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

Byoungnam Min, Igor V. Grigoriev, In-Geol Choi

Research output: Contribution to journalArticle

12 Citations (Scopus)

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 highquality 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.

Original languageEnglish
Pages (from-to)2936-2937
Number of pages2
JournalBioinformatics
Volume33
Issue number18
DOIs
Publication statusPublished - 2017

ASJC Scopus subject areas

  • Statistics and Probability
  • Medicine(all)
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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