Original Research ARTICLE
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
- 1Universidad Nacional Autónoma de México, Mexico
- 2Estación Experimental de Aula Dei (CSIC), Spain
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 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 of 95.9% that are perfectly consistent with strongly supported clades on the core- and pan-genome trees. In addition, we identified 14 misclassified RefSeq genome sequences, 12 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.
Keywords: Stenotrophomonas, phylogenomics, Molecular Systematics, Microbial Genomics, Pan-genomics, Molecular markers, open-source software, maximum-likelihood methods, unsupervised learning, species delimitation, Microbial Taxonomy, Microbial Diversity, statistics, Bioinformatics tools, genome analysis
Received: 16 Jan 2018;
Accepted: 05 Apr 2018.
Edited by:Jesus L. Romalde, Universidade de Santiago de Compostela, Spain
Reviewed by:Anne-Kristin Kaster, Karlsruhe Institute of Technology
Francisco J. Roig, Universitat de València, Spain
Copyright: © 2018 Vinuesa, Ochoa and Contreras-Moreira. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Dr. Pablo Vinuesa, Universidad Nacional Autónoma de México, Ciudad de Mexico, Mexico, firstname.lastname@example.org