MiCoP: microbial community profiling method for detecting viral and fungal organisms in metagenomic samples.
Identifieur interne : 000513 ( PubMed/Corpus ); précédent : 000512; suivant : 000514MiCoP: microbial community profiling method for detecting viral and fungal organisms in metagenomic samples.
Auteurs : Nathan Lapierre ; Serghei Mangul ; Mohammed Alser ; Igor Mandric ; Nicholas C. Wu ; David Koslicki ; Eleazar EskinSource :
- BMC genomics [ 1471-2164 ] ; 2019.
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
Links to Exploration step
pubmed:31167634Le document en format XML
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Wu</name><affiliation><nlm:affiliation>Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA92037, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Koslicki, David" sort="Koslicki, David" uniqKey="Koslicki D" first="David" last="Koslicki">David Koslicki</name><affiliation><nlm:affiliation>Department of Mathematics, Oregon State University, Corvallis, 97331, OR, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Eskin, Eleazar" sort="Eskin, Eleazar" uniqKey="Eskin E" first="Eleazar" last="Eskin">Eleazar Eskin</name><affiliation><nlm:affiliation>Department of Computer Science, University of California, Los Angeles, 90095, CA, USA.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2019">2019</date><idno type="RBID">pubmed:31167634</idno><idno type="pmid">31167634</idno><idno type="doi">10.1186/s12864-019-5699-9</idno><idno type="wicri:Area/PubMed/Corpus">000513</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000513</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">MiCoP: microbial community profiling method for detecting viral and fungal organisms in metagenomic samples.</title><author><name sortKey="Lapierre, Nathan" sort="Lapierre, Nathan" uniqKey="Lapierre N" first="Nathan" last="Lapierre">Nathan Lapierre</name><affiliation><nlm:affiliation>Department of Computer Science, University of California, Los Angeles, 90095, CA, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Mangul, Serghei" sort="Mangul, Serghei" uniqKey="Mangul S" first="Serghei" last="Mangul">Serghei Mangul</name><affiliation><nlm:affiliation>Department of Computer Science, University of California, Los Angeles, 90095, CA, USA. smangul@ucla.edu.</nlm:affiliation></affiliation></author><author><name sortKey="Alser, Mohammed" sort="Alser, Mohammed" uniqKey="Alser M" first="Mohammed" last="Alser">Mohammed Alser</name><affiliation><nlm:affiliation>Department of Computer Science, ETH Zürich, Zürich, 8092, Switzerland.</nlm:affiliation></affiliation></author><author><name sortKey="Mandric, Igor" sort="Mandric, Igor" uniqKey="Mandric I" first="Igor" last="Mandric">Igor Mandric</name><affiliation><nlm:affiliation>Department of Computer Science, University of California, Los Angeles, 90095, CA, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Wu, Nicholas C" sort="Wu, Nicholas C" uniqKey="Wu N" first="Nicholas C" last="Wu">Nicholas C. Wu</name><affiliation><nlm:affiliation>Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA92037, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Koslicki, David" sort="Koslicki, David" uniqKey="Koslicki D" first="David" last="Koslicki">David Koslicki</name><affiliation><nlm:affiliation>Department of Mathematics, Oregon State University, Corvallis, 97331, OR, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Eskin, Eleazar" sort="Eskin, Eleazar" uniqKey="Eskin E" first="Eleazar" last="Eskin">Eleazar Eskin</name><affiliation><nlm:affiliation>Department of Computer Science, University of California, Los Angeles, 90095, CA, USA.</nlm:affiliation></affiliation></author></analytic><series><title level="j">BMC genomics</title><idno type="eISSN">1471-2164</idno><imprint><date when="2019" type="published">2019</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Computational Biology (methods)</term><term>Fungi (classification)</term><term>Fungi (genetics)</term><term>Genetic Markers</term><term>Genome, Fungal</term><term>Genome, Viral</term><term>High-Throughput Nucleotide Sequencing (methods)</term><term>Humans</term><term>Metagenomics (methods)</term><term>Microbiota</term><term>Sequence Analysis, DNA (methods)</term><term>Viruses (classification)</term><term>Viruses (genetics)</term></keywords><keywords scheme="MESH" type="chemical" xml:lang="en"><term>Genetic Markers</term></keywords><keywords scheme="MESH" qualifier="classification" xml:lang="en"><term>Fungi</term><term>Viruses</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Fungi</term><term>Viruses</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Computational Biology</term><term>High-Throughput Nucleotide Sequencing</term><term>Metagenomics</term><term>Sequence Analysis, DNA</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Genome, Fungal</term><term>Genome, Viral</term><term>Humans</term><term>Microbiota</term></keywords></textClass></profileDesc></teiHeader><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></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">31167634</PMID><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></Author><Author ValidYN="Y"><LastName>Mandric</LastName><ForeName>Igor</ForeName><Initials>I</Initials><AffiliationInfo><Affiliation>Department of Computer Science, University of California, Los Angeles, 90095, CA, USA.</Affiliation></AffiliationInfo></Author><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></Author><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></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>R01 ES021801</GrantID><Acronym>ES</Acronym><Agency>NIEHS NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>R01 MH101782</GrantID><Acronym>MH</Acronym><Agency>NIMH NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>K25 HL080079</GrantID><Acronym>HL</Acronym><Agency>NHLBI NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>P01 HL028481</GrantID><Acronym>HL</Acronym><Agency>NHLBI NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>U01 DA024417</GrantID><Acronym>DA</Acronym><Agency>NIDA NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>T32 EB016640</GrantID><Acronym>EB</Acronym><Agency>NIBIB NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>R01 ES022282</GrantID><Acronym>ES</Acronym><Agency>NIEHS NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>P01 HL030568</GrantID><Acronym>HL</Acronym><Agency>NHLBI NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>R01 GM083198</GrantID><Acronym>GM</Acronym><Agency>NIGMS NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2019</Year><Month>06</Month><Day>06</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>BMC Genomics</MedlineTA><NlmUniqueID>100965258</NlmUniqueID><ISSNLinking>1471-2164</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D005819">Genetic 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