Typos

docs/annotations.md

@@ -1,10 +1,10 @@
 # DeepRVAT Annotation pipeline
 
-This pipeline is based on [snakemake](https://snakemake.readthedocs.io/en/stable/). It uses [bcftools + samstools](https://www.htslib.org/), as well as [perl](https://www.perl.org/), [deepRiPe](https://ohlerlab.mdc-berlin.de/software/DeepRiPe_140/) and [deepSEA](http://deepsea.princeton.edu/) as well as [VEP](http://www.ensembl.org/info/docs/tools/vep/index.html), including plugins for [primateAI](https://github.com/Illumina/PrimateAI) and  [spliceAI](https://github.com/Illumina/SpliceAI). DeepRiPe annotations were acquired using [faatpipe repository by HealthML](https://github.com/HealthML/faatpipe)[[1]](#reference-1-target) and DeepSea annotations were calculated using [kipoi-veff2](https://github.com/kipoi/kipoi-veff2)[[2]](#reference-2-target), abSplice scores were computet using [abSplice](https://github.com/gagneurlab/absplice/)[[3]](#reference-3-target)
+This pipeline is based on [snakemake](https://snakemake.readthedocs.io/en/stable/). It uses [bcftools + samstools](https://www.htslib.org/), as well as [perl](https://www.perl.org/), [deepRiPe](https://ohlerlab.mdc-berlin.de/software/DeepRiPe_140/) and [deepSEA](http://deepsea.princeton.edu/) as well as [VEP](http://www.ensembl.org/info/docs/tools/vep/index.html), including plugins for [primateAI](https://github.com/Illumina/PrimateAI) and  [spliceAI](https://github.com/Illumina/SpliceAI). DeepRiPe annotations were acquired using [faatpipe repository by HealthML](https://github.com/HealthML/faatpipe)[[1]](#reference-1-target) and DeepSea annotations were calculated using [kipoi-veff2](https://github.com/kipoi/kipoi-veff2)[[2]](#reference-2-target), abSplice scores were computed using [abSplice](https://github.com/gagneurlab/absplice/)[[3]](#reference-3-target)
 
 ![dag](_static/annotation_rulegraph.svg)
 
-*Figure 1: Rulegraph of the annoation pipeline.*
+*Figure 1: Rule graph of the annotation pipeline.*
 
 ## Output 
 This pipeline outputs a parquet file including all annotations as well as a file containing IDs to all protein coding genes needed to run DeepRVAT. 
@@ -28,7 +28,7 @@ BCFtools as well as HTSlib should be installed on the machine,
 
 should be installed for runnning the pipeline, together with the [plugins](https://www.ensembl.org/info/docs/tools/vep/script/vep_plugins.html) for primateAI and spliceAI. Annotation data for CADD, spliceAI and primateAI should be downloaded. The path to the data may be specified in the corresponding [config file](https://github.com/PMBio/deeprvat/blob/main/pipelines/config/deeprvat_annotation_config.yaml). 
 Download paths:
-- [CADD](https://cadd.bihealth.org/download): "All possible SNVs of GRCh38/hg38" and "gnomad.genomes.r3.0.indel.tsv.gz" incl. their  Tabix Indices
+- [CADD](https://cadd.bihealth.org/download): "All possible SNVs of GRCh38/hg38" and "gnomad.genomes.r3.0.indel.tsv.gz" incl. their Tabix Indices
 - [SpliceAI](https://basespace.illumina.com/s/otSPW8hnhaZR): "genome_scores_v1.3"/"spliceai_scores.raw.snv.hg38.vcf.gz" and "spliceai_scores.raw.indel.hg38.vcf.gz" 
 - [PrimateAI](https://basespace.illumina.com/s/yYGFdGih1rXL) PrimateAI supplementary data/"PrimateAI_scores_v0.2_GRCh38_sorted.tsv.bgz"
 - [AlphaMissense](https://storage.googleapis.com/dm_alphamissense/AlphaMissense_hg38.tsv.gz) 
@@ -80,7 +80,7 @@ The config above would use the following directory structure:
 
 
 Bcf files created by the [preprocessing pipeline](https://deeprvat.readthedocs.io/en/latest/preprocessing.html) are used as input data. The input data directory should only contain the files needed. 
-The pipeline also uses the variant.tsv file, the reference file and the genotypes file from the preprocesing pipeline. 
+The pipeline also uses the variant.tsv file, the reference file and the genotypes file from the preprocessing pipeline. 
 A GTF file as described in [requirements](#requirements) and the FASTA file used for preprocessing is also necessary.
 The pipeline beginns by installing the repositories needed for the annotations, it will automatically install all repositories in the `repo_dir` folder that can be specified in the config file relative to the annotation working directory.
 The text file mapping blocks to chromosomes is stored in `metadata` folder. The output is stored in the `output_dir/annotations` folder and any temporary files in the `tmp` subfolder. All repositories used including VEP with its corresponding cache as well as plugins are stored in `repo_dir/ensempl-vep`.
@@ -107,7 +107,7 @@ Data for VEP plugins and the CADD cache are stored in `annotation data`.
 
 
 ### Running the pipeline
-This pipeline should be run after running the [preprocessing pipeline](https://deeprvat.readthedocs.io/en/latest/preprocessing.html), since it relies on some of its outpur files (specifically the bcf files in  `norm/bcf/`, the variant files in `norm/variants/` and the genotype file `preprocessed/genotypes.h5`
+This pipeline should be run after running the [preprocessing pipeline](https://deeprvat.readthedocs.io/en/latest/preprocessing.html), since it relies on some of its output files (specifically the bcf files in  `norm/bcf/`, the variant files in `norm/variants/` and the genotype file `preprocessed/genotypes.h5`
 
 After configuration and activating the `deeprvat_annotations` environment run the pipeline using snakemake:
 

docs/deeprvat.md

@@ -100,7 +100,7 @@ _Coming soon_
 (Association_testing)=
 ## Association testing using pretrained models
 
-If you already have a pretrained DeepRVAT model, we have setup pipelines for runing only the association testing stage. This includes creating the association dataset files, computing burdens, regression, and evaluation. 
+If you already have a pretrained DeepRVAT model, we have setup pipelines for running only the association testing stage. This includes creating the association dataset files, computing burdens, regression, and evaluation. 
 
 
 ### Input data
@@ -190,7 +190,7 @@ snakemake -j 1 --snakefile [path_to_deeprvat]/pipelines/association_testing_pret
 --->
 
 ## Training
-To run only the training stage of DeepRVAT, comprised of training data creation and running the DeepRVAT model, we have setup a training pipeline.
+To run only the training stage of DeepRVAT, comprised of training data creation and running the DeepRVAT model, we have set up a training pipeline.
 
 ### Input data
 The following files should be contained within your `experiment` directory: 
@@ -258,7 +258,7 @@ The following files should be contained within your `experiment` directory:
 - `protein_coding_genes.parquet`
 - `baseline_results` directory
 
-### Running the training and association testing pipelinewith SEAK
+### Running the training and association testing pipeline with SEAK
 
 ```shell
 cd experiment

docs/practical.md

@@ -15,7 +15,7 @@ Note that DeepRVAT scores are on a scale between 0 and 1, with a score closer to
 
 ### Pretrained models
 
-Some users may wish to select variants or make variant-to-gene assigments differently from our methods, or to work on datasets other than UKBB. For this, we provide an ensemble of pretrained DeepRVAT gene impairment modules, which can be used for scoring individual-gene pairs for subsequent association testing. We also provide a pipeline for functional annotation of variants for compatibility with the pretrained modules.
+Some users may wish to select variants or make variant-to-gene assignments differently from our methods, or to work on datasets other than UKBB. For this, we provide an ensemble of pretrained DeepRVAT gene impairment modules, which can be used for scoring individual-gene pairs for subsequent association testing. We also provide a pipeline for functional annotation of variants for compatibility with the pretrained modules.
 
 ### Model training