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DESMAN: a new tool for de novo extraction of strains from metagenomes

Overview of attention for article published in Genome Biology (Online Edition), September 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (97th percentile)

Mentioned by

blogs
2 blogs
twitter
180 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
103 Dimensions

Readers on

mendeley
330 Mendeley
citeulike
3 CiteULike
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Title
DESMAN: a new tool for de novo extraction of strains from metagenomes
Published in
Genome Biology (Online Edition), September 2017
DOI 10.1186/s13059-017-1309-9
Pubmed ID
Authors

Christopher Quince, Tom O. Delmont, Sébastien Raguideau, Johannes Alneberg, Aaron E. Darling, Gavin Collins, A. Murat Eren

Abstract

We introduce DESMAN for De novo Extraction of Strains from Metagenomes. Large multi-sample metagenomes are being generated but strain variation results in fragmentary co-assemblies. Current algorithms can bin contigs into metagenome-assembled genomes but are unable to resolve strain-level variation. DESMAN identifies variants in core genes and uses co-occurrence across samples to link variants into haplotypes and abundance profiles. These are then searched for against non-core genes to determine the accessory genome of each strain. We validated DESMAN on a complex 50-species 210-genome 96-sample synthetic mock data set and then applied it to the Tara Oceans microbiome.

Twitter Demographics

The data shown below were collected from the profiles of 180 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 330 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 330 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 88 27%
Researcher 69 21%
Student > Master 42 13%
Student > Bachelor 17 5%
Student > Doctoral Student 16 5%
Other 48 15%
Unknown 50 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 115 35%
Biochemistry, Genetics and Molecular Biology 61 18%
Computer Science 28 8%
Immunology and Microbiology 22 7%
Environmental Science 13 4%
Other 28 8%
Unknown 63 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 106. 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 11 January 2021.
All research outputs
#264,859
of 19,504,205 outputs
Outputs from Genome Biology (Online Edition)
#170
of 3,841 outputs
Outputs of similar age
#7,179
of 289,587 outputs
Outputs of similar age from Genome Biology (Online Edition)
#1
of 1 outputs
Altmetric has tracked 19,504,205 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,841 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.9. This one has done particularly well, scoring higher than 95% 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 289,587 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% 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