McNair, K. | bioRxiv Science

bioRxiv2018

THEA: A novel approach to gene identification in phage genomes

MotivationCurrently 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 THEA (The Algorithm), a novel method for gene calling specifically designed for phage genomes. While 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 graph theory to find the optimal path.\n\nResultsWe compare THEA to other gene callers by annotating a set of 2,133 complete phage genomes from GenBank, using THEA and the three most popular gene callers. We found that the four programs agree on 82% of the total predicted genes, with THEA predicting significantly more genes than the other three. We searched for these extra genes in both GenBanks non-redundant protein database and sequence read archive, and found that they are present at levels that suggest that these are functional protein coding genes.\n\nAvailability and ImplementationThe source code and all files can be found at: https://github.com/deprekate/THEA\n\nContactKatelyn McNair: deprekate@gmail.com

bioinformatics↗

bioRxiv2017

Prophage genomics reveals patterns in phage genome organization and replication

Temperate phage genomes are highly variable mosaic collections of genes that infect a bacterial host, integrate into the hosts genome or replicate as low copy number plasmids, and are regulated to switch from the lysogenic to lytic cycles to generate new virions and escape their host. Genomes from most Bacterial phyla contain at least one or more prophages. We updated our PhiSpy algorithm to improve detection of prophages and to provide a web-based framework for PhiSpy. We have used this algorithm to identify 36,488 prophage regions from 11,941 bacterial genomes, including almost 600 prophages with no known homology to any proteins. Transfer RNA genes were abundant in the prophages, many of which alleviate the limits of translation efficiency due to host codon bias and presumably enable phages to surpass the normal capacity of the hosts translation machinery. We identified integrase genes in 15,765 prophages (43% of the prophages). The integrase was routinely located at either end of the integrated phage genome, and was used to orient and align prophage genomes to reveal their underlying organization. The conserved genome alignments of phages recapitulate early, middle, and late gene order in transcriptional control of phage genes, and demonstrate that gene order, presumably selected by transcription timing and/or coordination among functional modules has been stably conserved throughout phage evolution.

biochemistry↗