Machine-Learning Classification Suggests That Many Alphaproteobacterial Prophages May Instead Be Gene Transfer Agents

Kogay, Roman; Neely, Taylor B.; Birnbaum, Daniel P.; Hankel, Camille R.; Shakya, Migun; Zhaxybayeva, Olga; Delaye, Luis

doi:10.1093/gbe/evz206

Title: Machine-Learning Classification Suggests That Many Alphaproteobacterial Prophages May Instead Be Gene Transfer Agents

Journal Article · 26 September 2019 · Genome Biology and Evolution

DOI: https://doi.org/10.1093/gbe/evz206 · OSTI ID:1625346

Kogay, Roman ^[1]; Neely, Taylor B. ^[2]; Birnbaum, Daniel P. ^[3]; Hankel, Camille R. ^[4]; Shakya, Migun ^[5];

^[6]; Delaye, Luis

Dartmouth College, Hanover, NH (United States). Dept. of Biological Sciences; DOE/OSTI
Dartmouth College, Hanover, NH (United States). Dept. of Biological Sciences; Amazon.com Inc., Seattle, WA (United States)
Dartmouth College, Hanover, NH (United States). Dept. of Biological Sciences; Harvard University, Cambridge, MA (United States). School of Engineering and Applied Sciences
Dartmouth College, Hanover, NH (United States). Dept. of Biological Sciences; Harvard Univ., Cambridge, MA (United States). Dept. of Earth and Planetary Sciences
Dartmouth College, Hanover, NH (United States). Dept. of Biological Sciences; Los Alamos National Lab. (LANL), Los Alamos, NM (United States). Bioscience Div.
Dartmouth College, Hanover, NH (United States). Dept. of Biological Sciences; Dartmouth College, Hanover, NH (United States). Dept. of Computer Science

Abstract Many of the sequenced bacterial and archaeal genomes encode regions of viral provenance. Yet, not all of these regions encode bona fide viruses. Gene transfer agents (GTAs) are thought to be former viruses that are now maintained in genomes of some bacteria and archaea and are hypothesized to enable exchange of DNA within bacterial populations. In Alphaproteobacteria, genes homologous to the “head–tail” gene cluster that encodes structural components of the Rhodobacter capsulatus GTA (RcGTA) are found in many taxa, even if they are only distantly related to Rhodobacter capsulatus. Yet, in most genomes available in GenBank RcGTA-like genes have annotations of typical viral proteins, and therefore are not easily distinguished from their viral homologs without additional analyses. Here, we report a “support vector machine” classifier that quickly and accurately distinguishes RcGTA-like genes from their viral homologs by capturing the differences in the amino acid composition of the encoded proteins. Our open-source classifier is implemented in Python and can be used to scan homologs of the RcGTA genes in newly sequenced genomes. The classifier can also be trained to identify other types of GTAs, or even to detect other elements of viral ancestry. Using the classifier trained on a manually curated set of homologous viruses and GTAs, we detected RcGTA-like “head–tail” gene clusters in 57.5% of the 1,423 examined alphaproteobacterial genomes. We also demonstrated that more than half of the in silico prophage predictions are instead likely to be GTAs, suggesting that in many alphaproteobacterial genomes the RcGTA-like elements remain unrecognized.

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Research Organization:: Los Alamos National Lab (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: Dartmouth Dean of Faculty; Dartmouth James O. Freedman Presidential Scholarship; National Science Foundation (NSF); Simons Foundation Investigator in Mathematical Modeling of Living Systems; USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC52-06NA25396

OSTI ID:: 1625346

Journal Information:: Genome Biology and Evolution, Journal Name: Genome Biology and Evolution Journal Issue: 10 Vol. 11; ISSN 1759-6653

Publisher:: Society for Molecular Biology and EvolutionCopyright Statement

Country of Publication:: United States

Language:: English

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