OrthoPhyl – A turn-key solution for large scale whole genome bacterial phylogenomics

Abstract

AbstractMotivationThere are a staggering number of publicly available bacterial genome sequences (at writing, 1.5 million assemblies in NCBI’s GenBank alone), and the deposition rate continues to increase. This wealth of data begs for phylogenetic analyses to place these sequences within an evolutionary context. A phylogenetic placement not only aids in taxonomic classification, but informs the evolution of novel phenotypes, targets of selection, and horizontal gene transfer. Building trees from multi-gene codon alignments is a laborious task that requires bioinformatic expertise, rigorous curation of orthologs, and heavy computation. Compounding the problem is the lack of tools that can streamline these processes for building trees from large scale genomic data.ResultsHere we present OrthoPhyl, which takes as input bacterial genome assemblies and reconstructs trees from whole genome codon alignments. The workflow can analyze an arbitrarily large number of input genomes (>1200 tested here) by identifying a diversity spanning subset of assemblies and using these genomes to build gene models to identify orthologs in the full dataset. OrthoPhyl is an easy-to-run, turn-key solution for generating high-resolution bacterial trees from datasets with a wide range of divergences. To illustrate the versatility of OrthoPhyl, we show three use-cases: E. coli/Shigella, Brucella/Ochrobactrum, and the order Rickettsiales. We compare trees generated with our software to published trees from alternative methods. We show that OrthoPhyl trees are consistent with other methods while incorporating more data, allowing for greater numbers of input genomes, and more flexibility of analysis.Availability and ImplementationCode used in this manuscript is available athttps://github.com/eamiddlebrook/OrthoPhyl.git. To aid in usability, a Singularity container is available athttps://cloud.sylabs.io/library/earlyevol/default/orthophyl. Installation and execution instructions are provided in the associated github readme file. Assemblies used with this manuscript are available fromhttps://www.ncbi.nlm.nih.gov/assembly/with accessions inSupplementary table 1-3.ContactEarl A. Middlebrook –earlm@lanl.gov