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MetaGen: reference-free learning with multiple metagenomic samples

Overview of attention for article published in Genome Biology, October 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 (92nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (58th percentile)

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60 X users
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1 Facebook page

Citations

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11 Dimensions

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95 Mendeley
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Title
MetaGen: reference-free learning with multiple metagenomic samples
Published in
Genome Biology, October 2017
DOI 10.1186/s13059-017-1323-y
Pubmed ID
Authors

Xin Xing, Jun S. Liu, Wenxuan Zhong

Abstract

A major goal of metagenomics is to identify and study the entire collection of microbial species in a set of targeted samples. We describe a statistical metagenomic algorithm that simultaneously identifies microbial species and estimates their abundances without using reference genomes. As a trade-off, we require multiple metagenomic samples, usually ≥10 samples, to get highly accurate binning results. Compared to reference-free methods based primarily on k-mer distributions or coverage information, the proposed approach achieves a higher species binning accuracy and is particularly powerful when sequencing coverage is low. We demonstrated the performance of this new method through both simulation and real metagenomic studies. The MetaGen software is available at https://github.com/BioAlgs/MetaGen .

X Demographics

X Demographics

The data shown below were collected from the profiles of 60 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 95 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Ireland 1 1%
Unknown 94 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 28 29%
Researcher 21 22%
Student > Bachelor 10 11%
Student > Master 10 11%
Professor 6 6%
Other 12 13%
Unknown 8 8%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 29 31%
Agricultural and Biological Sciences 20 21%
Computer Science 15 16%
Immunology and Microbiology 5 5%
Mathematics 4 4%
Other 10 11%
Unknown 12 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 32. 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 19 October 2017.
All research outputs
#1,235,359
of 25,382,440 outputs
Outputs from Genome Biology
#935
of 4,468 outputs
Outputs of similar age
#25,163
of 331,602 outputs
Outputs of similar age from Genome Biology
#26
of 63 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,468 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has done well, scoring higher than 79% 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 331,602 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 92% of its contemporaries.
We're also able to compare this research output to 63 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 58% of its contemporaries.