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Assessment of k-mer spectrum applicability for metagenomic dissimilarity analysis

Overview of attention for article published in BMC Bioinformatics, January 2016
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  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#17 of 7,705)
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

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21 X users
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1 patent
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4 Wikipedia pages
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3 Google+ users

Citations

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58 Dimensions

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170 Mendeley
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Title
Assessment of k-mer spectrum applicability for metagenomic dissimilarity analysis
Published in
BMC Bioinformatics, January 2016
DOI 10.1186/s12859-015-0875-7
Pubmed ID
Authors

Veronika B. Dubinkina, Dmitry S. Ischenko, Vladimir I. Ulyantsev, Alexander V. Tyakht, Dmitry G. Alexeev

Abstract

A rapidly increasing flow of genomic data requires the development of efficient methods for obtaining its compact representation. Feature extraction facilitates classification, clustering and model analysis for testing and refining biological hypotheses. "Shotgun" metagenome is an analytically challenging type of genomic data - containing sequences of all genes from the totality of a complex microbial community. Recently, researchers started to analyze metagenomes using reference-free methods based on the analysis of oligonucleotides (k-mers) frequency spectrum previously applied to isolated genomes. However, little is known about their correlation with the existing approaches for metagenomic feature extraction, as well as the limits of applicability. Here we evaluated a metagenomic pairwise dissimilarity measure based on short k-mer spectrum using the example of human gut microbiota, a biomedically significant object of study. We developed a method for calculating pairwise dissimilarity (beta-diversity) of "shotgun" metagenomes based on short k-mer spectra (5≤k≤11). The method was validated on simulated metagenomes and further applied to a large collection of human gut metagenomes from the populations of the world (n=281). The k-mer spectrum-based measure was found to behave similarly to one based on mapping to a reference gene catalog, but different from one using a genome catalog. This difference turned out to be associated with a significant presence of viral reads in a number of metagenomes. Simulations showed limited impact of bacterial genetic variability as well as sequencing errors on k-mer spectra. Specific differences between the datasets from individual populations were identified. Our approach allows rapid estimation of pairwise dissimilarity between metagenomes. Though we applied this technique to gut microbiota, it should be useful for arbitrary metagenomes, even metagenomes with novel microbiota. Dissimilarity measure based on k-mer spectrum provides a wider perspective in comparison with the ones based on the alignment against reference sequence sets. It helps not to miss possible outstanding features of metagenomic composition, particularly related to the presence of an unknown bacteria, virus or eukaryote, as well as to technical artifacts (sample contamination, reads of non-biological origin, etc.) at the early stages of bioinformatic analysis. Our method is complementary to reference-based approaches and can be easily integrated into metagenomic analysis pipelines.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 170 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 2%
Brazil 2 1%
France 1 <1%
Sweden 1 <1%
Germany 1 <1%
Canada 1 <1%
United Kingdom 1 <1%
Japan 1 <1%
Spain 1 <1%
Other 0 0%
Unknown 157 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 42 25%
Researcher 40 24%
Student > Master 25 15%
Student > Bachelor 15 9%
Other 10 6%
Other 21 12%
Unknown 17 10%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 56 33%
Agricultural and Biological Sciences 54 32%
Computer Science 19 11%
Medicine and Dentistry 5 3%
Immunology and Microbiology 3 2%
Other 14 8%
Unknown 19 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 87. 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 08 April 2022.
All research outputs
#490,181
of 25,452,734 outputs
Outputs from BMC Bioinformatics
#17
of 7,705 outputs
Outputs of similar age
#8,658
of 400,647 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 141 outputs
Altmetric has tracked 25,452,734 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 7,705 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. 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 400,647 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 141 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 99% of its contemporaries.