PHANOTATE: a novel approach to gene identification in phage genomes

McNair, Katelyn; Zhou, Carol; Dinsdale, Elizabeth A.; Souza, Brian; Edwards, Robert A.

doi:10.1093/bioinformatics/btz265

PHANOTATE: a novel approach to gene identification in phage genomes

Journal Article · Thu Apr 25 00:00:00 EDT 2019 · Bioinformatics

DOI:https://doi.org/10.1093/bioinformatics/btz265· OSTI ID:1625296

McNair, Katelyn ^[1]; Zhou, Carol ^[2]; Dinsdale, Elizabeth A. ^[3]; Souza, Brian ^[2]; Edwards, Robert A. ^[4]

San Diego State Univ., CA (United States). Computational Sciences Research Center; DOE/OSTI
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
San Diego State Univ., CA (United States). Dept. of Biology
San Diego State Univ., CA (United States). Computational Sciences Research Center. Dept. of Biology. Viral Information Inst.

Motivation: Currently there are no tools specifically designed for annotating genes in phages. Several tools are available that have been adapted to run on phage genomes, but due to their underlying design, they are unable to capture the full complexity of phage genomes. Phages have adapted their genomes to be extremely compact, having adjacent genes that overlap and genes completely inside of other longer genes. This non-delineated genome structure makes it difficult for gene prediction using the currently available gene annotators. Here we present PHANOTATE, a novel method for gene calling specifically designed for phage genomes. Although the compact nature of genes in phages is a problem for current gene annotators, we exploit this property by treating a phage genome as a network of paths: where open reading frames are favorable, and overlaps and gaps are less favorable, but still possible. We represent this network of connections as a weighted graph, and use dynamic programing to find the optimal path. Results: We compare PHANOTATE to other gene callers by annotating a set of 2133 complete phage genomes from GenBank, using PHANOTATE and the three most popular gene callers. We found that the four programs agree on 82% of the total predicted genes, with PHANOTATE predicting more genes than the other three. We searched for these extra genes in both GenBank’s non-redundant protein database and all of the metagenomes in the sequence read archive, and found that they are present at levels that suggest that these are functional protein-coding genes. Availability and implementation: https://github.com/deprekate/PHANOTATE. Supplementary information: Supplementary data are available at Bioinformatics online.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: AC52-07NA27344

OSTI ID:: 1625296

Journal Information:: Bioinformatics, Journal Name: Bioinformatics Journal Issue: 22 Vol. 35; ISSN 1367-4803

Publisher:: International Society for Computational Biology - Oxford University PressCopyright Statement

Country of Publication:: United States

Language:: English

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