SimkaMin: fast and resource frugal de novo comparative metagenomics.
Identifieur interne : 000080 ( PubMed/Checkpoint ); précédent : 000079; suivant : 000081SimkaMin: fast and resource frugal de novo comparative metagenomics.
Auteurs : Gaëtan Benoit [France] ; Mahendra Mariadassou [France] ; Stéphane Robin [France] ; Sophie Schbath [France] ; Pierre Peterlongo [France] ; Claire Lemaitre [France]Source :
- Bioinformatics (Oxford, England) [ 1367-4811 ] ; 2020.
Abstract
De novo comparative metagenomics is one of the most straightforward ways to analyze large sets of metagenomic data. Latest methods use the fraction of shared k-mers to estimate genomic similarity between read sets. However, those methods, while extremely efficient, are still limited by computational needs for practical usage outside of large computing facilities.
DOI: 10.1093/bioinformatics/btz685
PubMed: 31504187
Affiliations:
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<front><div type="abstract" xml:lang="en">De novo comparative metagenomics is one of the most straightforward ways to analyze large sets of metagenomic data. Latest methods use the fraction of shared k-mers to estimate genomic similarity between read sets. However, those methods, while extremely efficient, are still limited by computational needs for practical usage outside of large computing facilities.</div>
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<DateRevised><Year>2020</Year>
<Month>02</Month>
<Day>20</Day>
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<Title>Bioinformatics (Oxford, England)</Title>
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<ArticleTitle>SimkaMin: fast and resource frugal de novo comparative metagenomics.</ArticleTitle>
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<Abstract><AbstractText Label="MOTIVATION" NlmCategory="BACKGROUND">De novo comparative metagenomics is one of the most straightforward ways to analyze large sets of metagenomic data. Latest methods use the fraction of shared k-mers to estimate genomic similarity between read sets. However, those methods, while extremely efficient, are still limited by computational needs for practical usage outside of large computing facilities.</AbstractText>
<AbstractText Label="RESULTS" NlmCategory="RESULTS">We present SimkaMin, a quick comparative metagenomics tool with low disk and memory footprints, thanks to an efficient data subsampling scheme used to estimate Bray-Curtis and Jaccard dissimilarities. One billion metagenomic reads can be analyzed in <3 min, with tiny memory (1.09 GB) and disk (≈0.3 GB) requirements and without altering the quality of the downstream comparative analyses, making of SimkaMin a tool perfectly tailored for very large-scale metagenomic projects.</AbstractText>
<AbstractText Label="AVAILABILITY AND IMPLEMENTATION" NlmCategory="METHODS">https://github.com/GATB/simka.</AbstractText>
<AbstractText Label="SUPPLEMENTARY INFORMATION" NlmCategory="BACKGROUND">Supplementary data are available at Bioinformatics online.</AbstractText>
<CopyrightInformation>© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.</CopyrightInformation>
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