids": "W/R",
          "cdnaPos": "732",
          "cdsPos": "652",
          "exons": "7/11",
          "proteinPos": "218",
          "geneId": "ENSG00000187634",
          "hgnc": "SAMD11",
          "consequence": [
            "missense_variant"
          ],
          "hgvsc": "ENST00000618181.4:c.652T>C",
          "hgvsp": "ENSP00000480870.1:p.(Trp218Arg)",
          "polyPhenScore": 0,
          "polyPhenPrediction": "benign",
          "proteinId": "ENSP00000480870.1",
          "siftScore": 1,
          "siftPrediction": "tolerated"
        },
        {
          "transcript": "ENST00000622503.4",
          "source": "Ensembl",
          "bioType": "protein_coding",
          "codons": "Tgg/Cgg",
          "aminoAcids": "W/R",
          "cdnaPos": "1110",
          "cdsPos": "1030",
          "exons": "10/14",
          "proteinPos": "344",
          "geneId": "ENSG00000187634",
          "hgnc": "SAMD11",
          "consequence": [
            "missense_variant"
          ],
          "hgvsc": "ENST00000622503.4:c.1030T>C",
          "hgvsp": "ENSP00000482138.1:p.(Trp344Arg)",
          "isCanonical": true,
          "polyPhenScore": 0,
          "polyPhenPrediction": "benign",
          "proteinId": "ENSP00000482138.1",
          "siftScore": 1,
          "siftPrediction": "tolerated"
        },
        {
          "transcript": "ENST00000618323.4",
          "source": "Ensembl",
          "bioType": "protein_coding",
          "codons": "cTg/cCg",
          "aminoAcids": "L/P",
          "cdnaPos": "712",
          "cdsPos": "632",
          "exons": "8/12",
          "proteinPos": "211",
          "geneId": "ENSG00000187634",
          "hgnc": "SAMD11",
          "consequence": [
            "missense_variant"
          ],
          "hgvsc": "ENST00000618323.4:c.632T>C",
          "hgvsp": "ENSP00000480678.1:p.(Leu211Pro)",
          "polyPhenScore": 0,
          "polyPhenPrediction": "unknown",
          "proteinId": "ENSP00000480678.1",
          "siftScore": 0.03,
          "siftPrediction": "deleterious - low confidence"
        },
        {
          "transcript": "ENST00000616016.4",
          "source": "Ensembl",
          "bioType": "protein_coding",
          "codons": "ccT/ccC",
          "aminoAcids": "P",
          "cdnaPos": "944",
          "cdsPos": "864",
          "exons": "9/13",
          "proteinPos": "288",
          "geneId": "ENSG00000187634",
          "hgnc": "SAMD11",
          "consequence": [
            "synonymous_variant"
          ],
          "hgvsc": "ENST00000616016.4:c.864T>C",
          "hgvsp": "ENST00000616016.4:c.864T>C(p.(Pro288=))",
          "proteinId": "ENSP00000478421.1"
        },
        {
          "transcript": "ENST00000618779.4",
          "source": "Ensembl",
          "bioType": "protein_coding",
          "codons": "Tgg/Cgg",
          "aminoAcids": "W/R",
          "cdnaPos": "921",
          "cdsPos": "841",
          "exons": "9/13",
          "proteinPos": "281",
          "geneId": "ENSG00000187634",
          "hgnc": "SAMD11",
          "consequence": [
            "missense_variant"
          ],
          "hgvsc": "ENST00000618779.4:c.841T>C",
          "hgvsp": "ENSP00000484256.1:p.(Trp281Arg)",
          "polyPhenScore": 0,
          "polyPhenPrediction": "benign",
          "proteinId": "ENSP00000484256.1",
          "siftScore": 1,
          "siftPrediction": "tolerated"
        },
        {
          "transcript": "ENST00000616125.4",
          "source": "Ensembl",
          "bioType": "protein_coding",
          "codons": "Tgg/Cgg",
          "aminoAcids": "W/R",
          "cdnaPos": "783",
          "cdsPos": "703",
          "exons": "8/12",
          "proteinPos": "235",
          "geneId": "ENSG00000187634",
          "hgnc": "SAMD11",
          "consequence": [
            "missense_variant"
          ],
          "hgvsc": "ENST00000616125.4:c.703T>C",
          "hgvsp": "ENSP00000484643.1:p.(Trp235Arg)",
          "polyPhenScore": 0,
          "polyPhenPrediction": "benign",
          "proteinId": "ENSP00000484643.1",
          "siftScore": 1,
          "siftPrediction": "tolerated"
        },
        {
          "transcript": "ENST00000620200.4",
          "source": "Ensembl",
          "bioType": "protein_coding",
          "codons": "cTg/cCg",
          "aminoAcids": "L/P",
          "cdnaPos": "427",
          "cdsPos": "347",
          "exons": "5/9",
          "proteinPos": "116",
          "geneId": "ENSG00000187634",
          "hgnc": "SAMD11",
          "consequence": [
            "missense_variant"
          ],
          "hgvsc": "ENST00000620200.4:c.347T>C",
          "hgvsp": "ENSP00000484820.1:p.(Leu116Pro)",
          "polyPhenScore": 0,
          "polyPhenPrediction": "unknown",
          "proteinId": "ENSP00000484820.1",
          "siftScore": 0.16,
          "siftPrediction": "tolerated - low confidence"
        },
        {
          "transcript": "ENST00000617307.4",
          "source": "Ensembl",
          "bioType": "protein_coding",
          "codons": "Tgg/Cgg",
          "aminoAcids": "W/R",
          "cdnaPos": "867",
          "cdsPos": "787",
          "exons": "9/13",
          "proteinPos": "263",
          "geneId": "ENSG00000187634",
          "hgnc": "SAMD11",
          "consequence": [
            "missense_variant"
          ],
          "hgvsc": "ENST00000617307.4:c.787T>C",
          "hgvsp": "ENSP00000482090.1:p.(Trp263Arg)",
          "polyPhenScore": 0,
          "polyPhenPrediction": "benign",
          "proteinId": "ENSP00000482090.1",
          "siftScore": 1,
          "siftPrediction": "tolerated"
        },
        {
          "transcript": "NM_152486.2",
          "source": "RefSeq",
          "bioType": "protein_coding",
          "codons": "Cgg/Cgg",
          "aminoAcids": "R",
          "cdnaPos": "1107",
          "cdsPos": "1027",
          "exons": "10/14",
          "proteinPos": "343",
          "geneId": "148398",
          "hgnc": "SAMD11",
          "consequence": [
            "synonymous_variant"
          ],
          "hgvsc": "NM_152486.2:c.1027T>C",
          "hgvsp": "NM_152486.2:c.1027T>C(p.(Arg343=))",
          "isCanonical": true,
          "proteinId": "NP_689699.2"
        },
        {
          "transcript": "ENST00000341065.8",
          "source": "Ensembl",
          "bioType": "protein_coding",
          "codons": "Tgg/Cgg",
          "aminoAcids": "W/R",
          "cdnaPos": "750",
          "cdsPos": "751",
          "exons": "8/12",
          "proteinPos": "251",
          "geneId": "ENSG00000187634",
          "hgnc": "SAMD11",
          "consequence": [
            "missense_variant"
          ],
          "hgvsc": "ENST00000341065.8:c.750T>C",
          "hgvsp": "ENSP00000349216.4:p.(Trp251Arg)",
          "polyPhenScore": 0,
          "polyPhenPrediction": "benign",
          "proteinId": "ENSP00000349216.4",
          "siftScore": 1,
          "siftPrediction": "tolerated"
        },
        {
          "transcript": "ENST00000455979.1",
          "source": "Ensembl",
          "bioType": "protein_coding",
          "codons": "Tgg/Cgg",
          "aminoAcids": "W/R",
          "cdnaPos": "507",
          "cdsPos": "508",
          "exons": "4/7",
          "proteinPos": "170",
          "geneId": "ENSG00000187634",
          "hgnc": "SAMD11",
          "consequence": [
            "missense_variant"
          ],
          "hgvsc": "ENST00000455979.1:c.507T>C",
          "hgvsp": "ENSP00000412228.1:p.(Trp170Arg)",
          "polyPhenScore": 0,
          "polyPhenPrediction": "benign",
          "proteinId": "ENSP00000412228.1",
          "siftScore": 1,
          "siftPrediction": "tolerated"
        },
        {
          "transcript": "ENST00000478729.1",
          "source": "Ensembl",
          "bioType": "processed_transcript",
          "geneId": "ENSG00000187634",
          "hgnc": "SAMD11",
          "consequence": [
            "downstream_gene_variant"
          ]
        },
        {
          "transcript": "ENST00000474461.1",
          "source": "Ensembl",
          "bioType": "retained_intron",
          "cdnaPos": "389",
          "exons": "3/4",
          "geneId": "ENSG00000187634",
          "hgnc": "SAMD11",
          "consequence": [
            "non_coding_transcript_exon_variant"
          ],
          "hgvsc": "ENST00000474461.1:n.389T>C"
        },
        {
          "transcript": "ENST00000466827.1",
          "source": "Ensembl",
          "bioType": "retained_intron",
          "cdnaPos": "191",
          "exons": "2/2",
          "geneId": "ENSG00000187634",
          "hgnc": "SAMD11",
          "consequence": [
            "non_coding_transcript_exon_variant"
          ],
          "hgvsc": "ENST00000466827.1:n.191T>C"
        },
        {
          "transcript": "ENST00000464948.1",
          "source": "Ensembl",
          "bioType": "retained_intron",
          "cdnaPos": "286",
          "exons": "1/2",
          "geneId": "ENSG00000187634",
          "hgnc": "SAMD11",
          "consequence": [
            "non_coding_transcript_exon_variant"
          ],
          "hgvsc": "ENST00000464948.1:n.286T>C"
        },
        {
          "transcript": "NM_015658.3",
          "source": "RefSeq",
          "bioType": "protein_coding",
          "geneId": "26155",
          "hgnc": "NOC2L",
          "consequence": [
            "downstream_gene_variant"
          ],
          "isCanonical": true,
          "proteinId": "NP_056473.2"
        },
        {
          "transcript": "ENST00000483767.5",
          "source": "Ensembl",
          "bioType": "retained_intron",
          "geneId": "ENSG00000188976",
          "hgnc": "NOC2L",
          "consequence": [
            "downstream_gene_variant"
          ]
        },
        {
          "transcript": "ENST00000327044.6",
          "source": "Ensembl",
          "bioType": "protein_coding",
          "geneId": "ENSG00000188976",
          "hgnc": "NOC2L",
          "consequence": [
            "downstream_gene_variant"
          ],
          "isCanonical": true,
          "proteinId": "ENSP00000317992.6"
        },
        {
          "transcript": "ENST00000477976.5",
          "source": "Ensembl",
          "bioType": "retained_intron",
          "geneId": "ENSG00000188976",
          "hgnc": "NOC2L",
          "consequence": [
            "downstream_gene_variant"
          ]
        },
        {
          "transcript": "ENST00000496938.1",
          "source": "Ensembl",
          "bioType": "processed_transcript",
          "geneId": "ENSG00000188976",
          "hgnc": "NOC2L",
          "consequence": [
            "downstream_gene_variant"
          ]
        }
      ]
    }
  ]
}
]}
+ + \ No newline at end of file diff --git a/3.22/utilities/jasix/index.html b/3.22/utilities/jasix/index.html index 2ed44ecb..38996a8a 100644 --- a/3.22/utilities/jasix/index.html +++ b/3.22/utilities/jasix/index.html @@ -6,13 +6,13 @@ Jasix | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

Jasix

Overview

The Jasix index is aimed at providing TABIX like indexing capabilities for the Illumina Connected Annotations JSON output.

Creating the Jasix index

The Jasix index (that comes in a .jsi) file is generated on-the-fly with Illumina Connected Annotations output. It can also be generated independently by running the Jasix command line utility on the JSON output file. Please note that the Jasix utility can only consume JSON files that follow the Illumina Connected Annotations JSON output format. The following code blocks demonstrate the help menu and index generating functionalities of Jasix.

Example

dotnet Jasix.dll -h
USAGE: dotnet Jasix.dll -i in.json.gz [options]
Indexes a Illumina Connected Annotations annotated JSON file

OPTIONS:
      --header, -t           print also the header lines
      --only-header, -H      print only the header lines
      --chromosomes, -l      list chromosome names
      --index, -c            create index
      --in, -i <VALUE>       input
      --out, -o <VALUE>      compressed output file name (default:console)
      --query, -q <VALUE>    query range
      --section, -s <VALUE>  complete section (positions or genes) to output
      --help, -h             displays the help menu
      --version, -v          displays the version
dotnet Jasix.dll --index -i input.json.gz
---------------------------------------------------------------------------
Jasix                                               (c) 2023 Illumina, Inc.
                                                                     3.22.0
---------------------------------------------------------------------------

Ref Sequence chrM indexed in 00:00:00.2
Ref Sequence chr1 indexed in 00:00:05.8
Ref Sequence chr2 indexed in 00:00:06.0
.
.
.
Peak memory usage: 28.5 MB
Time: 00:01:14.8

Querying the index

The Jasix query format is chr:start-end. If not provided, it assumes end=start. If only chr is provided, all entries for that chromosome will be provided.

dotnet Jasix.dll -i input.json.gz chrM:5000-7000
{
   "positions":[
      {
         "chromosome":"chrM",
         "refAllele":"C",
         "position":5581,
         "quality":3070.00,
         "filters":[
            "LowGQXHomSNP"
         ],
         "altAlleles":[
            "T"
         ],
         "samples":[
            {
               "variantFreq":1,
               "totalDepth":1625,
               "genotypeQuality":1,
               "alleleDepths":[
                  0,
                  1625
               ],
               "genotype":"1/1"
            }
         ],
         "variants":[
            {
               "altAllele":"T",
               "refAllele":"C",
               "begin":5581,
               "chromosome":"chrM",
               "end":5581,
               "variantType":"SNV",
               "vid":"MT:5581:T"
            }
         ]
      },
      {
         "chromosome":"chrM",
         "refAllele":"A",
         "position":6267,
         "quality":1637.00,
         "filters":[
            "LowGQXHetSNP"
         ],
         "altAlleles":[
            "G"
         ],
         "samples":[
            {
               "variantFreq":0.6873,
               "totalDepth":323,
               "genotypeQuality":1,
               "alleleDepths":[
                  101,
                  222
               ],
               "genotype":"0/1"
            }
         ],
         "variants":[
            {
               "altAllele":"G",
               "refAllele":"A",
               "begin":6267,
               "chromosome":"chrM",
               "end":6267,
               "variantType":"SNV",
               "vid":"MT:6267:G"
            }
         ]
      }
   ]
}

The default output stream is Console. However, if an output filename is provided, Jasix outputs the results to that file in a bgzip compressed format. The output is always a valid JSON entry. If requested (via -t option) the header of the indexed file will be provided. Multiple queries can be submitted in the same command and the output will contain them within the same "positions" block in order of the submitted queries (Warning: if the queries are out of order, or overlapping, the output will be out or order and intersecting).

dotnet Jasix.dll -i input.json.gz  -q chrM:5000-7000 -q chrM:8500-9500 -t
{
   "header":{
      "annotator":"Illumina Annotation Engine 1.6.2.0",
      "creationTime":"2017-08-30 11:42:57",
      "genomeAssembly":"GRCh37",
      "schemaVersion":6,
      "dataVersion":"84.24.39",
      "dataSources":[
         {
            "name":"VEP",
            "version":"84",
            "description":"Ensembl",
            "releaseDate":"2017-01-16"
         }
      ],
      "samples":[
         "Mother"
      ]
   },
   "positions":[
      {
         "chromosome":"chrM",
         "refAllele":"C",
         "position":5581,
         "quality":3070.00,
         "filters":[
            "LowGQXHomSNP"
         ],
         "altAlleles":[
            "T"
         ],
         "samples":[
            {
               "variantFreq":1,
               "totalDepth":1625,
               "genotypeQuality":1,
               "alleleDepths":[
                  0,
                  1625
               ],
               "genotype":"1/1"
            }
         ],
         "variants":[
            {
               "altAllele":"T",
               "refAllele":"C",
               "begin":5581,
               "chromosome":"chrM",
               "end":5581,
               "variantType":"SNV",
               "vid":"MT:5581:T"
            }
         ]
      },
      {
         "chromosome":"chrM",
         "refAllele":"A",
         "position":6267,
         "quality":1637.00,
         "filters":[
            "LowGQXHetSNP"
         ],
         "altAlleles":[
            "G"
         ],
         "samples":[
            {
               "variantFreq":0.6873,
               "totalDepth":323,
               "genotypeQuality":1,
               "alleleDepths":[
                  101,
                  222
               ],
               "genotype":"0/1"
            }
         ],
         "variants":[
            {
               "altAllele":"G",
               "refAllele":"A",
               "begin":6267,
               "chromosome":"chrM",
               "end":6267,
               "variantType":"SNV",
               "vid":"MT:6267:G"
            }
         ]
      },
      {
         "chromosome":"chrM",
         "refAllele":"G",
         "position":8702,
         "quality":3070.00,
         "filters":[
            "LowGQXHomSNP"
         ],
         "altAlleles":[
            "A"
         ],
         "samples":[
            {
               "variantFreq":0.9987,
               "totalDepth":1534,
               "genotypeQuality":1,
               "alleleDepths":[
                  2,
                  1532
               ],
               "genotype":"1/1"
            }
         ],
         "variants":[
            {
               "altAllele":"A",
               "refAllele":"G",
               "begin":8702,
               "chromosome":"chrM",
               "end":8702,
               "variantType":"SNV",
               "vid":"MT:8702:A"
            }
         ]
      },
      {
         "chromosome":"chrM",
         "refAllele":"G",
         "position":9378,
         "quality":3070.00,
         "filters":[
            "LowGQXHomSNP"
         ],
         "altAlleles":[
            "A"
         ],
         "samples":[
            {
               "variantFreq":1,
               "totalDepth":1018,
               "genotypeQuality":1,
               "alleleDepths":[
                  0,
                  1018
               ],
               "genotype":"1/1"
            }
         ],
         "variants":[
            {
               "altAllele":"A",
               "refAllele":"G",
               "begin":9378,
               "chromosome":"chrM",
               "end":9378,
               "variantType":"SNV",
               "vid":"MT:9378:A"
            }
         ]
      }
   ]
}

Extracting a section

The Illumina Connected Annotations JSON file has three sections: header, positions and genes. Header can be printed using the -H option. If you are interested in only the positions or genes section, you can use the -s or --section option.

dotnet Jasix.dll -i input.json.gz  -s genes
[
{
  "name": "ABCB10",
  "omim": [
    {
      "mimNumber": 605454,
      "geneName": "ATP-binding cassette, subfamily B, member 10"
    }
  ]
},
{
  "name": "ABCD3",
  "omim": [
    {
      "mimNumber": 170995,
      "geneName": "ATP-binding cassette, subfamily D, member 3 (peroxisomal membrane protein 1, 70kD)",
      "description": "The ABCD3 gene encodes a peroxisomal membrane transporter involved in the transport of branched-chain fatty acids and C27 bile acids into the peroxisome; the latter function is a crucial step in bile acid biosynthesis (summary by Ferdinandusse et al., 2015).",
      "phenotypes": [
        {
          "mimNumber": 616278,
          "phenotype": "?Bile acid synthesis defect, congenital, 5",
          "mapping": "molecular basis of the disorder is known",
          "inheritances": [
            "Autosomal recessive"
          ],
          "comments": [
            "unconfirmed or possibly spurious mapping"
          ]
        }
      ]
    }
  ]
}
]
- - +
Skip to main content
Version: 3.22

Jasix

Overview

The Jasix index is aimed at providing TABIX like indexing capabilities for the Illumina Connected Annotations JSON output.

Creating the Jasix index

The Jasix index (that comes in a .jsi) file is generated on-the-fly with Illumina Connected Annotations output. It can also be generated independently by running the Jasix command line utility on the JSON output file. Please note that the Jasix utility can only consume JSON files that follow the Illumina Connected Annotations JSON output format. The following code blocks demonstrate the help menu and index generating functionalities of Jasix.

Example

dotnet Jasix.dll -h
USAGE: dotnet Jasix.dll -i in.json.gz [options]
Indexes a Illumina Connected Annotations annotated JSON file

OPTIONS:
      --header, -t           print also the header lines
      --only-header, -H      print only the header lines
      --chromosomes, -l      list chromosome names
      --index, -c            create index
      --in, -i <VALUE>       input
      --out, -o <VALUE>      compressed output file name (default:console)
      --query, -q <VALUE>    query range
      --section, -s <VALUE>  complete section (positions or genes) to output
      --help, -h             displays the help menu
      --version, -v          displays the version
dotnet Jasix.dll --index -i input.json.gz
---------------------------------------------------------------------------
Jasix                                               (c) 2023 Illumina, Inc.
                                                                     3.22.0
---------------------------------------------------------------------------

Ref Sequence chrM indexed in 00:00:00.2
Ref Sequence chr1 indexed in 00:00:05.8
Ref Sequence chr2 indexed in 00:00:06.0
.
.
.
Peak memory usage: 28.5 MB
Time: 00:01:14.8

Querying the index

The Jasix query format is chr:start-end. If not provided, it assumes end=start. If only chr is provided, all entries for that chromosome will be provided.

dotnet Jasix.dll -i input.json.gz chrM:5000-7000
{
   "positions":[
      {
         "chromosome":"chrM",
         "refAllele":"C",
         "position":5581,
         "quality":3070.00,
         "filters":[
            "LowGQXHomSNP"
         ],
         "altAlleles":[
            "T"
         ],
         "samples":[
            {
               "variantFreq":1,
               "totalDepth":1625,
               "genotypeQuality":1,
               "alleleDepths":[
                  0,
                  1625
               ],
               "genotype":"1/1"
            }
         ],
         "variants":[
            {
               "altAllele":"T",
               "refAllele":"C",
               "begin":5581,
               "chromosome":"chrM",
               "end":5581,
               "variantType":"SNV",
               "vid":"MT:5581:T"
            }
         ]
      },
      {
         "chromosome":"chrM",
         "refAllele":"A",
         "position":6267,
         "quality":1637.00,
         "filters":[
            "LowGQXHetSNP"
         ],
         "altAlleles":[
            "G"
         ],
         "samples":[
            {
               "variantFreq":0.6873,
               "totalDepth":323,
               "genotypeQuality":1,
               "alleleDepths":[
                  101,
                  222
               ],
               "genotype":"0/1"
            }
         ],
         "variants":[
            {
               "altAllele":"G",
               "refAllele":"A",
               "begin":6267,
               "chromosome":"chrM",
               "end":6267,
               "variantType":"SNV",
               "vid":"MT:6267:G"
            }
         ]
      }
   ]
}

The default output stream is Console. However, if an output filename is provided, Jasix outputs the results to that file in a bgzip compressed format. The output is always a valid JSON entry. If requested (via -t option) the header of the indexed file will be provided. Multiple queries can be submitted in the same command and the output will contain them within the same "positions" block in order of the submitted queries (Warning: if the queries are out of order, or overlapping, the output will be out or order and intersecting).

dotnet Jasix.dll -i input.json.gz  -q chrM:5000-7000 -q chrM:8500-9500 -t
{
   "header":{
      "annotator":"Illumina Annotation Engine 1.6.2.0",
      "creationTime":"2017-08-30 11:42:57",
      "genomeAssembly":"GRCh37",
      "schemaVersion":6,
      "dataVersion":"84.24.39",
      "dataSources":[
         {
            "name":"VEP",
            "version":"84",
            "description":"Ensembl",
            "releaseDate":"2017-01-16"
         }
      ],
      "samples":[
         "Mother"
      ]
   },
   "positions":[
      {
         "chromosome":"chrM",
         "refAllele":"C",
         "position":5581,
         "quality":3070.00,
         "filters":[
            "LowGQXHomSNP"
         ],
         "altAlleles":[
            "T"
         ],
         "samples":[
            {
               "variantFreq":1,
               "totalDepth":1625,
               "genotypeQuality":1,
               "alleleDepths":[
                  0,
                  1625
               ],
               "genotype":"1/1"
            }
         ],
         "variants":[
            {
               "altAllele":"T",
               "refAllele":"C",
               "begin":5581,
               "chromosome":"chrM",
               "end":5581,
               "variantType":"SNV",
               "vid":"MT:5581:T"
            }
         ]
      },
      {
         "chromosome":"chrM",
         "refAllele":"A",
         "position":6267,
         "quality":1637.00,
         "filters":[
            "LowGQXHetSNP"
         ],
         "altAlleles":[
            "G"
         ],
         "samples":[
            {
               "variantFreq":0.6873,
               "totalDepth":323,
               "genotypeQuality":1,
               "alleleDepths":[
                  101,
                  222
               ],
               "genotype":"0/1"
            }
         ],
         "variants":[
            {
               "altAllele":"G",
               "refAllele":"A",
               "begin":6267,
               "chromosome":"chrM",
               "end":6267,
               "variantType":"SNV",
               "vid":"MT:6267:G"
            }
         ]
      },
      {
         "chromosome":"chrM",
         "refAllele":"G",
         "position":8702,
         "quality":3070.00,
         "filters":[
            "LowGQXHomSNP"
         ],
         "altAlleles":[
            "A"
         ],
         "samples":[
            {
               "variantFreq":0.9987,
               "totalDepth":1534,
               "genotypeQuality":1,
               "alleleDepths":[
                  2,
                  1532
               ],
               "genotype":"1/1"
            }
         ],
         "variants":[
            {
               "altAllele":"A",
               "refAllele":"G",
               "begin":8702,
               "chromosome":"chrM",
               "end":8702,
               "variantType":"SNV",
               "vid":"MT:8702:A"
            }
         ]
      },
      {
         "chromosome":"chrM",
         "refAllele":"G",
         "position":9378,
         "quality":3070.00,
         "filters":[
            "LowGQXHomSNP"
         ],
         "altAlleles":[
            "A"
         ],
         "samples":[
            {
               "variantFreq":1,
               "totalDepth":1018,
               "genotypeQuality":1,
               "alleleDepths":[
                  0,
                  1018
               ],
               "genotype":"1/1"
            }
         ],
         "variants":[
            {
               "altAllele":"A",
               "refAllele":"G",
               "begin":9378,
               "chromosome":"chrM",
               "end":9378,
               "variantType":"SNV",
               "vid":"MT:9378:A"
            }
         ]
      }
   ]
}

Extracting a section

The Illumina Connected Annotations JSON file has three sections: header, positions and genes. Header can be printed using the -H option. If you are interested in only the positions or genes section, you can use the -s or --section option.

dotnet Jasix.dll -i input.json.gz  -s genes
[
{
  "name": "ABCB10",
  "omim": [
    {
      "mimNumber": 605454,
      "geneName": "ATP-binding cassette, subfamily B, member 10"
    }
  ]
},
{
  "name": "ABCD3",
  "omim": [
    {
      "mimNumber": 170995,
      "geneName": "ATP-binding cassette, subfamily D, member 3 (peroxisomal membrane protein 1, 70kD)",
      "description": "The ABCD3 gene encodes a peroxisomal membrane transporter involved in the transport of branched-chain fatty acids and C27 bile acids into the peroxisome; the latter function is a crucial step in bile acid biosynthesis (summary by Ferdinandusse et al., 2015).",
      "phenotypes": [
        {
          "mimNumber": 616278,
          "phenotype": "?Bile acid synthesis defect, congenital, 5",
          "mapping": "molecular basis of the disorder is known",
          "inheritances": [
            "Autosomal recessive"
          ],
          "comments": [
            "unconfirmed or possibly spurious mapping"
          ]
        }
      ]
    }
  ]
}
]
+ + \ No newline at end of file diff --git a/3.22/utilities/sautils/index.html b/3.22/utilities/sautils/index.html index e6dbf8a4..ebad3f9a 100644 --- a/3.22/utilities/sautils/index.html +++ b/3.22/utilities/sautils/index.html @@ -6,13 +6,13 @@ SAUtils | IlluminaConnectedAnnotations - - + +
-
Skip to main content
Version: 3.22

SAUtils

Overview

SAUtils is a utility tool that creates binary supplementary annotation files (.nsa, .gsa, .npd, .nsi, etc.) from original data files (e.g. VCFs, TSVs, XML, HTML, etc.) for various data sources (e.g. ClinVar, dbSNP, gnomAD, etc.). These binary files can be fed into the Illumina Connected Annotations Annotation engine to provide supplementary annotations in the output.

The SAUtils Menu

SAUtils supports building binary files for many data sources. The help menu lists them out in the form of sub-commands.

dotnet SAUtils.dll
---------------------------------------------------------------------------
SAUtils                                             (c) 2023 Illumina, Inc.
                                                                     3.22.0
---------------------------------------------------------------------------

Utilities focused on supplementary annotation

USAGE: dotnet SAUtils.dll <command> [options]

COMMAND: AutoDownloadGenerate   auto download and generate Omim, Clinvar, Clingen
         AaCon                  create AA conservation database
         ancestralAllele        create Ancestral allele database from 1000Genomes data
         ClinGen                create ClinGen database
         Downloader             download ClinGen database
         clinvar                create ClinVar database
         concat                 merge multiple NSA files for the same data source having non-overlapping regions
         Cosmic                 create COSMIC database
         CosmicSv               create COSMIC SV database
         CosmicFusion           create COSMIC gene fusion database
         CosmicCGC              create COSMIC cancer gene census database
         CustomGene             create custom gene annotation database
         CustomVar              create custom variant annotation database
         Dann                   create DANN database
         Dbsnp                  create dbSNP database
         Dgv                    create DGV database
         DiseaseValidity        create disease validity database
         DosageMapRegions       create dosage map regions
         DosageSensitivity      create dosage sensitivity database
         DownloadOmim           download OMIM database
         ExtractMiniSA          extracts mini SA
         ExtractMiniXml         extracts mini XML (ClinVar)
         FilterSpliceNetTsv     filter SpliceNet predictions
         FusionCatcher          create FusionCatcher database
         Gerp                   create GERP conservation database
         GlobalMinor            create global minor allele database
         Gnomad                 create gnomAD database
         Gnomad-lcr             create gnomAD low complexity region database
         GnomadGeneScores       create gnomAD gene scores database
         GnomadSV               create gnomAD structural variant database
         Index                  edit an index file
         MitoHet                create mitochondrial Heteroplasmy database
         MitomapSvDb            create MITOMAP structural variants database
         MitomapVarDb           create MITOMAP small variants database
         Omim                   create OMIM database
         OneKGen                create 1000 Genome small variants database
         OneKGenSv              create 1000 Genomes structural variants database
         OneKGenSvVcfToBed      convert 1000 Genomes structural variants VCF file into a BED-like file
         PhyloP                 create PhyloP database
         PrimateAi              create PrimateAI database
         RefMinor               create Reference Minor database from 1000 Genome 
         RemapWithDbsnp         remap a VCF file given source and destination rsID mappings
         Revel                  create REVEL database
         SpliceAi               create SpliceAI database
         TopMed                 create TOPMed database
         Gme                    create GME Variome database
         Decipher               create Decipher database

You can get further detailed help for each sub-command by typing in the subcommand. For example:

dotnet SAUtils.dll clinvar
---------------------------------------------------------------------------
SAUtils                                             (c) 2023 Illumina, Inc.
                                                                     3.22.0
---------------------------------------------------------------------------

USAGE: dotnet SAUtils.dll clinvar [options]
Creates a supplementary database with ClinVar annotations

OPTIONS:
      --ref, -r <VALUE>      compressed reference sequence file
      --rcv, -i <VALUE>      ClinVar Full release XML file
      --vcv, -c <VALUE>      ClinVar Variation release XML file
      --out, -o <VALUE>      output directory
      --help, -h             displays the help menu
      --version, -v          displays the version

More detailed instructions about each sub-command can be found in documentation of respective data sources.

Output File Formats

The format of the binary file SAUtils produce depend on the type of annotation data represented in that file (e.g. small variant vs. structural variants vs. genes).

File ExtensionDescription
.nsaSmall variant annotations (e.g. SNV, insertions, deletions, etc.)
.gsaCompact variant annotations (e.g. SNV, insertions, deletions, etc.)
.idxIndex file
.nsiInterval annotations (e.g. SV, CNVs, intervals)
.ngaGene annotations
.npdConservation scores
.rmaReference Minor allele
.gfsGene fusions source
.gfjGene fusions JSON
.schemaJSON schema
- - +
Skip to main content
Version: 3.22

SAUtils

Overview

SAUtils is a utility tool that creates binary supplementary annotation files (.nsa, .gsa, .npd, .nsi, etc.) from original data files (e.g. VCFs, TSVs, XML, HTML, etc.) for various data sources (e.g. ClinVar, dbSNP, gnomAD, etc.). These binary files can be fed into the Illumina Connected Annotations Annotation engine to provide supplementary annotations in the output.

The SAUtils Menu

SAUtils supports building binary files for many data sources. The help menu lists them out in the form of sub-commands.

dotnet SAUtils.dll
---------------------------------------------------------------------------
SAUtils                                             (c) 2023 Illumina, Inc.
                                                                     3.22.0
---------------------------------------------------------------------------

Utilities focused on supplementary annotation

USAGE: dotnet SAUtils.dll <command> [options]

COMMAND: AutoDownloadGenerate   auto download and generate Omim, Clinvar, Clingen
         AaCon                  create AA conservation database
         ancestralAllele        create Ancestral allele database from 1000Genomes data
         ClinGen                create ClinGen database
         Downloader             download ClinGen database
         clinvar                create ClinVar database
         concat                 merge multiple NSA files for the same data source having non-overlapping regions
         Cosmic                 create COSMIC database
         CosmicSv               create COSMIC SV database
         CosmicFusion           create COSMIC gene fusion database
         CosmicCGC              create COSMIC cancer gene census database
         CustomGene             create custom gene annotation database
         CustomVar              create custom variant annotation database
         Dann                   create DANN database
         Dbsnp                  create dbSNP database
         Dgv                    create DGV database
         DiseaseValidity        create disease validity database
         DosageMapRegions       create dosage map regions
         DosageSensitivity      create dosage sensitivity database
         DownloadOmim           download OMIM database
         ExtractMiniSA          extracts mini SA
         ExtractMiniXml         extracts mini XML (ClinVar)
         FilterSpliceNetTsv     filter SpliceNet predictions
         FusionCatcher          create FusionCatcher database
         Gerp                   create GERP conservation database
         GlobalMinor            create global minor allele database
         Gnomad                 create gnomAD database
         Gnomad-lcr             create gnomAD low complexity region database
         GnomadGeneScores       create gnomAD gene scores database
         GnomadSV               create gnomAD structural variant database
         Index                  edit an index file
         MitoHet                create mitochondrial Heteroplasmy database
         MitomapSvDb            create MITOMAP structural variants database
         MitomapVarDb           create MITOMAP small variants database
         Omim                   create OMIM database
         OneKGen                create 1000 Genome small variants database
         OneKGenSv              create 1000 Genomes structural variants database
         OneKGenSvVcfToBed      convert 1000 Genomes structural variants VCF file into a BED-like file
         PhyloP                 create PhyloP database
         PrimateAi              create PrimateAI database
         RefMinor               create Reference Minor database from 1000 Genome 
         RemapWithDbsnp         remap a VCF file given source and destination rsID mappings
         Revel                  create REVEL database
         SpliceAi               create SpliceAI database
         TopMed                 create TOPMed database
         Gme                    create GME Variome database
         Decipher               create Decipher database

You can get further detailed help for each sub-command by typing in the subcommand. For example:

dotnet SAUtils.dll clinvar
---------------------------------------------------------------------------
SAUtils                                             (c) 2023 Illumina, Inc.
                                                                     3.22.0
---------------------------------------------------------------------------

USAGE: dotnet SAUtils.dll clinvar [options]
Creates a supplementary database with ClinVar annotations

OPTIONS:
      --ref, -r <VALUE>      compressed reference sequence file
      --rcv, -i <VALUE>      ClinVar Full release XML file
      --vcv, -c <VALUE>      ClinVar Variation release XML file
      --out, -o <VALUE>      output directory
      --help, -h             displays the help menu
      --version, -v          displays the version

More detailed instructions about each sub-command can be found in documentation of respective data sources.

Output File Formats

The format of the binary file SAUtils produce depend on the type of annotation data represented in that file (e.g. small variant vs. structural variants vs. genes).

File ExtensionDescription
.nsaSmall variant annotations (e.g. SNV, insertions, deletions, etc.)
.gsaCompact variant annotations (e.g. SNV, insertions, deletions, etc.)
.idxIndex file
.nsiInterval annotations (e.g. SV, CNVs, intervals)
.ngaGene annotations
.npdConservation scores
.rmaReference Minor allele
.gfsGene fusions source
.gfjGene fusions JSON
.schemaJSON schema
+ + \ No newline at end of file diff --git a/404.html b/404.html index 1e5f0e25..7c1ab694 100644 --- a/404.html +++ b/404.html @@ -6,13 +6,13 @@ Page Not Found | IlluminaConnectedAnnotations - - + +
-
Skip to main content

Page Not Found

We could not find what you were looking for.

Please contact the owner of the site that linked you to the original URL and let them know their link is broken.

- - +
Skip to main content

Page Not Found

We could not find what you were looking for.

Please contact the owner of the site that linked you to the original URL and let them know their link is broken.

+ + \ No newline at end of file diff --git a/assets/js/f048ed9e.28bc210a.js b/assets/js/f048ed9e.28bc210a.js deleted file mode 100644 index 71b5bb3c..00000000 --- a/assets/js/f048ed9e.28bc210a.js +++ /dev/null @@ -1 +0,0 @@ -"use strict";(self.webpackChunknirvana_documentation=self.webpackChunknirvana_documentation||[]).push([[9962],{3905:(e,n,t)=>{t.d(n,{Zo:()=>d,kt:()=>h});var a=t(7294);function i(e,n,t){return n in e?Object.defineProperty(e,n,{value:t,enumerable:!0,configurable:!0,writable:!0}):e[n]=t,e}function o(e,n){var t=Object.keys(e);if(Object.getOwnPropertySymbols){var a=Object.getOwnPropertySymbols(e);n&&(a=a.filter((function(n){return Object.getOwnPropertyDescriptor(e,n).enumerable}))),t.push.apply(t,a)}return t}function l(e){for(var n=1;n=0||(i[t]=e[t]);return i}(e,n);if(Object.getOwnPropertySymbols){var o=Object.getOwnPropertySymbols(e);for(a=0;a=0||Object.prototype.propertyIsEnumerable.call(e,t)&&(i[t]=e[t])}return i}var s=a.createContext({}),c=function(e){var n=a.useContext(s),t=n;return e&&(t="function"==typeof e?e(n):l(l({},n),e)),t},d=function(e){var n=c(e.components);return a.createElement(s.Provider,{value:n},e.children)},m="mdxType",u={inlineCode:"code",wrapper:function(e){var n=e.children;return a.createElement(a.Fragment,{},n)}},p=a.forwardRef((function(e,n){var t=e.components,i=e.mdxType,o=e.originalType,s=e.parentName,d=r(e,["components","mdxType","originalType","parentName"]),m=c(t),p=i,h=m["".concat(s,".").concat(p)]||m[p]||u[p]||o;return t?a.createElement(h,l(l({ref:n},d),{},{components:t})):a.createElement(h,l({ref:n},d))}));function h(e,n){var t=arguments,i=n&&n.mdxType;if("string"==typeof e||i){var o=t.length,l=new Array(o);l[0]=p;var r={};for(var s in n)hasOwnProperty.call(n,s)&&(r[s]=n[s]);r.originalType=e,r[m]="string"==typeof e?e:i,l[1]=r;for(var c=2;c{t.r(n),t.d(n,{contentTitle:()=>l,default:()=>m,frontMatter:()=>o,metadata:()=>r,toc:()=>s});var a=t(7462),i=(t(7294),t(3905));const o={title:"Getting Started"},l=void 0,r={unversionedId:"introduction/getting-started",id:"introduction/getting-started",title:"Getting Started",description:"Illumina Connected Annotations is written in C# using .NET Core (an amazing runtime environment that currently runs on Windows, Linux, Mac OS X, and in Docker images). Once .NET Core has been downloaded, all you need to do is grab the source, compile it, and grab the data files.",source:"@site/docs/introduction/getting-started.md",sourceDirName:"introduction",slug:"/introduction/getting-started",permalink:"/IlluminaConnectedAnnotationsDocumentation/introduction/getting-started",editUrl:"https://github.com/Illumina/IlluminaConnectedAnnotationsDocumentation/edit/master/docs/introduction/getting-started.md",tags:[],version:"current",frontMatter:{title:"Getting Started"},sidebar:"docs",previous:{title:"Dependencies",permalink:"/IlluminaConnectedAnnotationsDocumentation/introduction/dependencies"},next:{title:"Parsing Illumina Connected Annotations JSON",permalink:"/IlluminaConnectedAnnotationsDocumentation/introduction/parsing-json"}},s=[{value:"Getting Illumina Connected Annotations",id:"getting-illumina-connected-annotations",children:[{value:"Latest Release",id:"latest-release",children:[],level:3},{value:"Quick Start",id:"quick-start",children:[],level:3},{value:"Docker",id:"docker",children:[],level:3}],level:2},{value:"Downloading the data files",id:"downloading-the-data-files",children:[],level:2},{value:"Download a test VCF file",id:"download-a-test-vcf-file",children:[],level:2},{value:"Running Illumina Connected Annotations",id:"running-illumina-connected-annotations",children:[],level:2},{value:"The Illumina Connected Annotations command line",id:"the-illumina-connected-annotations-command-line",children:[{value:"Specifying annotation sources",id:"specifying-annotation-sources",children:[],level:3}],level:2}],c={toc:s},d="wrapper";function m(e){let{components:n,...o}=e;return(0,i.kt)(d,(0,a.Z)({},c,o,{components:n,mdxType:"MDXLayout"}),(0,i.kt)("p",null,"Illumina Connected Annotations is written in C# using ",(0,i.kt)("a",{parentName:"p",href:"https://www.microsoft.com/net/download/core"},".NET Core")," (an amazing runtime environment that currently runs on Windows, Linux, Mac OS X, and in Docker images). 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Please make sure that you have the most current runtime from the ",(0,i.kt)("a",{parentName:"p",href:"https://www.microsoft.com/net/download/core"},".NET Core downloads")," page."))),(0,i.kt)("h2",{id:"getting-illumina-connected-annotations"},"Getting Illumina Connected Annotations"),(0,i.kt)("h3",{id:"latest-release"},"Latest Release"),(0,i.kt)("p",null,"Please visit ",(0,i.kt)("a",{parentName:"p",href:"https://developer.illumina.com/IlluminaConnectedAnnotations"},"Illumina Connected Annotations"),". to obtain the latest release."),(0,i.kt)("pre",null,(0,i.kt)("code",{parentName:"pre",className:"language-bash"},"mkdir -p IlluminaConnectedAnnotations/Data\ncd IlluminaConnectedAnnotations\nunzip IlluminaConnectedAnnotations-3.22.0-0-gc13dcb61-net6.0.zip\n")),(0,i.kt)("h3",{id:"quick-start"},"Quick Start"),(0,i.kt)("p",null,"If you want to get started right away, we've created ",(0,i.kt)("a",{target:"_blank",href:t(172).Z},"a script")," that unzips the Illumina Connected Annotations build, downloads the annotation data, and starts annotating a test file:"),(0,i.kt)("pre",null,(0,i.kt)("code",{parentName:"pre",className:"language-bash"},"bash ./TestIlluminaConnectedAnnotations.sh IlluminaConnectedAnnotationsBuild.zip\n")),(0,i.kt)("p",null,"We have verified that this script works on Windows (using Git Bash or WSL), Linux, and Mac OS X."),(0,i.kt)("h3",{id:"docker"},"Docker"),(0,i.kt)("p",null,"Obtain the docker image in a zip file (e.g. IlluminaConnectedAnnotations-3.22.0-0-gc13dcb61-net6.0-docker.tar.gz), and load it as follows"),(0,i.kt)("pre",null,(0,i.kt)("code",{parentName:"pre",className:"language-bash"},"docker load < IlluminaConnectedAnnotations-3.22.0-0-gc13dcb61-net6.0-docker.tar.gz\n")),(0,i.kt)("p",null,"If you want to build your own docker image, it is really easy to do. You just need to have Illumina Connected Annotations zip file and then download the ",(0,i.kt)("a",{parentName:"p",href:"https://illumina.github.io/IlluminaConnectedAnnotationsDocumentation/files/Dockerfile.sh"},"Dockerfile")," and ",(0,i.kt)("a",{parentName:"p",href:"https://illumina.github.io/IlluminaConnectedAnnotationsDocumentation/files/create_docker_image.sh"},"this script"),"."),(0,i.kt)("p",null,"Put both files (",(0,i.kt)("inlineCode",{parentName:"p"},"create_docker_image.sh")," and ",(0,i.kt)("inlineCode",{parentName:"p"},"Dockerfile"),") inside the same folder."),(0,i.kt)("p",null,"In terminal, execute command below inside the folder where you put those scripts:"),(0,i.kt)("pre",null,(0,i.kt)("code",{parentName:"pre",className:"language-bash"},"chmod +x create_docker_image.sh\n./create_docker_image.sh [path to zip file] [image tag]\n")),(0,i.kt)("p",null,"After you run the script, the docker image will be available in your local machine with image name ",(0,i.kt)("inlineCode",{parentName:"p"},"illumina-connected-annotations:[image tag specified]"),"."),(0,i.kt)("p",null,"For Docker, we have special instructions for running the Downloader:"),(0,i.kt)("pre",null,(0,i.kt)("code",{parentName:"pre",className:"language-bash"},"docker run --rm -it -v local/data/folder:/scratch illumina-connected-annotations:v3.22.0 Downloader --ga GRCh37 -o /scratch\n")),(0,i.kt)("p",null,"Similarly, we have special instructions for running IlluminaConnectedAnnotations (Here's ",(0,i.kt)("a",{parentName:"p",href:"https://illumina.github.io/IlluminaConnectedAnnotationsDocumentation/files/HiSeq.10000.vcf.gz"},"a toy VCF")," in case you need it):"),(0,i.kt)("pre",null,(0,i.kt)("code",{parentName:"pre",className:"language-bash"},"docker run --rm -it -v local/data/folder:/scratch illumina-connected-annotations:v3.22.0 IlluminaConnectedAnnotations -c /scratch/Cache/ \\\n -r /scratch/References/Homo_sapiens.GRCh37.Nirvana.dat \\\n --sd /scratch/SupplementaryAnnotation/GRCh37 \\\n -i /scratch/HiSeq.10000.vcf.gz -o /scratch/HiSeq\n")),(0,i.kt)("div",{className:"admonition admonition-caution alert alert--warning"},(0,i.kt)("div",{parentName:"div",className:"admonition-heading"},(0,i.kt)("h5",{parentName:"div"},(0,i.kt)("span",{parentName:"h5",className:"admonition-icon"},(0,i.kt)("svg",{parentName:"span",xmlns:"http://www.w3.org/2000/svg",width:"16",height:"16",viewBox:"0 0 16 16"},(0,i.kt)("path",{parentName:"svg",fillRule:"evenodd",d:"M8.893 1.5c-.183-.31-.52-.5-.887-.5s-.703.19-.886.5L.138 13.499a.98.98 0 0 0 0 1.001c.193.31.53.501.886.501h13.964c.367 0 .704-.19.877-.5a1.03 1.03 0 0 0 .01-1.002L8.893 1.5zm.133 11.497H6.987v-2.003h2.039v2.003zm0-3.004H6.987V5.987h2.039v4.006z"}))),"caution")),(0,i.kt)("div",{parentName:"div",className:"admonition-content"},(0,i.kt)("p",{parentName:"div"},"Please note that since our data files are usually accessed through a Docker volume, there is a noticeable performance penalty when running Illumina Connected Annotations in Docker."))),(0,i.kt)("div",{className:"admonition admonition-tip alert alert--success"},(0,i.kt)("div",{parentName:"div",className:"admonition-heading"},(0,i.kt)("h5",{parentName:"div"},(0,i.kt)("span",{parentName:"h5",className:"admonition-icon"},(0,i.kt)("svg",{parentName:"span",xmlns:"http://www.w3.org/2000/svg",width:"12",height:"16",viewBox:"0 0 12 16"},(0,i.kt)("path",{parentName:"svg",fillRule:"evenodd",d:"M6.5 0C3.48 0 1 2.19 1 5c0 .92.55 2.25 1 3 1.34 2.25 1.78 2.78 2 4v1h5v-1c.22-1.22.66-1.75 2-4 .45-.75 1-2.08 1-3 0-2.81-2.48-5-5.5-5zm3.64 7.48c-.25.44-.47.8-.67 1.11-.86 1.41-1.25 2.06-1.45 3.23-.02.05-.02.11-.02.17H5c0-.06 0-.13-.02-.17-.2-1.17-.59-1.83-1.45-3.23-.2-.31-.42-.67-.67-1.11C2.44 6.78 2 5.65 2 5c0-2.2 2.02-4 4.5-4 1.22 0 2.36.42 3.22 1.19C10.55 2.94 11 3.94 11 5c0 .66-.44 1.78-.86 2.48zM4 14h5c-.23 1.14-1.3 2-2.5 2s-2.27-.86-2.5-2z"}))),"tip")),(0,i.kt)("div",{parentName:"div",className:"admonition-content"},(0,i.kt)("p",{parentName:"div"},"For convenience, the user is encouraged to create aliases for the docker commands. For example:"),(0,i.kt)("pre",{parentName:"div"},(0,i.kt)("code",{parentName:"pre",className:"language-bash"},'alias IlluminaConnectedAnnotations="docker run --rm -it -v local/data/folder:/scratch illumina-connected-annotations:v3.22.0 IlluminaConnectedAnnotations"\n')))),(0,i.kt)("h2",{id:"downloading-the-data-files"},"Downloading the data files"),(0,i.kt)("p",null,"To download the latest data sources (or update the ones that you already have), use the following command to automate the download from S3:"),(0,i.kt)("pre",null,(0,i.kt)("code",{parentName:"pre",className:"language-bash"},"dotnet bin/Release/net6.0/Downloader.dll \\\n --ga GRCh37 \\\n -o Data\n")),(0,i.kt)("ul",null,(0,i.kt)("li",{parentName:"ul"},"the ",(0,i.kt)("inlineCode",{parentName:"li"},"--ga")," argument specifies the genome assembly which can be ",(0,i.kt)("inlineCode",{parentName:"li"},"GRCh37"),", ",(0,i.kt)("inlineCode",{parentName:"li"},"GRCh38"),", or ",(0,i.kt)("inlineCode",{parentName:"li"},"both"),"."),(0,i.kt)("li",{parentName:"ul"},"the ",(0,i.kt)("inlineCode",{parentName:"li"},"-o")," argument specifies the output directory")),(0,i.kt)("div",{className:"admonition admonition-info alert alert--info"},(0,i.kt)("div",{parentName:"div",className:"admonition-heading"},(0,i.kt)("h5",{parentName:"div"},(0,i.kt)("span",{parentName:"h5",className:"admonition-icon"},(0,i.kt)("svg",{parentName:"span",xmlns:"http://www.w3.org/2000/svg",width:"14",height:"16",viewBox:"0 0 14 16"},(0,i.kt)("path",{parentName:"svg",fillRule:"evenodd",d:"M7 2.3c3.14 0 5.7 2.56 5.7 5.7s-2.56 5.7-5.7 5.7A5.71 5.71 0 0 1 1.3 8c0-3.14 2.56-5.7 5.7-5.7zM7 1C3.14 1 0 4.14 0 8s3.14 7 7 7 7-3.14 7-7-3.14-7-7-7zm1 3H6v5h2V4zm0 6H6v2h2v-2z"}))),"Glitches in the Matrix")),(0,i.kt)("div",{parentName:"div",className:"admonition-content"},(0,i.kt)("p",{parentName:"div"},"Every once in a while, the download process does not go smoothly. Perhaps the internet connection cut out or you ran out of disk space. The Downloader attempts to detect these situations by checking the file sizes at the very end. If you see that a file was marked ",(0,i.kt)("inlineCode",{parentName:"p"},"truncated"),", try fixing the root cause and running the downloader again."))),(0,i.kt)("div",{className:"admonition admonition-tip alert alert--success"},(0,i.kt)("div",{parentName:"div",className:"admonition-heading"},(0,i.kt)("h5",{parentName:"div"},(0,i.kt)("span",{parentName:"h5",className:"admonition-icon"},(0,i.kt)("svg",{parentName:"span",xmlns:"http://www.w3.org/2000/svg",width:"12",height:"16",viewBox:"0 0 12 16"},(0,i.kt)("path",{parentName:"svg",fillRule:"evenodd",d:"M6.5 0C3.48 0 1 2.19 1 5c0 .92.55 2.25 1 3 1.34 2.25 1.78 2.78 2 4v1h5v-1c.22-1.22.66-1.75 2-4 .45-.75 1-2.08 1-3 0-2.81-2.48-5-5.5-5zm3.64 7.48c-.25.44-.47.8-.67 1.11-.86 1.41-1.25 2.06-1.45 3.23-.02.05-.02.11-.02.17H5c0-.06 0-.13-.02-.17-.2-1.17-.59-1.83-1.45-3.23-.2-.31-.42-.67-.67-1.11C2.44 6.78 2 5.65 2 5c0-2.2 2.02-4 4.5-4 1.22 0 2.36.42 3.22 1.19C10.55 2.94 11 3.94 11 5c0 .66-.44 1.78-.86 2.48zM4 14h5c-.23 1.14-1.3 2-2.5 2s-2.27-.86-2.5-2z"}))),"tip")),(0,i.kt)("div",{parentName:"div",className:"admonition-content"},(0,i.kt)("p",{parentName:"div"},"From time to time, you can re-run the Downloader to get the latest annotation files. It will only download the files that changed."))),(0,i.kt)("h2",{id:"download-a-test-vcf-file"},"Download a test VCF file"),(0,i.kt)("p",null,"Here's ",(0,i.kt)("a",{parentName:"p",href:"https://illumina.github.io/IlluminaConnectedAnnotationsDocumentation/files/HiSeq.10000.vcf.gz"},"a toy VCF file")," you can play around with:"),(0,i.kt)("pre",null,(0,i.kt)("code",{parentName:"pre",className:"language-bash"},"curl -O https://illumina.github.io/IlluminaConnectedAnnotationsDocumentation/files/HiSeq.10000.vcf.gz\n")),(0,i.kt)("h2",{id:"running-illumina-connected-annotations"},"Running Illumina Connected Annotations"),(0,i.kt)("p",null,"Once you have downloaded the data sets, use the following command to annotate your VCF:"),(0,i.kt)("pre",null,(0,i.kt)("code",{parentName:"pre",className:"language-bash"},"dotnet Annotator.dll \\\n -c Data/Cache \\\n --sd Data/SupplementaryAnnotation/GRCh37 \\\n -r Data/References/Homo_sapiens.GRCh37.Nirvana.dat \\\n -i HiSeq.10000.vcf.gz \\\n -o HiSeq.10000\n")),(0,i.kt)("ul",null,(0,i.kt)("li",{parentName:"ul"},"the ",(0,i.kt)("inlineCode",{parentName:"li"},"-c")," argument specifies the cache directory"),(0,i.kt)("li",{parentName:"ul"},"the ",(0,i.kt)("inlineCode",{parentName:"li"},"--sd")," argument specifies the supplementary annotation directory"),(0,i.kt)("li",{parentName:"ul"},"the ",(0,i.kt)("inlineCode",{parentName:"li"},"-r")," argument specifies the compressed reference path"),(0,i.kt)("li",{parentName:"ul"},"the ",(0,i.kt)("inlineCode",{parentName:"li"},"-i")," argument specifies the input VCF path"),(0,i.kt)("li",{parentName:"ul"},"the ",(0,i.kt)("inlineCode",{parentName:"li"},"-o")," argument specifies the output filename prefix")),(0,i.kt)("p",null,"When running Illumina Connected Annotations, performance metrics are shown as it evaluates each chromosome in the input VCF file:"),(0,i.kt)("pre",null,(0,i.kt)("code",{parentName:"pre",className:"language-bash"},"---------------------------------------------------------------------------\nIllumina Connected Annotations (c) 2023 Illumina, Inc.\n 3.22.0\n---------------------------------------------------------------------------\n\nInitialization Time Positions/s\n---------------------------------------------------------------------------\nCache 00:00:00.0\nSA Position Scan 00:00:00.0 153,634\n\nReference Preload Annotation Variants/s\n---------------------------------------------------------------------------\nchr1 00:00:00.2 00:00:00.8 11,873\n\nSummary Time Percent\n---------------------------------------------------------------------------\nInitialization 00:00:00.0 1.5 %\nPreload 00:00:00.2 4.9 %\nAnnotation 00:00:00.8 18.5 %\n\nTime: 00:00:04.4\n")),(0,i.kt)("p",null,"The output will be a JSON file called ",(0,i.kt)("inlineCode",{parentName:"p"},"HiSeq.10000.json.gz"),". Here's ",(0,i.kt)("a",{parentName:"p",href:"https://illumina.github.io/IlluminaConnectedAnnotationsDocumentation/files/HiSeq.10000.json.gz"},"the full JSON file"),"."),(0,i.kt)("h2",{id:"the-illumina-connected-annotations-command-line"},"The Illumina Connected Annotations command line"),(0,i.kt)("p",null,"The full command line options can be viewed by using the ",(0,i.kt)("inlineCode",{parentName:"p"},"-h")," option or no options"),(0,i.kt)("pre",null,(0,i.kt)("code",{parentName:"pre",className:"language-bash"},"dotnet Annotator.dll\n---------------------------------------------------------------------------\nIllumina Connected Annotations (c) 2023 Illumina, Inc.\n 3.22.0\n---------------------------------------------------------------------------\n\nUSAGE: dotnet Annotator.dll -i -c --sd -r -o \nAnnotates a set of variants\n\nOPTIONS:\n --cache, -c \n input cache directory\n --in, -i input VCF path\n --out, -o output file path\n --ref, -r input compressed reference sequence path\n --sd input supplementary annotation directory\n --sources, -s annotation data sources to be used (comma\n separated list of supported tags)\n --force-mt forces to annotate mitochondrial variants\n --legacy-vids enables support for legacy VIDs\n --enable-dq report DQ from VCF samples field\n --enable-bidirectional-fusions\n enables support for bidirectional gene fusions\n --str user provided STR annotation TSV file\n --vcf-info additional vcf info field keys (comma separated)\n desired in the output\n --vcf-sample-info \n additional vcf format field keys (comma separated)\n desired in the output\n --help, -h displays the help menu\n --version, -v displays the version\n\nSupplementary annotation version: 69, Reference version: 7\n")),(0,i.kt)("h3",{id:"specifying-annotation-sources"},"Specifying annotation sources"),(0,i.kt)("p",null,"By default, Illumina Connected Annotations will use all available data sources. However, the user can customize the set of sources using the ",(0,i.kt)("inlineCode",{parentName:"p"},"--sources|-s")," option. If an unknown source is specified, a warning message will be printed."),(0,i.kt)("pre",null,(0,i.kt)("code",{parentName:"pre",className:"language-bash"},"dotnet Annotator.dll \\\n -c Data/Cache/GRCh37 \\\n --sd Data/SupplementaryAnnotation/GRCh37 \\\n -r Data/References/Homo_sapiens.GRCh37.Nirvana.dat \\\n -i HiSeq.10000.vcf.gz \\\n -o HiSeq.10000 \\\n -s omim,gnomad,ense\n ---------------------------------------------------------------------------\n Illumina Connected Annotations (c) 2023 Illumina, Inc.\n 3.22.0\n ---------------------------------------------------------------------------\n\n WARNING: Unknown tag in data-sources: ense.\n Available values are: aminoAcidConservation,primateAI,dbsnp,spliceAI,revel,cosmic,clinvar,gnomad,\n mitomap,oneKg,gmeVariome,topmed,clingen,decipher,gnomAD-preview,clingenDosageSensitivityMap,\n gerpScore,dannScore,omim,clingenGeneValidity,phylopScore,lowComplexityRegion,refMinor,\n heteroplasmy,Ensembl,RefSeq\n\n Initialization Time Positions/s\n ---------------------------------------------------------------------------\n SA Position Scan 00:00:00.3 307,966\n ....\n ..\n")),(0,i.kt)("p",null,"The list of available values is compiled from the files provided (using ",(0,i.kt)("inlineCode",{parentName:"p"},"-c")," and ",(0,i.kt)("inlineCode",{parentName:"p"},"--sd")," options)."))}m.isMDXComponent=!0},172:(e,n,t)=>{t.d(n,{Z:()=>a});const a=t.p+"assets/files/TestIlluminaConnectedAnnotations-e26785a7184802763e147e22e2a39eb6.sh"}}]); \ No newline at end of file diff --git a/assets/js/f048ed9e.ff1332e3.js b/assets/js/f048ed9e.ff1332e3.js new file mode 100644 index 00000000..5a639d9b --- /dev/null +++ b/assets/js/f048ed9e.ff1332e3.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunknirvana_documentation=self.webpackChunknirvana_documentation||[]).push([[9962],{3905:(e,n,t)=>{t.d(n,{Zo:()=>d,kt:()=>h});var a=t(7294);function i(e,n,t){return n in e?Object.defineProperty(e,n,{value:t,enumerable:!0,configurable:!0,writable:!0}):e[n]=t,e}function o(e,n){var t=Object.keys(e);if(Object.getOwnPropertySymbols){var a=Object.getOwnPropertySymbols(e);n&&(a=a.filter((function(n){return Object.getOwnPropertyDescriptor(e,n).enumerable}))),t.push.apply(t,a)}return t}function l(e){for(var n=1;n=0||(i[t]=e[t]);return i}(e,n);if(Object.getOwnPropertySymbols){var o=Object.getOwnPropertySymbols(e);for(a=0;a=0||Object.prototype.propertyIsEnumerable.call(e,t)&&(i[t]=e[t])}return i}var s=a.createContext({}),c=function(e){var n=a.useContext(s),t=n;return e&&(t="function"==typeof e?e(n):l(l({},n),e)),t},d=function(e){var n=c(e.components);return a.createElement(s.Provider,{value:n},e.children)},m="mdxType",u={inlineCode:"code",wrapper:function(e){var n=e.children;return a.createElement(a.Fragment,{},n)}},p=a.forwardRef((function(e,n){var t=e.components,i=e.mdxType,o=e.originalType,s=e.parentName,d=r(e,["components","mdxType","originalType","parentName"]),m=c(t),p=i,h=m["".concat(s,".").concat(p)]||m[p]||u[p]||o;return t?a.createElement(h,l(l({ref:n},d),{},{components:t})):a.createElement(h,l({ref:n},d))}));function h(e,n){var t=arguments,i=n&&n.mdxType;if("string"==typeof e||i){var o=t.length,l=new Array(o);l[0]=p;var r={};for(var s in n)hasOwnProperty.call(n,s)&&(r[s]=n[s]);r.originalType=e,r[m]="string"==typeof e?e:i,l[1]=r;for(var c=2;c{t.r(n),t.d(n,{contentTitle:()=>l,default:()=>m,frontMatter:()=>o,metadata:()=>r,toc:()=>s});var a=t(7462),i=(t(7294),t(3905));const o={title:"Getting Started"},l=void 0,r={unversionedId:"introduction/getting-started",id:"introduction/getting-started",title:"Getting Started",description:"Illumina Connected Annotations is written in C# using .NET Core (an amazing runtime environment that currently runs on Windows, Linux, Mac OS X, and in Docker images). Once .NET Core has been downloaded, all you need to do is grab the source, compile it, and grab the data files.",source:"@site/docs/introduction/getting-started.md",sourceDirName:"introduction",slug:"/introduction/getting-started",permalink:"/IlluminaConnectedAnnotationsDocumentation/introduction/getting-started",editUrl:"https://github.com/Illumina/IlluminaConnectedAnnotationsDocumentation/edit/master/docs/introduction/getting-started.md",tags:[],version:"current",frontMatter:{title:"Getting Started"},sidebar:"docs",previous:{title:"Dependencies",permalink:"/IlluminaConnectedAnnotationsDocumentation/introduction/dependencies"},next:{title:"Parsing Illumina Connected Annotations JSON",permalink:"/IlluminaConnectedAnnotationsDocumentation/introduction/parsing-json"}},s=[{value:"Getting Illumina Connected Annotations",id:"getting-illumina-connected-annotations",children:[{value:"Latest Release",id:"latest-release",children:[],level:3},{value:"Quick Start",id:"quick-start",children:[],level:3},{value:"Docker",id:"docker",children:[],level:3}],level:2},{value:"Downloading the data files",id:"downloading-the-data-files",children:[],level:2},{value:"Download a test VCF file",id:"download-a-test-vcf-file",children:[],level:2},{value:"Running Illumina Connected Annotations",id:"running-illumina-connected-annotations",children:[],level:2},{value:"The Illumina Connected Annotations command line",id:"the-illumina-connected-annotations-command-line",children:[{value:"Specifying annotation sources",id:"specifying-annotation-sources",children:[],level:3}],level:2}],c={toc:s},d="wrapper";function m(e){let{components:n,...o}=e;return(0,i.kt)(d,(0,a.Z)({},c,o,{components:n,mdxType:"MDXLayout"}),(0,i.kt)("p",null,"Illumina Connected Annotations is written in C# using ",(0,i.kt)("a",{parentName:"p",href:"https://www.microsoft.com/net/download/core"},".NET Core")," (an amazing runtime environment that currently runs on Windows, Linux, Mac OS X, and in Docker images). Once .NET Core has been downloaded, all you need to do is grab the source, compile it, and grab the data files."),(0,i.kt)("div",{className:"admonition admonition-tip alert alert--success"},(0,i.kt)("div",{parentName:"div",className:"admonition-heading"},(0,i.kt)("h5",{parentName:"div"},(0,i.kt)("span",{parentName:"h5",className:"admonition-icon"},(0,i.kt)("svg",{parentName:"span",xmlns:"http://www.w3.org/2000/svg",width:"12",height:"16",viewBox:"0 0 12 16"},(0,i.kt)("path",{parentName:"svg",fillRule:"evenodd",d:"M6.5 0C3.48 0 1 2.19 1 5c0 .92.55 2.25 1 3 1.34 2.25 1.78 2.78 2 4v1h5v-1c.22-1.22.66-1.75 2-4 .45-.75 1-2.08 1-3 0-2.81-2.48-5-5.5-5zm3.64 7.48c-.25.44-.47.8-.67 1.11-.86 1.41-1.25 2.06-1.45 3.23-.02.05-.02.11-.02.17H5c0-.06 0-.13-.02-.17-.2-1.17-.59-1.83-1.45-3.23-.2-.31-.42-.67-.67-1.11C2.44 6.78 2 5.65 2 5c0-2.2 2.02-4 4.5-4 1.22 0 2.36.42 3.22 1.19C10.55 2.94 11 3.94 11 5c0 .66-.44 1.78-.86 2.48zM4 14h5c-.23 1.14-1.3 2-2.5 2s-2.27-.86-2.5-2z"}))),"tip")),(0,i.kt)("div",{parentName:"div",className:"admonition-content"},(0,i.kt)("p",{parentName:"div"},"Illumina Connected Annotations currently uses .NET6.0. Please make sure that you have the most current runtime from the ",(0,i.kt)("a",{parentName:"p",href:"https://www.microsoft.com/net/download/core"},".NET Core downloads")," page."))),(0,i.kt)("h2",{id:"getting-illumina-connected-annotations"},"Getting Illumina Connected Annotations"),(0,i.kt)("h3",{id:"latest-release"},"Latest Release"),(0,i.kt)("p",null,"Please visit ",(0,i.kt)("a",{parentName:"p",href:"https://developer.illumina.com/IlluminaConnectedAnnotations"},"Illumina Connected Annotations"),". to obtain the latest release."),(0,i.kt)("pre",null,(0,i.kt)("code",{parentName:"pre",className:"language-bash"},"mkdir -p IlluminaConnectedAnnotations/Data\ncd IlluminaConnectedAnnotations\nunzip IlluminaConnectedAnnotations-3.22.0-0-gc13dcb61-net6.0.zip\n")),(0,i.kt)("h3",{id:"quick-start"},"Quick Start"),(0,i.kt)("p",null,"If you want to get started right away, we've created ",(0,i.kt)("a",{target:"_blank",href:t(172).Z},"a script")," that unzips the Illumina Connected Annotations build, downloads the annotation data, and starts annotating a test file:"),(0,i.kt)("pre",null,(0,i.kt)("code",{parentName:"pre",className:"language-bash"},"bash ./TestIlluminaConnectedAnnotations.sh IlluminaConnectedAnnotationsBuild.zip\n")),(0,i.kt)("p",null,"We have verified that this script works on Windows (using Git Bash or WSL), Linux, and Mac OS X."),(0,i.kt)("h3",{id:"docker"},"Docker"),(0,i.kt)("p",null,"Obtain the docker image in a zip file (e.g. IlluminaConnectedAnnotations-3.22.0-0-gc13dcb61-net6.0-docker.tar.gz), and load it as follows"),(0,i.kt)("pre",null,(0,i.kt)("code",{parentName:"pre",className:"language-bash"},"docker load < IlluminaConnectedAnnotations-3.22.0-0-gc13dcb61-net6.0-docker.tar.gz\n")),(0,i.kt)("p",null,"If you want to build your own docker image, it is really easy to do. You just need to have Illumina Connected Annotations zip file and then download the ",(0,i.kt)("a",{parentName:"p",href:"https://illumina.github.io/IlluminaConnectedAnnotationsDocumentation/files/Dockerfile"},"Dockerfile")," and ",(0,i.kt)("a",{parentName:"p",href:"https://illumina.github.io/IlluminaConnectedAnnotationsDocumentation/files/create_docker_image.sh"},"this script"),"."),(0,i.kt)("p",null,"Put both files (",(0,i.kt)("inlineCode",{parentName:"p"},"create_docker_image.sh")," and ",(0,i.kt)("inlineCode",{parentName:"p"},"Dockerfile"),") inside the same folder."),(0,i.kt)("p",null,"In terminal, execute command below inside the folder where you put those scripts:"),(0,i.kt)("pre",null,(0,i.kt)("code",{parentName:"pre",className:"language-bash"},"chmod +x create_docker_image.sh\n./create_docker_image.sh [path to zip file] [image tag]\n")),(0,i.kt)("p",null,"After you run the script, the docker image will be available in your local machine with image name ",(0,i.kt)("inlineCode",{parentName:"p"},"illumina-connected-annotations:[image tag specified]"),"."),(0,i.kt)("p",null,"For Docker, we have special instructions for running the Downloader:"),(0,i.kt)("pre",null,(0,i.kt)("code",{parentName:"pre",className:"language-bash"},"docker run --rm -it -v local/data/folder:/scratch illumina-connected-annotations:v3.22.0 Downloader --ga GRCh37 -o /scratch\n")),(0,i.kt)("p",null,"Similarly, we have special instructions for running IlluminaConnectedAnnotations (Here's ",(0,i.kt)("a",{parentName:"p",href:"https://illumina.github.io/IlluminaConnectedAnnotationsDocumentation/files/HiSeq.10000.vcf.gz"},"a toy VCF")," in case you need it):"),(0,i.kt)("pre",null,(0,i.kt)("code",{parentName:"pre",className:"language-bash"},"docker run --rm -it -v local/data/folder:/scratch illumina-connected-annotations:v3.22.0 IlluminaConnectedAnnotations -c /scratch/Cache/ \\\n -r /scratch/References/Homo_sapiens.GRCh37.Nirvana.dat \\\n --sd /scratch/SupplementaryAnnotation/GRCh37 \\\n -i /scratch/HiSeq.10000.vcf.gz -o /scratch/HiSeq\n")),(0,i.kt)("div",{className:"admonition admonition-caution alert alert--warning"},(0,i.kt)("div",{parentName:"div",className:"admonition-heading"},(0,i.kt)("h5",{parentName:"div"},(0,i.kt)("span",{parentName:"h5",className:"admonition-icon"},(0,i.kt)("svg",{parentName:"span",xmlns:"http://www.w3.org/2000/svg",width:"16",height:"16",viewBox:"0 0 16 16"},(0,i.kt)("path",{parentName:"svg",fillRule:"evenodd",d:"M8.893 1.5c-.183-.31-.52-.5-.887-.5s-.703.19-.886.5L.138 13.499a.98.98 0 0 0 0 1.001c.193.31.53.501.886.501h13.964c.367 0 .704-.19.877-.5a1.03 1.03 0 0 0 .01-1.002L8.893 1.5zm.133 11.497H6.987v-2.003h2.039v2.003zm0-3.004H6.987V5.987h2.039v4.006z"}))),"caution")),(0,i.kt)("div",{parentName:"div",className:"admonition-content"},(0,i.kt)("p",{parentName:"div"},"Please note that since our data files are usually accessed through a Docker volume, there is a noticeable performance penalty when running Illumina Connected Annotations in Docker."))),(0,i.kt)("div",{className:"admonition admonition-tip alert alert--success"},(0,i.kt)("div",{parentName:"div",className:"admonition-heading"},(0,i.kt)("h5",{parentName:"div"},(0,i.kt)("span",{parentName:"h5",className:"admonition-icon"},(0,i.kt)("svg",{parentName:"span",xmlns:"http://www.w3.org/2000/svg",width:"12",height:"16",viewBox:"0 0 12 16"},(0,i.kt)("path",{parentName:"svg",fillRule:"evenodd",d:"M6.5 0C3.48 0 1 2.19 1 5c0 .92.55 2.25 1 3 1.34 2.25 1.78 2.78 2 4v1h5v-1c.22-1.22.66-1.75 2-4 .45-.75 1-2.08 1-3 0-2.81-2.48-5-5.5-5zm3.64 7.48c-.25.44-.47.8-.67 1.11-.86 1.41-1.25 2.06-1.45 3.23-.02.05-.02.11-.02.17H5c0-.06 0-.13-.02-.17-.2-1.17-.59-1.83-1.45-3.23-.2-.31-.42-.67-.67-1.11C2.44 6.78 2 5.65 2 5c0-2.2 2.02-4 4.5-4 1.22 0 2.36.42 3.22 1.19C10.55 2.94 11 3.94 11 5c0 .66-.44 1.78-.86 2.48zM4 14h5c-.23 1.14-1.3 2-2.5 2s-2.27-.86-2.5-2z"}))),"tip")),(0,i.kt)("div",{parentName:"div",className:"admonition-content"},(0,i.kt)("p",{parentName:"div"},"For convenience, the user is encouraged to create aliases for the docker commands. For example:"),(0,i.kt)("pre",{parentName:"div"},(0,i.kt)("code",{parentName:"pre",className:"language-bash"},'alias IlluminaConnectedAnnotations="docker run --rm -it -v local/data/folder:/scratch illumina-connected-annotations:v3.22.0 IlluminaConnectedAnnotations"\n')))),(0,i.kt)("h2",{id:"downloading-the-data-files"},"Downloading the data files"),(0,i.kt)("p",null,"To download the latest data sources (or update the ones that you already have), use the following command to automate the download from S3:"),(0,i.kt)("pre",null,(0,i.kt)("code",{parentName:"pre",className:"language-bash"},"dotnet bin/Release/net6.0/Downloader.dll \\\n --ga GRCh37 \\\n -o Data\n")),(0,i.kt)("ul",null,(0,i.kt)("li",{parentName:"ul"},"the ",(0,i.kt)("inlineCode",{parentName:"li"},"--ga")," argument specifies the genome assembly which can be ",(0,i.kt)("inlineCode",{parentName:"li"},"GRCh37"),", ",(0,i.kt)("inlineCode",{parentName:"li"},"GRCh38"),", or ",(0,i.kt)("inlineCode",{parentName:"li"},"both"),"."),(0,i.kt)("li",{parentName:"ul"},"the ",(0,i.kt)("inlineCode",{parentName:"li"},"-o")," argument specifies the output directory")),(0,i.kt)("div",{className:"admonition admonition-info alert alert--info"},(0,i.kt)("div",{parentName:"div",className:"admonition-heading"},(0,i.kt)("h5",{parentName:"div"},(0,i.kt)("span",{parentName:"h5",className:"admonition-icon"},(0,i.kt)("svg",{parentName:"span",xmlns:"http://www.w3.org/2000/svg",width:"14",height:"16",viewBox:"0 0 14 16"},(0,i.kt)("path",{parentName:"svg",fillRule:"evenodd",d:"M7 2.3c3.14 0 5.7 2.56 5.7 5.7s-2.56 5.7-5.7 5.7A5.71 5.71 0 0 1 1.3 8c0-3.14 2.56-5.7 5.7-5.7zM7 1C3.14 1 0 4.14 0 8s3.14 7 7 7 7-3.14 7-7-3.14-7-7-7zm1 3H6v5h2V4zm0 6H6v2h2v-2z"}))),"Glitches in the Matrix")),(0,i.kt)("div",{parentName:"div",className:"admonition-content"},(0,i.kt)("p",{parentName:"div"},"Every once in a while, the download process does not go smoothly. Perhaps the internet connection cut out or you ran out of disk space. The Downloader attempts to detect these situations by checking the file sizes at the very end. If you see that a file was marked ",(0,i.kt)("inlineCode",{parentName:"p"},"truncated"),", try fixing the root cause and running the downloader again."))),(0,i.kt)("div",{className:"admonition admonition-tip alert alert--success"},(0,i.kt)("div",{parentName:"div",className:"admonition-heading"},(0,i.kt)("h5",{parentName:"div"},(0,i.kt)("span",{parentName:"h5",className:"admonition-icon"},(0,i.kt)("svg",{parentName:"span",xmlns:"http://www.w3.org/2000/svg",width:"12",height:"16",viewBox:"0 0 12 16"},(0,i.kt)("path",{parentName:"svg",fillRule:"evenodd",d:"M6.5 0C3.48 0 1 2.19 1 5c0 .92.55 2.25 1 3 1.34 2.25 1.78 2.78 2 4v1h5v-1c.22-1.22.66-1.75 2-4 .45-.75 1-2.08 1-3 0-2.81-2.48-5-5.5-5zm3.64 7.48c-.25.44-.47.8-.67 1.11-.86 1.41-1.25 2.06-1.45 3.23-.02.05-.02.11-.02.17H5c0-.06 0-.13-.02-.17-.2-1.17-.59-1.83-1.45-3.23-.2-.31-.42-.67-.67-1.11C2.44 6.78 2 5.65 2 5c0-2.2 2.02-4 4.5-4 1.22 0 2.36.42 3.22 1.19C10.55 2.94 11 3.94 11 5c0 .66-.44 1.78-.86 2.48zM4 14h5c-.23 1.14-1.3 2-2.5 2s-2.27-.86-2.5-2z"}))),"tip")),(0,i.kt)("div",{parentName:"div",className:"admonition-content"},(0,i.kt)("p",{parentName:"div"},"From time to time, you can re-run the Downloader to get the latest annotation files. It will only download the files that changed."))),(0,i.kt)("h2",{id:"download-a-test-vcf-file"},"Download a test VCF file"),(0,i.kt)("p",null,"Here's ",(0,i.kt)("a",{parentName:"p",href:"https://illumina.github.io/IlluminaConnectedAnnotationsDocumentation/files/HiSeq.10000.vcf.gz"},"a toy VCF file")," you can play around with:"),(0,i.kt)("pre",null,(0,i.kt)("code",{parentName:"pre",className:"language-bash"},"curl -O https://illumina.github.io/IlluminaConnectedAnnotationsDocumentation/files/HiSeq.10000.vcf.gz\n")),(0,i.kt)("h2",{id:"running-illumina-connected-annotations"},"Running Illumina Connected Annotations"),(0,i.kt)("p",null,"Once you have downloaded the data sets, use the following command to annotate your VCF:"),(0,i.kt)("pre",null,(0,i.kt)("code",{parentName:"pre",className:"language-bash"},"dotnet Annotator.dll \\\n -c Data/Cache \\\n --sd Data/SupplementaryAnnotation/GRCh37 \\\n -r Data/References/Homo_sapiens.GRCh37.Nirvana.dat \\\n -i HiSeq.10000.vcf.gz \\\n -o HiSeq.10000\n")),(0,i.kt)("ul",null,(0,i.kt)("li",{parentName:"ul"},"the ",(0,i.kt)("inlineCode",{parentName:"li"},"-c")," argument specifies the cache directory"),(0,i.kt)("li",{parentName:"ul"},"the ",(0,i.kt)("inlineCode",{parentName:"li"},"--sd")," argument specifies the supplementary annotation directory"),(0,i.kt)("li",{parentName:"ul"},"the ",(0,i.kt)("inlineCode",{parentName:"li"},"-r")," argument specifies the compressed reference path"),(0,i.kt)("li",{parentName:"ul"},"the ",(0,i.kt)("inlineCode",{parentName:"li"},"-i")," argument specifies the input VCF path"),(0,i.kt)("li",{parentName:"ul"},"the ",(0,i.kt)("inlineCode",{parentName:"li"},"-o")," argument specifies the output filename prefix")),(0,i.kt)("p",null,"When running Illumina Connected Annotations, performance metrics are shown as it evaluates each chromosome in the input VCF file:"),(0,i.kt)("pre",null,(0,i.kt)("code",{parentName:"pre",className:"language-bash"},"---------------------------------------------------------------------------\nIllumina Connected Annotations (c) 2023 Illumina, Inc.\n 3.22.0\n---------------------------------------------------------------------------\n\nInitialization Time Positions/s\n---------------------------------------------------------------------------\nCache 00:00:00.0\nSA Position Scan 00:00:00.0 153,634\n\nReference Preload Annotation Variants/s\n---------------------------------------------------------------------------\nchr1 00:00:00.2 00:00:00.8 11,873\n\nSummary Time Percent\n---------------------------------------------------------------------------\nInitialization 00:00:00.0 1.5 %\nPreload 00:00:00.2 4.9 %\nAnnotation 00:00:00.8 18.5 %\n\nTime: 00:00:04.4\n")),(0,i.kt)("p",null,"The output will be a JSON file called ",(0,i.kt)("inlineCode",{parentName:"p"},"HiSeq.10000.json.gz"),". Here's ",(0,i.kt)("a",{parentName:"p",href:"https://illumina.github.io/IlluminaConnectedAnnotationsDocumentation/files/HiSeq.10000.json.gz"},"the full JSON file"),"."),(0,i.kt)("h2",{id:"the-illumina-connected-annotations-command-line"},"The Illumina Connected Annotations command line"),(0,i.kt)("p",null,"The full command line options can be viewed by using the ",(0,i.kt)("inlineCode",{parentName:"p"},"-h")," option or no options"),(0,i.kt)("pre",null,(0,i.kt)("code",{parentName:"pre",className:"language-bash"},"dotnet Annotator.dll\n---------------------------------------------------------------------------\nIllumina Connected Annotations (c) 2023 Illumina, Inc.\n 3.22.0\n---------------------------------------------------------------------------\n\nUSAGE: dotnet Annotator.dll -i -c --sd -r -o \nAnnotates a set of variants\n\nOPTIONS:\n --cache, -c \n input cache directory\n --in, -i input VCF path\n --out, -o output file path\n --ref, -r input compressed reference sequence path\n --sd input supplementary annotation directory\n --sources, -s annotation data sources to be used (comma\n separated list of supported tags)\n --force-mt forces to annotate mitochondrial variants\n --legacy-vids enables support for legacy VIDs\n --enable-dq report DQ from VCF samples field\n --enable-bidirectional-fusions\n enables support for bidirectional gene fusions\n --str user provided STR annotation TSV file\n --vcf-info additional vcf info field keys (comma separated)\n desired in the output\n --vcf-sample-info \n additional vcf format field keys (comma separated)\n desired in the output\n --help, -h displays the help menu\n --version, -v displays the version\n\nSupplementary annotation version: 69, Reference version: 7\n")),(0,i.kt)("h3",{id:"specifying-annotation-sources"},"Specifying annotation sources"),(0,i.kt)("p",null,"By default, Illumina Connected Annotations will use all available data sources. However, the user can customize the set of sources using the ",(0,i.kt)("inlineCode",{parentName:"p"},"--sources|-s")," option. If an unknown source is specified, a warning message will be printed."),(0,i.kt)("pre",null,(0,i.kt)("code",{parentName:"pre",className:"language-bash"},"dotnet Annotator.dll \\\n -c Data/Cache/GRCh37 \\\n --sd Data/SupplementaryAnnotation/GRCh37 \\\n -r Data/References/Homo_sapiens.GRCh37.Nirvana.dat \\\n -i HiSeq.10000.vcf.gz \\\n -o HiSeq.10000 \\\n -s omim,gnomad,ense\n ---------------------------------------------------------------------------\n Illumina Connected Annotations (c) 2023 Illumina, Inc.\n 3.22.0\n ---------------------------------------------------------------------------\n\n WARNING: Unknown tag in data-sources: ense.\n Available values are: aminoAcidConservation,primateAI,dbsnp,spliceAI,revel,cosmic,clinvar,gnomad,\n mitomap,oneKg,gmeVariome,topmed,clingen,decipher,gnomAD-preview,clingenDosageSensitivityMap,\n gerpScore,dannScore,omim,clingenGeneValidity,phylopScore,lowComplexityRegion,refMinor,\n heteroplasmy,Ensembl,RefSeq\n\n Initialization Time Positions/s\n ---------------------------------------------------------------------------\n SA Position Scan 00:00:00.3 307,966\n ....\n ..\n")),(0,i.kt)("p",null,"The list of available values is compiled from the files provided (using ",(0,i.kt)("inlineCode",{parentName:"p"},"-c")," and ",(0,i.kt)("inlineCode",{parentName:"p"},"--sd")," options)."))}m.isMDXComponent=!0},172:(e,n,t)=>{t.d(n,{Z:()=>a});const a=t.p+"assets/files/TestIlluminaConnectedAnnotations-e26785a7184802763e147e22e2a39eb6.sh"}}]); \ No newline at end of file diff --git a/assets/js/main.1ca7a4bd.js b/assets/js/main.d6673b0d.js similarity index 99% rename from assets/js/main.1ca7a4bd.js rename to assets/js/main.d6673b0d.js index a00153f5..69cb559d 100644 --- a/assets/js/main.1ca7a4bd.js +++ b/assets/js/main.d6673b0d.js @@ -1,2 +1,2 @@ -/*! 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