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A scalable assembly-free variable selection algorithm for biomarker discovery from metagenomes

Overview of attention for article published in BMC Bioinformatics, August 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

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18 tweeters
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1 Google+ user

Citations

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41 Mendeley
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Title
A scalable assembly-free variable selection algorithm for biomarker discovery from metagenomes
Published in
BMC Bioinformatics, August 2016
DOI 10.1186/s12859-016-1186-3
Pubmed ID
Authors

Anestis Gkanogiannis, Stéphane Gazut, Marcel Salanoubat, Sawsan Kanj, Thomas Brüls

Abstract

Metagenomics holds great promises for deepening our knowledge of key bacterial driven processes, but metagenome assembly remains problematic, typically resulting in representation biases and discarding significant amounts of non-redundant sequence information. In order to alleviate constraints assembly can impose on downstream analyses, and/or to increase the fraction of raw reads assembled via targeted assemblies relying on pre-assembly binning steps, we developed a set of binning modules and evaluated their combination in a new "assembly-free" binning protocol. We describe a scalable multi-tiered binning algorithm that combines frequency and compositional features to cluster unassembled reads, and demonstrate i) significant runtime performance gains of the developed modules against state of the art software, obtained through parallelization and the efficient use of large lock-free concurrent hash maps, ii) its relevance for clustering unassembled reads from high complexity (e.g., harboring 700 distinct genomes) samples, iii) its relevance to experimental setups involving multiple samples, through a use case consisting in the "de novo" identification of sequences from a target genome (e.g., a pathogenic strain) segregating at low levels in a cohort of 50 complex microbiomes (harboring 100 distinct genomes each), in the background of closely related strains and the absence of reference genomes, iv) its ability to correctly identify clusters of sequences from the E. coli O104:H4 genome as the most strongly correlated to the infection status in 53 microbiomes sampled from the 2011 STEC outbreak in Germany, and to accurately cluster contigs of this pathogenic strain from a cross-assembly of these 53 microbiomes. We present a set of sequence clustering ("binning") modules and their application to biomarker (e.g., genomes of pathogenic organisms) discovery from large synthetic and real metagenomics datasets. Initially designed for the "assembly-free" analysis of individual metagenomic samples, we demonstrate their extension to setups involving multiple samples via the usage of the "alignment-free" d2S statistic to relate clusters across samples, and illustrate how the clustering modules can otherwise be leveraged for de novo "pre-assembly" tasks by segregating sequences into biologically meaningful partitions.

Twitter Demographics

The data shown below were collected from the profiles of 18 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 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 5%
Germany 1 2%
Canada 1 2%
Estonia 1 2%
Spain 1 2%
United States 1 2%
Unknown 34 83%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 34%
Student > Ph. D. Student 8 20%
Student > Master 5 12%
Student > Postgraduate 3 7%
Student > Bachelor 2 5%
Other 4 10%
Unknown 5 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 39%
Biochemistry, Genetics and Molecular Biology 8 20%
Computer Science 4 10%
Arts and Humanities 1 2%
Environmental Science 1 2%
Other 6 15%
Unknown 5 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 26 August 2016.
All research outputs
#2,820,719
of 20,114,180 outputs
Outputs from BMC Bioinformatics
#1,113
of 6,731 outputs
Outputs of similar age
#51,546
of 284,157 outputs
Outputs of similar age from BMC Bioinformatics
#2
of 28 outputs
Altmetric has tracked 20,114,180 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,731 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one has done well, scoring higher than 83% 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 284,157 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 81% of its contemporaries.
We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 96% of its contemporaries.