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FunGAP: Fungal Genome Annotation Pipeline v1.0.1

Last updated: Jan 7, 2019

FunGAP is freely available for academic use. For the commerical use or license of FunGAP, please contact In-Geol Choi (igchoi (at) korea.ac.kr). Please, cite the following reference

Reference: Byoungnam Min, Igor V Grigoriev, and In-Geol Choi, FunGAP: Fungal Genome Annotation Pipeline using evidence-based gene model evaluation (2017), Bioinformatics, Volume 33, Issue 18, Pages 2936–2937, https://doi.org/10.1093/bioinformatics/btx353

Usage of FunGAP

0. Prerequisites

To run FunGAP, users are required to prepare three main arguments

  • Genome assembly (FASTA)
  • Transcriptomic reads (FASTQ)
  • Protein database (FASTA)

Currently, FunGAP takes only Illumina-sequenced reads (paired-end or single-read). Paired-end read FASTQ files should have formatted file names such as XXXX_1.fastq and XXXX_2.fastq. An example would be hyphae_1.fastq and hyphae_2.fastq. For single-read, it should be like XXXX_s.fastq. Also BAM file is acceptable with --trans_bam option.

1. Preparing protein database

FunGAP requires PROTEIN DATABASE in FASTA file. We recommend three or four relatives' proteome to reduce computing time. For convenience, we provide a script download_sister_orgs.py to build your own protein database for your genome using NCBI API.

Example command ($FUNGAP_DIR is your FunGAP installation directory):

python $FUNGAP_DIR/download_sister_orgs.py \
  --download_dir sister_orgs \
  --taxon "Schizophyllum" \
  --num_sisters 3 \
  --email_address mbnmbn00@gmail.com

E-mail address is needed for NCBI Entrez. All taxon levels are allowed for --taxon argument, but genus level is appropriate. Now make a protein database.

cd sister_orgs/
zcat ./*faa.gz > prot_db.faa

You can now input prot_db.faa in the --sister_proteome argument.

2. Augustus species model

Augustus gene predictor requires to select pre-defined species model. Run augustus --species=help to print out the model list. FunGAP provides a script get_augustus_species.py to help choose proper species model.

Example command ($FUNGAP_DIR is your FunGAP installation directory):

python $FUNGAP_DIR/get_augustus_species.py \
  --genus_name "Schizophyllum" \
  --email_address mbnmbn00@gmail.com

This will suggest coprinus_cinereus and laccaria_bicolor.

3. Running FunGAP

Usage ($FUNGAP_DIR is your FunGAP installation directory):

python $FUNGAP_DIR/fungap.py \
  --output_dir <output_directory> \
  --trans_read_1 <transcriptome_reads_fastq_1> \
  --trans_read_2 <transcriptome_reads_fastq_2> \
  --genome_assembly <genome_assembly_fasta> \
  --augustus_species <augustus_species> \
  --sister_proteome <sister_proteome> \
  --num_cores <number_of_cpus_to_be_used>

4. Output

Final output will be located in fungap_out directory

  • fungap_out_prot.faa
  • fungap_out.gff3
  • fungap_out_stats.html

It took about 9 hours by dual Intel(R) Xeon(R) CPU E5-2670 v3 with 40 CPU cores.

5. Add Pfam annotation to FunGAP GFF3 output

Interproscan can infer the functions of predicted genes. The gff3_add_pfam.py script adds the annotation to the GFF3 file.

Example commands:

$ interproscan.sh -i <protein.fasta> -f tsv -appl Pfam --goterms -pa --iprlookup -b <base_name> --tempdir <TEMP-DIR>
$ gff3_add_pfam.py --input_gff3 <fungap_out.gff3> --pfam_file <interproscan_output.tsv>