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Functional analysis of metagenomes and metatranscriptomes using SEED and KEGG

Overview of attention for article published in BMC Bioinformatics, February 2011
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6 Wikipedia pages

Citations

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122 Dimensions

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531 Mendeley
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6 CiteULike
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Title
Functional analysis of metagenomes and metatranscriptomes using SEED and KEGG
Published in
BMC Bioinformatics, February 2011
DOI 10.1186/1471-2105-12-s1-s21
Pubmed ID
Authors

Suparna Mitra, Paul Rupek, Daniel C Richter, Tim Urich, Jack A Gilbert, Folker Meyer, Andreas Wilke, Daniel H Huson

Abstract

Metagenomics is the study of microbial organisms using sequencing applied directly to environmental samples. Technological advances in next-generation sequencing methods are fueling a rapid increase in the number and scope of metagenome projects. While metagenomics provides information on the gene content, metatranscriptomics aims at understanding gene expression patterns in microbial communities. The initial computational analysis of a metagenome or metatranscriptome addresses three questions: (1) Who is out there? (2) What are they doing? and (3) How do different datasets compare? There is a need for new computational tools to answer these questions. In 2007, the program MEGAN (MEtaGenome ANalyzer) was released, as a standalone interactive tool for analyzing the taxonomic content of a single metagenome dataset. The program has subsequently been extended to support comparative analyses of multiple datasets.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 531 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 17 3%
Brazil 8 2%
Germany 4 <1%
Canada 4 <1%
France 3 <1%
Mexico 3 <1%
Belgium 3 <1%
India 3 <1%
Chile 2 <1%
Other 18 3%
Unknown 466 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 142 27%
Researcher 112 21%
Student > Master 71 13%
Student > Bachelor 36 7%
Student > Doctoral Student 28 5%
Other 82 15%
Unknown 60 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 311 59%
Biochemistry, Genetics and Molecular Biology 48 9%
Environmental Science 32 6%
Computer Science 21 4%
Immunology and Microbiology 12 2%
Other 36 7%
Unknown 71 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 21 March 2020.
All research outputs
#7,453,126
of 22,785,242 outputs
Outputs from BMC Bioinformatics
#3,023
of 7,279 outputs
Outputs of similar age
#60,683
of 198,238 outputs
Outputs of similar age from BMC Bioinformatics
#18
of 39 outputs
Altmetric has tracked 22,785,242 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,279 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 50% 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 198,238 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 39 others from the same source and published within six weeks on either side of this one. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.