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
Vinuesa P, Ochoa L, Contreras-Moreira B (2018) 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. Frontiers in Microbiology
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.
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.