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The PhyloPythiaS Web Server for Taxonomic Assignment of Metagenome Sequences

Overview of attention for article published in PLOS ONE, June 2012
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Title
The PhyloPythiaS Web Server for Taxonomic Assignment of Metagenome Sequences
Published in
PLOS ONE, June 2012
DOI 10.1371/journal.pone.0038581
Pubmed ID
Authors

Kaustubh Raosaheb Patil, Linus Roune, Alice Carolyn McHardy

Abstract

Metagenome sequencing is becoming common and there is an increasing need for easily accessible tools for data analysis. An essential step is the taxonomic classification of sequence fragments. We describe a web server for the taxonomic assignment of metagenome sequences with PhyloPythiaS. PhyloPythiaS is a fast and accurate sequence composition-based classifier that utilizes the hierarchical relationships between clades. Taxonomic assignments with the web server can be made with a generic model, or with sample-specific models that users can specify and create. Several interactive visualization modes and multiple download formats allow quick and convenient analysis and downstream processing of taxonomic assignments. Here, we demonstrate usage of our web server by taxonomic assignment of metagenome samples from an acidophilic biofilm community of an acid mine and of a microbial community from cow rumen.

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Mendeley readers

The data shown below were compiled from readership statistics for 186 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 8 4%
Brazil 4 2%
Germany 3 2%
United Kingdom 2 1%
Finland 1 <1%
Netherlands 1 <1%
Estonia 1 <1%
Denmark 1 <1%
Japan 1 <1%
Other 1 <1%
Unknown 163 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 51 27%
Student > Master 33 18%
Researcher 32 17%
Student > Doctoral Student 13 7%
Student > Bachelor 12 6%
Other 29 16%
Unknown 16 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 97 52%
Biochemistry, Genetics and Molecular Biology 22 12%
Computer Science 17 9%
Environmental Science 8 4%
Immunology and Microbiology 5 3%
Other 18 10%
Unknown 19 10%