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FMAP: Functional Mapping and Analysis Pipeline for metagenomics and metatranscriptomics studies

Overview of attention for article published in BMC Bioinformatics, October 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (85th percentile)
  • High Attention Score compared to outputs of the same age and source (89th percentile)

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1 blog
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10 X users
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1 Facebook page

Citations

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96 Dimensions

Readers on

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271 Mendeley
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Title
FMAP: Functional Mapping and Analysis Pipeline for metagenomics and metatranscriptomics studies
Published in
BMC Bioinformatics, October 2016
DOI 10.1186/s12859-016-1278-0
Pubmed ID
Authors

Jiwoong Kim, Min Soo Kim, Andrew Y. Koh, Yang Xie, Xiaowei Zhan

Abstract

Given the lack of a complete and comprehensive library of microbial reference genomes, determining the functional profile of diverse microbial communities is challenging. The available functional analysis pipelines lack several key features: (i) an integrated alignment tool, (ii) operon-level analysis, and (iii) the ability to process large datasets. Here we introduce our open-sourced, stand-alone functional analysis pipeline for analyzing whole metagenomic and metatranscriptomic sequencing data, FMAP (Functional Mapping and Analysis Pipeline). FMAP performs alignment, gene family abundance calculations, and statistical analysis (three levels of analyses are provided: differentially-abundant genes, operons and pathways). The resulting output can be easily visualized with heatmaps and functional pathway diagrams. FMAP functional predictions are consistent with currently available functional analysis pipelines. FMAP is a comprehensive tool for providing functional analysis of metagenomic/metatranscriptomic sequencing data. With the added features of integrated alignment, operon-level analysis, and the ability to process large datasets, FMAP will be a valuable addition to the currently available functional analysis toolbox. We believe that this software will be of great value to the wider biology and bioinformatics communities.

X Demographics

X Demographics

The data shown below were collected from the profiles of 10 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 271 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 1%
Brazil 3 1%
South Africa 1 <1%
Norway 1 <1%
Canada 1 <1%
United Kingdom 1 <1%
Spain 1 <1%
Belgium 1 <1%
Unknown 258 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 61 23%
Researcher 56 21%
Student > Master 40 15%
Student > Bachelor 21 8%
Student > Doctoral Student 18 7%
Other 38 14%
Unknown 37 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 80 30%
Biochemistry, Genetics and Molecular Biology 73 27%
Computer Science 20 7%
Environmental Science 16 6%
Immunology and Microbiology 6 2%
Other 30 11%
Unknown 46 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 15 June 2017.
All research outputs
#2,874,471
of 24,885,505 outputs
Outputs from BMC Bioinformatics
#836
of 7,601 outputs
Outputs of similar age
#47,044
of 326,945 outputs
Outputs of similar age from BMC Bioinformatics
#14
of 129 outputs
Altmetric has tracked 24,885,505 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,601 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done well, scoring higher than 88% 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 326,945 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 85% of its contemporaries.
We're also able to compare this research output to 129 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.