MiCoP: microbial community profiling method for detecting viral and fungal organisms in metagenomic samples.
Identifieur interne : 000481 ( PubMed/Checkpoint ); précédent : 000480; suivant : 000482MiCoP: microbial community profiling method for detecting viral and fungal organisms in metagenomic samples.
Auteurs : Nathan Lapierre [États-Unis] ; Serghei Mangul [États-Unis] ; Mohammed Alser [Suisse] ; Igor Mandric [États-Unis] ; Nicholas C. Wu [États-Unis] ; David Koslicki [États-Unis] ; Eleazar Eskin [États-Unis]Source :
- BMC genomics [ 1471-2164 ] ; 2019.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- Algorithms, Computational Biology (methods), Fungi (classification), Fungi (genetics), Genetic Markers, Genome, Fungal, Genome, Viral, High-Throughput Nucleotide Sequencing (methods), Humans, Metagenomics (methods), Microbiota, Sequence Analysis, DNA (methods), Viruses (classification), Viruses (genetics).
- MESH :
- chemical : Genetic Markers.
- classification : Fungi, Viruses.
- genetics : Fungi, Viruses.
- methods : Computational Biology, High-Throughput Nucleotide Sequencing, Metagenomics, Sequence Analysis, DNA.
- Algorithms, Genome, Fungal, Genome, Viral, Humans, Microbiota.
Abstract
High throughput sequencing has spurred the development of metagenomics, which involves the direct analysis of microbial communities in various environments such as soil, ocean water, and the human body. Many existing methods based on marker genes or k-mers have limited sensitivity or are too computationally demanding for many users. Additionally, most work in metagenomics has focused on bacteria and archaea, neglecting to study other key microbes such as viruses and eukaryotes.
DOI: 10.1186/s12864-019-5699-9
PubMed: 31167634
Affiliations:
Links toward previous steps (curation, corpus...)
Links to Exploration step
pubmed:31167634Le document en format XML
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<term>Genetic Markers</term>
<term>Genome, Fungal</term>
<term>Genome, Viral</term>
<term>High-Throughput Nucleotide Sequencing (methods)</term>
<term>Humans</term>
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<front><div type="abstract" xml:lang="en">High throughput sequencing has spurred the development of metagenomics, which involves the direct analysis of microbial communities in various environments such as soil, ocean water, and the human body. Many existing methods based on marker genes or k-mers have limited sensitivity or are too computationally demanding for many users. Additionally, most work in metagenomics has focused on bacteria and archaea, neglecting to study other key microbes such as viruses and eukaryotes.</div>
</front>
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<DateCompleted><Year>2019</Year>
<Month>11</Month>
<Day>06</Day>
</DateCompleted>
<DateRevised><Year>2020</Year>
<Month>02</Month>
<Day>25</Day>
</DateRevised>
<Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1471-2164</ISSN>
<JournalIssue CitedMedium="Internet"><Volume>20</Volume>
<Issue>Suppl 5</Issue>
<PubDate><Year>2019</Year>
<Month>Jun</Month>
<Day>06</Day>
</PubDate>
</JournalIssue>
<Title>BMC genomics</Title>
<ISOAbbreviation>BMC Genomics</ISOAbbreviation>
</Journal>
<ArticleTitle>MiCoP: microbial community profiling method for detecting viral and fungal organisms in metagenomic samples.</ArticleTitle>
<Pagination><MedlinePgn>423</MedlinePgn>
</Pagination>
<ELocationID EIdType="doi" ValidYN="Y">10.1186/s12864-019-5699-9</ELocationID>
<Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">High throughput sequencing has spurred the development of metagenomics, which involves the direct analysis of microbial communities in various environments such as soil, ocean water, and the human body. Many existing methods based on marker genes or k-mers have limited sensitivity or are too computationally demanding for many users. Additionally, most work in metagenomics has focused on bacteria and archaea, neglecting to study other key microbes such as viruses and eukaryotes.</AbstractText>
<AbstractText Label="RESULTS" NlmCategory="RESULTS">Here we present a method, MiCoP (Microbiome Community Profiling), that uses fast-mapping of reads to build a comprehensive reference database of full genomes from viruses and eukaryotes to achieve maximum read usage and enable the analysis of the virome and eukaryome in each sample. We demonstrate that mapping of metagenomic reads is feasible for the smaller viral and eukaryotic reference databases. We show that our method is accurate on simulated and mock community data and identifies many more viral and fungal species than previously-reported results on real data from the Human Microbiome Project.</AbstractText>
<AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">MiCoP is a mapping-based method that proves more effective than existing methods at abundance profiling of viruses and eukaryotes in metagenomic samples. MiCoP can be used to detect the full diversity of these communities. The code, data, and documentation are publicly available on GitHub at: https://github.com/smangul1/MiCoP .</AbstractText>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>LaPierre</LastName>
<ForeName>Nathan</ForeName>
<Initials>N</Initials>
<AffiliationInfo><Affiliation>Department of Computer Science, University of California, Los Angeles, 90095, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Mangul</LastName>
<ForeName>Serghei</ForeName>
<Initials>S</Initials>
<AffiliationInfo><Affiliation>Department of Computer Science, University of California, Los Angeles, 90095, CA, USA. smangul@ucla.edu.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Alser</LastName>
<ForeName>Mohammed</ForeName>
<Initials>M</Initials>
<AffiliationInfo><Affiliation>Department of Computer Science, ETH Zürich, Zürich, 8092, Switzerland.</Affiliation>
</AffiliationInfo>
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<Author ValidYN="Y"><LastName>Mandric</LastName>
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<AffiliationInfo><Affiliation>Department of Computer Science, University of California, Los Angeles, 90095, CA, USA.</Affiliation>
</AffiliationInfo>
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<Author ValidYN="Y"><LastName>Wu</LastName>
<ForeName>Nicholas C</ForeName>
<Initials>NC</Initials>
<AffiliationInfo><Affiliation>Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA92037, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Koslicki</LastName>
<ForeName>David</ForeName>
<Initials>D</Initials>
<AffiliationInfo><Affiliation>Department of Mathematics, Oregon State University, Corvallis, 97331, OR, USA.</Affiliation>
</AffiliationInfo>
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<Author ValidYN="Y"><LastName>Eskin</LastName>
<ForeName>Eleazar</ForeName>
<Initials>E</Initials>
<AffiliationInfo><Affiliation>Department of Computer Science, University of California, Los Angeles, 90095, CA, USA.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Department of Human Genetics, University of California, Los Angeles, 90095, CA, USA.</Affiliation>
</AffiliationInfo>
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<MeshHeading><DescriptorName UI="D016679" MajorTopicYN="N">Genome, Viral</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D059014" MajorTopicYN="N">High-Throughput Nucleotide Sequencing</DescriptorName>
<QualifierName UI="Q000379" MajorTopicYN="N">methods</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D056186" MajorTopicYN="N">Metagenomics</DescriptorName>
<QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D064307" MajorTopicYN="Y">Microbiota</DescriptorName>
</MeshHeading>
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<QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D014780" MajorTopicYN="N">Viruses</DescriptorName>
<QualifierName UI="Q000145" MajorTopicYN="N">classification</QualifierName>
<QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName>
</MeshHeading>
</MeshHeadingList>
<KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Abundance estimation</Keyword>
<Keyword MajorTopicYN="N">Alignment</Keyword>
<Keyword MajorTopicYN="N">Community profiling</Keyword>
<Keyword MajorTopicYN="N">Eukaryome</Keyword>
<Keyword MajorTopicYN="N">Metagenomics</Keyword>
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