Estimating the total genome length of a metagenomic sample using k-mers.
Identifieur interne : 000577 ( PubMed/Curation ); précédent : 000576; suivant : 000578Estimating the total genome length of a metagenomic sample using k-mers.
Auteurs : Kui Hua [République populaire de Chine] ; Xuegong Zhang [République populaire de Chine]Source :
- BMC genomics [ 1471-2164 ] ; 2019.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- genetics : Microbiota.
- methods : Metagenomics.
- Algorithms, Datasets as Topic, High-Throughput Nucleotide Sequencing, Humans, Metagenome, Sequence Analysis, DNA, Software.
Abstract
Metagenomic sequencing is a powerful technology for studying the mixture of microbes or the microbiomes on human and in the environment. One basic task of analyzing metagenomic data is to identify the component genomes in the community. This task is challenging due to the complexity of microbiome composition, limited availability of known reference genomes, and usually insufficient sequencing coverage.
DOI: 10.1186/s12864-019-5467-x
PubMed: 30967110
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<author><name sortKey="Hua, Kui" sort="Hua, Kui" uniqKey="Hua K" first="Kui" last="Hua">Kui Hua</name>
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<country xml:lang="fr">République populaire de Chine</country>
<wicri:regionArea>MOE Key Laboratory of Bioinformatics Division and Center for Synthetic & System Biology, BNRIST, Beijing, 100084</wicri:regionArea>
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<author><name sortKey="Zhang, Xuegong" sort="Zhang, Xuegong" uniqKey="Zhang X" first="Xuegong" last="Zhang">Xuegong Zhang</name>
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<author><name sortKey="Zhang, Xuegong" sort="Zhang, Xuegong" uniqKey="Zhang X" first="Xuegong" last="Zhang">Xuegong Zhang</name>
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<series><title level="j">BMC genomics</title>
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<term>Datasets as Topic</term>
<term>High-Throughput Nucleotide Sequencing</term>
<term>Humans</term>
<term>Metagenome</term>
<term>Metagenomics (methods)</term>
<term>Microbiota (genetics)</term>
<term>Sequence Analysis, DNA</term>
<term>Software</term>
</keywords>
<keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes</term>
<term>Analyse de séquence d'ADN</term>
<term>Données de la recherche comme sujet</term>
<term>Humains</term>
<term>Logiciel</term>
<term>Microbiote (génétique)</term>
<term>Métagénome</term>
<term>Métagénomique ()</term>
<term>Séquençage nucléotidique à haut débit</term>
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<keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Microbiota</term>
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<keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Metagenomics</term>
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<term>Analyse de séquence d'ADN</term>
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<term>Humains</term>
<term>Logiciel</term>
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<front><div type="abstract" xml:lang="en">Metagenomic sequencing is a powerful technology for studying the mixture of microbes or the microbiomes on human and in the environment. One basic task of analyzing metagenomic data is to identify the component genomes in the community. This task is challenging due to the complexity of microbiome composition, limited availability of known reference genomes, and usually insufficient sequencing coverage.</div>
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<Month>08</Month>
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<Title>BMC genomics</Title>
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<ArticleTitle>Estimating the total genome length of a metagenomic sample using k-mers.</ArticleTitle>
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<Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Metagenomic sequencing is a powerful technology for studying the mixture of microbes or the microbiomes on human and in the environment. One basic task of analyzing metagenomic data is to identify the component genomes in the community. This task is challenging due to the complexity of microbiome composition, limited availability of known reference genomes, and usually insufficient sequencing coverage.</AbstractText>
<AbstractText Label="RESULTS" NlmCategory="RESULTS">As an initial step toward understanding the complete composition of a metagenomic sample, we studied the problem of estimating the total length of all distinct component genomes in a metagenomic sample. We showed that this problem can be solved by estimating the total number of distinct k-mers in all the metagenomic sequencing data. We proposed a method for this estimation based on the sequencing coverage distribution of observed k-mers, and introduced a k-mer redundancy index (KRI) to fill in the gap between the count of distinct k-mers and the total genome length. We showed the effectiveness of the proposed method on a set of carefully designed simulation data corresponding to multiple situations of true metagenomic data. Results on real data indicate that the uncaptured genomic information can vary dramatically across metagenomic samples, with the potential to mislead downstream analyses.</AbstractText>
<AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">We proposed the question of how long the total genome length of all different species in a microbial community is and introduced a method to answer it.</AbstractText>
</Abstract>
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<AffiliationInfo><Affiliation>MOE Key Laboratory of Bioinformatics Division and Center for Synthetic & System Biology, BNRIST, Beijing, 100084, China.</Affiliation>
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<AffiliationInfo><Affiliation>Department of Automation, Tsinghua University, Beijing, 100084, China.</Affiliation>
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<AffiliationInfo><Affiliation>Department of Automation, Tsinghua University, Beijing, 100084, China. zhangxg@tsinghua.edu.cn.</Affiliation>
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<AffiliationInfo><Affiliation>School of Life Sciences, Tsinghua University, Beijing, 100084, China. zhangxg@tsinghua.edu.cn.</Affiliation>
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