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MetaFast: fast reference-free graph-based comparison of shotgun metagenomic data

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

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#13 of 7,651)
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Mentioned by

9 news outlets
2 blogs
56 tweeters
1 Facebook page


9 Dimensions

Readers on

72 Mendeley
6 CiteULike
MetaFast: fast reference-free graph-based comparison of shotgun metagenomic data
Published in
Bioinformatics, June 2016
DOI 10.1093/bioinformatics/btw312
Pubmed ID

Vladimir I. Ulyantsev, Sergey V. Kazakov, Veronika B. Dubinkina, Alexander V. Tyakht, Dmitry G. Alexeev, Ulyantsev, Vladimir I, Kazakov, Sergey V, Dubinkina, Veronika B, Tyakht, Alexander V, Alexeev, Dmitry G


High-throughput metagenomic sequencing has revolutionized our view on the structure and metabolic potential of microbial communities. However, analysis of metagenomic composition is often complicated by the high complexity of the community and the lack of related reference genomic sequences. As a start point for comparative metagenomic analysis, the researchers require efficient means for assessing pairwise similarity of the metagenomes (beta-diversity). A number of approaches is used to address this task, however, most of them have inherent disadvantages that limit their scope of applicability. For instance, the reference-based methods poorly perform on metagenomes from previously unstudied niches, while composition-based methods appear to be too abstract for straightforward interpretation and do not allow to identify the differentially abundant features. We developed MetaFast, an approach that allows to represent a shotgun metagenome from an arbitrary environment as a modified de Bruijn graph consisting of simplified components. For multiple metagenomes, the resulting representation is used to obtain a pairwise similarity matrix. The dimensional structure of the metagenomic components preserved in our algorithm reflects the inherent subspecies-level diversity of microbiota. The method is computationally efficient and especially promising for an analysis of metagenomes from novel environmental niches. Source code and binaries are freely available for download at https://github.com/ctlab/metafast The code is written in Java and is platform independent (tested on Linux and Windows x86_64). VIU: ulyantsev@rain.ifmo.ru, SVK: svkazakov@rain.ifmo.ru, VBD: dubinkina@phystech.edu, AVT: at@niifhm.ru, DGA: exappeal@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Twitter Demographics

The data shown below were collected from the profiles of 56 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 72 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 4%
France 2 3%
Spain 1 1%
Estonia 1 1%
Germany 1 1%
Switzerland 1 1%
Mexico 1 1%
United Kingdom 1 1%
Denmark 1 1%
Other 1 1%
Unknown 59 82%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 29%
Researcher 20 28%
Student > Master 9 13%
Other 6 8%
Professor > Associate Professor 6 8%
Other 10 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 32 44%
Biochemistry, Genetics and Molecular Biology 14 19%
Computer Science 14 19%
Environmental Science 3 4%
Immunology and Microbiology 3 4%
Other 6 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 111. 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 03 August 2017.
All research outputs
of 11,563,317 outputs
Outputs from Bioinformatics
of 7,651 outputs
Outputs of similar age
of 276,849 outputs
Outputs of similar age from Bioinformatics
of 238 outputs
Altmetric has tracked 11,563,317 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,651 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.7. This one has done particularly well, scoring higher than 99% 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 276,849 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 238 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 98% of its contemporaries.