PHANOTATE: a novel approach to gene identification in phage genomes

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

doi:10.1093/bioinformatics/btz265

PHANOTATE: a novel approach to gene identification in phage genomes

Bioinformatics. 2019 Nov 1;35(22):4537-4542. doi: 10.1093/bioinformatics/btz265.

Authors

Katelyn McNair¹, Carol Zhou², Elizabeth A Dinsdale³, Brian Souza², Robert A Edwards^{1

3

4}

Affiliations

¹ Computational Sciences Research Center, San Diego State University, San Diego, CA 92182, USA.
² Lawrence Livermore National Laboratory, Livermore, CA 94550, USA.
³ Department of Biology, San Diego State University, San Diego, CA 92182, USA.
⁴ Viral Information Institute, San Diego State University, San Diego, CA 92182, USA.

Abstract

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.

Publication types

Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Bacteriophages*
Genome, Viral*
Metagenome
Open Reading Frames
Sequence Analysis, DNA
Software