GET_PHYLOMARKERS, a Software Package to Select Optimal Orthologous Clusters for Phylogenomics and Inferring Pan-Genome Phylogenies, Used for a Critical Geno-Taxonomic Revision of the Genus Stenotrophomonas.

Luz E Ochoa-Sánchez,Pablo Vinuesa,Bruno Contreras-Moreira

doi:10.3389/fmicb.2018.00771

Abstract

The massive accumulation of genome-sequences in public databases promoted the proliferation of genome-level phylogenetic analyses in many areas of biological research. However, due to diverse evolutionary and genetic processes, many loci have undesirable properties for phylogenetic reconstruction. These, if undetected, can result in erroneous or biased estimates, particularly when estimating species trees from concatenated datasets. To deal with these problems, we developed GET_PHYLOMARKERS, a pipeline designed to identify high-quality markers to estimate robust genome phylogenies from the orthologous clusters, or the pan-genome matrix (PGM), computed by GET_HOMOLOGUES. In the first context, a set of sequential filters are applied to exclude recombinant alignments and those producing anomalous or poorly resolved trees. Multiple sequence alignments and maximum likelihood (ML) phylogenies are computed in parallel on multi-core computers. A ML species tree is estimated from the concatenated set of top-ranking alignments at the DNA or protein levels, using either FastTree or IQ-TREE (IQT). The latter is used by default due to its superior performance revealed in an extensive benchmark analysis. In addition, parsimony and ML phylogenies can be estimated from the PGM. We demonstrate the practical utility of the software by analyzing 170 Stenotrophomonas genome sequences available in RefSeq and 10 new complete genomes of Mexican environmental S. maltophilia complex (Smc) isolates reported herein. A combination of core-genome and PGM analyses was used to revise the molecular systematics of the genus. An unsupervised learning approach that uses a goodness of clustering statistic identified 20 groups within the Smc at a core-genome average nucleotide identity (cgANIb) of 95.9% that are perfectly consistent with strongly supported clades on the core- and pan-genome trees. In addition, we identified 16 misclassified RefSeq genome sequences, 14 of them labeled as S. maltophilia, demonstrating the broad utility of the software for phylogenomics and geno-taxonomic studies. The code, a detailed manual and tutorials are freely available for Linux/UNIX servers under the GNU GPLv3 license at https://github.com/vinuesa/get_phylomarkers. A docker image bundling GET_PHYLOMARKERS with GET_HOMOLOGUES is available at https://hub.docker.com/r/csicunam/get_homologues/, which can be easily run on any platform.

Highlights

Accurate phylogenies represent key models of descent in modern biological research
Current geno-taxonomic practice is largely based on the estimation ofgenome phylogenies (Daubin et al, 2002; Lerat et al, 2003; Tettelin et al, 2005; Ciccarelli et al, 2006; Wu and Eisen, 2008) and the computation of diverse overall genome relatedness indices (OGRIs) (Chun and Rainey, 2014), such as the popular genomic average nucleotide identity values (Konstantinidis and Tiedje, 2005; Goris et al, 2007; Richter and Rosselló-Móra, 2009)
In this study we developed and benchmarked GET_PHYLOMARKERS, an open-source, comprehensive, and easy-to-use software package for phylogenomics and microbial genome taxonomy

Summary

Introduction

Accurate phylogenies represent key models of descent in modern biological research. They are applied to the study of a broad spectrum of evolutionary topics, ranging from the analysis of populations up to the ecology of communities (Dornburg et al, 2017). Current geno-taxonomic practice is largely based on the estimation of (core-)genome phylogenies (Daubin et al, 2002; Lerat et al, 2003; Tettelin et al, 2005; Ciccarelli et al, 2006; Wu and Eisen, 2008) and the computation of diverse overall genome relatedness indices (OGRIs) (Chun and Rainey, 2014), such as the popular genomic average nucleotide identity (gANI) values (Konstantinidis and Tiedje, 2005; Goris et al, 2007; Richter and Rosselló-Móra, 2009) These indices are rapidly and effectively replacing the traditional DNA-DNA hybridization values used for species delimitation in the pre-genomic era (Stackebrandt and Goebel, 1994; Vandamme et al, 1996; Stackebrandt et al, 2002). Robust phylogenomic inference requires the selection of well-suited markers for the task (Vinuesa, 2010)

Methods

Results

Conclusion