FunGAP: Fungal Genome Annotation Pipeline using evidence-based gene model evaluation

Byoungnam Min; Igor V Grigoriev; In-Geol Choi

doi:10.1093/bioinformatics/btx353

FunGAP: Fungal Genome Annotation Pipeline using evidence-based gene model evaluation

Bioinformatics. 2017 Sep 15;33(18):2936-2937. doi: 10.1093/bioinformatics/btx353.

Authors

Byoungnam Min¹, Igor V Grigoriev^{2

3}, In-Geol Choi¹

Affiliations

¹ Department of Biotechnology, College of Life Sciences and Biotechnology, Korea University, Seoul 02841, Korea.
² US Department of Energy Joint Genome Institute, Walnut Creek, CA 94598, USA.
³ Department of Plant and Microbial Biology, University of California, Berkeley, Berkeley, CA 94720, USA.

PMID: 28582481
DOI: 10.1093/bioinformatics/btx353

Abstract

Motivation: Successful genome analysis depends on the quality of gene prediction. Although fungal genome sequencing and assembly have become trivial, its annotation procedure has not been standardized yet.

Results: FunGAP predicts protein-coding genes in a fungal genome assembly. To attain high-quality gene models, this program runs multiple gene predictors, evaluates all predicted genes, and assembles gene models that are highly supported by homology to known sequences. To do this, we built a scoring function to estimate the congruency of each gene model based on known protein or domain homology.

Availability and implementation: FunGAP is written in Python script and is available in GitHub ( https://github.com/CompSynBioLab-KoreaUniv/FunGAP ). This software is freely available only for noncommercial users.

Contact: igchoi@korea.ac.kr.

Supplementary information: Supplementary data are available at Bioinformatics online.

MeSH terms

Computational Biology / methods*
Fungal Proteins*
Genome, Fungal*
Molecular Sequence Annotation / methods*
Software*

Substances

Fungal Proteins