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CoMet: a workflow using contig coverage and composition for binning a metagenomic sample with high precision

Overview of attention for article published in BMC Bioinformatics, December 2017
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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 (83rd percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

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22 tweeters

Citations

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10 Dimensions

Readers on

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62 Mendeley
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Title
CoMet: a workflow using contig coverage and composition for binning a metagenomic sample with high precision
Published in
BMC Bioinformatics, December 2017
DOI 10.1186/s12859-017-1967-3
Pubmed ID
Authors

Damayanthi Herath, Sen-Lin Tang, Kshitij Tandon, David Ackland, Saman Kumara Halgamuge

Abstract

In metagenomics, the separation of nucleotide sequences belonging to an individual or closely matched populations is termed binning. Binning helps the evaluation of underlying microbial population structure as well as the recovery of individual genomes from a sample of uncultivable microbial organisms. Both supervised and unsupervised learning methods have been employed in binning; however, characterizing a metagenomic sample containing multiple strains remains a significant challenge. In this study, we designed and implemented a new workflow, Coverage and composition based binning of Metagenomes (CoMet), for binning contigs in a single metagenomic sample. CoMet utilizes coverage values and the compositional features of metagenomic contigs. The binning strategy in CoMet includes the initial grouping of contigs in guanine-cytosine (GC) content-coverage space and refinement of bins in tetranucleotide frequencies space in a purely unsupervised manner. With CoMet, the clustering algorithm DBSCAN is employed for binning contigs. The performances of CoMet were compared against four existing approaches for binning a single metagenomic sample, including MaxBin, Metawatt, MyCC (default) and MyCC (coverage) using multiple datasets including a sample comprised of multiple strains. Binning methods based on both compositional features and coverages of contigs had higher performances than the method which is based only on compositional features of contigs. CoMet yielded higher or comparable precision in comparison to the existing binning methods on benchmark datasets of varying complexities. MyCC (coverage) had the highest ranking score in F1-score. However, the performances of CoMet were higher than MyCC (coverage) on the dataset containing multiple strains. Furthermore, CoMet recovered contigs of more species and was 18 - 39% higher in precision than the compared existing methods in discriminating species from the sample of multiple strains. CoMet resulted in higher precision than MyCC (default) and MyCC (coverage) on a real metagenome. The approach proposed with CoMet for binning contigs, improves the precision of binning while characterizing more species in a single metagenomic sample and in a sample containing multiple strains. The F1-scores obtained from different binning strategies vary with different datasets; however, CoMet yields the highest F1-score with a sample comprised of multiple strains.

Twitter Demographics

The data shown below were collected from the profiles of 22 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 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 62 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 24%
Researcher 14 23%
Student > Bachelor 8 13%
Student > Master 5 8%
Student > Doctoral Student 3 5%
Other 12 19%
Unknown 5 8%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 15 24%
Agricultural and Biological Sciences 14 23%
Computer Science 7 11%
Engineering 4 6%
Environmental Science 3 5%
Other 11 18%
Unknown 8 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 04 January 2018.
All research outputs
#2,494,447
of 20,111,379 outputs
Outputs from BMC Bioinformatics
#926
of 6,731 outputs
Outputs of similar age
#71,997
of 432,305 outputs
Outputs of similar age from BMC Bioinformatics
#55
of 445 outputs
Altmetric has tracked 20,111,379 research outputs across all sources so far. Compared to these this one has done well and is in the 87th 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 86% 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 432,305 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 83% of its contemporaries.
We're also able to compare this research output to 445 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.