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Assessment of k-mer spectrum applicability for metagenomic dissimilarity analysis

BACKGROUND: 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 analyt...

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Autores principales: Dubinkina, Veronika B., Ischenko, Dmitry S., Ulyantsev, Vladimir I., Tyakht, Alexander V., Alexeev, Dmitry G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4715287/
https://www.ncbi.nlm.nih.gov/pubmed/26774270
http://dx.doi.org/10.1186/s12859-015-0875-7
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author Dubinkina, Veronika B.
Ischenko, Dmitry S.
Ulyantsev, Vladimir I.
Tyakht, Alexander V.
Alexeev, Dmitry G.
author_facet Dubinkina, Veronika B.
Ischenko, Dmitry S.
Ulyantsev, Vladimir I.
Tyakht, Alexander V.
Alexeev, Dmitry G.
author_sort Dubinkina, Veronika B.
collection PubMed
description BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-015-0875-7) contains supplementary material, which is available to authorized users.
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spelling pubmed-47152872016-01-17 Assessment of k-mer spectrum applicability for metagenomic dissimilarity analysis Dubinkina, Veronika B. Ischenko, Dmitry S. Ulyantsev, Vladimir I. Tyakht, Alexander V. Alexeev, Dmitry G. BMC Bioinformatics Research Article BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-015-0875-7) contains supplementary material, which is available to authorized users. BioMed Central 2016-01-16 /pmc/articles/PMC4715287/ /pubmed/26774270 http://dx.doi.org/10.1186/s12859-015-0875-7 Text en © Dubinkina et al. 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Dubinkina, Veronika B.
Ischenko, Dmitry S.
Ulyantsev, Vladimir I.
Tyakht, Alexander V.
Alexeev, Dmitry G.
Assessment of k-mer spectrum applicability for metagenomic dissimilarity analysis
title Assessment of k-mer spectrum applicability for metagenomic dissimilarity analysis
title_full Assessment of k-mer spectrum applicability for metagenomic dissimilarity analysis
title_fullStr Assessment of k-mer spectrum applicability for metagenomic dissimilarity analysis
title_full_unstemmed Assessment of k-mer spectrum applicability for metagenomic dissimilarity analysis
title_short Assessment of k-mer spectrum applicability for metagenomic dissimilarity analysis
title_sort assessment of k-mer spectrum applicability for metagenomic dissimilarity analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4715287/
https://www.ncbi.nlm.nih.gov/pubmed/26774270
http://dx.doi.org/10.1186/s12859-015-0875-7
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