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Metagenomic Data Utilization and Analysis (MEDUSA) and Construction of a Global Gut Microbial Gene Catalogue

Overview of attention for article published in PLoS Computational Biology, July 2014
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Title
Metagenomic Data Utilization and Analysis (MEDUSA) and Construction of a Global Gut Microbial Gene Catalogue
Published in
PLoS Computational Biology, July 2014
DOI 10.1371/journal.pcbi.1003706
Pubmed ID
Authors

Fredrik H. Karlsson, Intawat Nookaew, Jens Nielsen

Abstract

Metagenomic sequencing has contributed important new knowledge about the microbes that live in a symbiotic relationship with humans. With modern sequencing technology it is possible to generate large numbers of sequencing reads from a metagenome but analysis of the data is challenging. Here we present the bioinformatics pipeline MEDUSA that facilitates analysis of metagenomic reads at the gene and taxonomic level. We also constructed a global human gut microbial gene catalogue by combining data from 4 studies spanning 3 continents. Using MEDUSA we mapped 782 gut metagenomes to the global gene catalogue and a catalogue of sequenced microbial species. Hereby we find that all studies share about half a million genes and that on average 300,000 genes are shared by half the studied subjects. The gene richness is higher in the European studies compared to Chinese and American and this is also reflected in the species richness. Even though it is possible to identify common species and a core set of genes, we find that there are large variations in abundance of species and genes.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 197 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 4%
Belgium 3 2%
Germany 2 1%
Australia 1 <1%
Brazil 1 <1%
Switzerland 1 <1%
Hungary 1 <1%
Sweden 1 <1%
Japan 1 <1%
Other 1 <1%
Unknown 178 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 51 26%
Student > Ph. D. Student 47 24%
Student > Master 23 12%
Student > Bachelor 16 8%
Student > Doctoral Student 14 7%
Other 23 12%
Unknown 23 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 80 41%
Biochemistry, Genetics and Molecular Biology 36 18%
Medicine and Dentistry 9 5%
Engineering 7 4%
Computer Science 6 3%
Other 31 16%
Unknown 28 14%