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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…

Overview of attention for article published in Frontiers in Microbiology, May 2018
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  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

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114 Mendeley
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Title
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
Published in
Frontiers in Microbiology, May 2018
DOI 10.3389/fmicb.2018.00771
Pubmed ID
Authors

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

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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 49 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 114 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 114 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 15%
Student > Ph. D. Student 16 14%
Student > Bachelor 15 13%
Student > Master 12 11%
Student > Doctoral Student 11 10%
Other 16 14%
Unknown 27 24%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 31 27%
Agricultural and Biological Sciences 26 23%
Immunology and Microbiology 9 8%
Environmental Science 6 5%
Earth and Planetary Sciences 2 2%
Other 7 6%
Unknown 33 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 34. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 31 May 2019.
All research outputs
#1,060,085
of 23,577,761 outputs
Outputs from Frontiers in Microbiology
#568
of 26,068 outputs
Outputs of similar age
#24,969
of 327,484 outputs
Outputs of similar age from Frontiers in Microbiology
#21
of 604 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 26,068 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.4. This one has done particularly well, scoring higher than 97% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 327,484 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 604 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 96% of its contemporaries.