OrthoPhyl - A turn-key solution for large scale whole genome bacterial phylogenomics
There are a staggering number of publicly available bacterial genome sequences (at writing, 2.0 million assemblies in NCBIs 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. Here we present OrthoPhyl, which takes bacterial genome assemblies and reconstructs trees from whole genome codon alignments. The analysis pipeline 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 infer orthologs in the full dataset. 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 OrthoPhyl to trees generated with kSNP3 and GToTree along with published trees using 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 at https://github.com/eamiddlebrook/OrthoPhyl/blob/OrthoPhyl_1.0/. Installation and execution instructions are provided in the associated github README.md file. Third party software versions and OrthoPhyl execution files will remain static in the OrthoPhyl_1.0 branch, with the main branch housing current development. For versions of software dependencies see Supplemental Table 1. To aid in usability, a Singularity container is available at https://cloud.sylabs.io/library/earlyevol/default/orthophyl or with the command singularity pull library://earlyevol/default/orthophyl:1.0_ms. Also see the GitHub page for Singularity usage guide. Assemblies used within this manuscript are available from https://www.ncbi.nlm.nih.gov/assembly/ with accessions in Supplemental tables 2-4.