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RIEMS: a software pipeline for sensitive and comprehensive taxonomic classification of reads from metagenomics datasets

Overview of attention for article published in BMC Bioinformatics, March 2015
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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 (89th percentile)

Mentioned by

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35 tweeters

Citations

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27 Dimensions

Readers on

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152 Mendeley
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1 CiteULike
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Title
RIEMS: a software pipeline for sensitive and comprehensive taxonomic classification of reads from metagenomics datasets
Published in
BMC Bioinformatics, March 2015
DOI 10.1186/s12859-015-0503-6
Pubmed ID
Authors

Matthias Scheuch, Dirk Höper, Martin Beer

Abstract

Fuelled by the advent and subsequent development of next generation sequencing technologies, metagenomics became a powerful tool for the analysis of microbial communities both scientifically and diagnostically. The biggest challenge is the extraction of relevant information from the huge sequence datasets generated for metagenomics studies. Although a plethora of tools are available, data analysis is still a bottleneck. To overcome the bottleneck of data analysis, we developed an automated computational workflow called RIEMS - Reliable Information Extraction from Metagenomic Sequence datasets. RIEMS assigns every individual read sequence within a dataset taxonomically by cascading different sequence analyses with decreasing stringency of the assignments using various software applications. After completion of the analyses, the results are summarised in a clearly structured result protocol organised taxonomically. The high accuracy and performance of RIEMS analyses were proven in comparison with other tools for metagenomics data analysis using simulated sequencing read datasets. RIEMS has the potential to fill the gap that still exists with regard to data analysis for metagenomics studies. The usefulness and power of RIEMS for the analysis of genuine sequencing datasets was demonstrated with an early version of RIEMS in 2011 when it was used to detect the orthobunyavirus sequences leading to the discovery of Schmallenberg virus.

Twitter Demographics

The data shown below were collected from the profiles of 35 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 152 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 3%
Brazil 3 2%
Germany 3 2%
France 2 1%
Estonia 2 1%
United Kingdom 1 <1%
Australia 1 <1%
Belgium 1 <1%
Sweden 1 <1%
Other 1 <1%
Unknown 133 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 42 28%
Student > Master 25 16%
Student > Ph. D. Student 25 16%
Student > Bachelor 14 9%
Unspecified 9 6%
Other 37 24%
Readers by discipline Count As %
Agricultural and Biological Sciences 68 45%
Biochemistry, Genetics and Molecular Biology 28 18%
Computer Science 18 12%
Unspecified 11 7%
Engineering 6 4%
Other 21 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 14. 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 28 September 2015.
All research outputs
#1,103,192
of 13,426,363 outputs
Outputs from BMC Bioinformatics
#373
of 4,990 outputs
Outputs of similar age
#22,894
of 215,110 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 1 outputs
Altmetric has tracked 13,426,363 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,990 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done particularly well, scoring higher than 93% 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 215,110 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 89% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them