MiCoP: microbial community profiling method for detecting viral and fungal organisms in metagenomic samples
Identifieur interne : 000482 ( Main/Exploration ); précédent : 000481; suivant : 000483MiCoP: 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 ; 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.
Here we present a method, MiCoP (
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:
Url:
DOI: 10.1186/s12864-019-5699-9
PubMed: 31167634
PubMed Central: 6551237
Affiliations:
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Serghei" uniqKey="Mangul S" first="Serghei" last="Mangul">Serghei Mangul</name><affiliation wicri:level="1"><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="ISNI">0000 0000 9632 6718</institution-id><institution-id institution-id-type="GRID">grid.19006.3e</institution-id><institution>Department of Computer Science,</institution><institution>University of California,</institution></institution-wrap>Los Angeles, 90095 CA USA</nlm:aff><country>États-Unis</country><placeName><region type="state">Californie</region></placeName><wicri:regionArea>Los Angeles</wicri:regionArea></affiliation></author><author><name sortKey="Alser, Mohammed" sort="Alser, Mohammed" uniqKey="Alser M" first="Mohammed" last="Alser">Mohammed Alser</name><affiliation wicri:level="1"><nlm:aff id="Aff3"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2156 2780</institution-id><institution-id institution-id-type="GRID">grid.5801.c</institution-id><institution>Department of Computer Science,</institution><institution>ETH Zürich,</institution></institution-wrap>Zürich, 8092 Switzerland</nlm:aff><country xml:lang="fr">Suisse</country><wicri:regionArea>Zürich</wicri:regionArea></affiliation></author><author><name sortKey="Mandric, Igor" sort="Mandric, Igor" uniqKey="Mandric I" first="Igor" last="Mandric">Igor Mandric</name><affiliation wicri:level="1"><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="ISNI">0000 0000 9632 6718</institution-id><institution-id institution-id-type="GRID">grid.19006.3e</institution-id><institution>Department of Computer Science,</institution><institution>University of California,</institution></institution-wrap>Los Angeles, 90095 CA USA</nlm:aff><country>États-Unis</country><placeName><region type="state">Californie</region></placeName><wicri:regionArea>Los Angeles</wicri:regionArea></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:aff id="Aff4"><institution-wrap><institution-id institution-id-type="ISNI">0000000122199231</institution-id><institution-id institution-id-type="GRID">grid.214007.0</institution-id><institution>Department of Integrative Structural and Computational Biology,</institution><institution>The Scripps Research Institute,</institution></institution-wrap>La Jolla, CA92037 USA</nlm:aff><wicri:noCountry code="subfield">CA92037 USA</wicri:noCountry></affiliation></author><author><name sortKey="Koslicki, David" sort="Koslicki, David" uniqKey="Koslicki D" first="David" last="Koslicki">David Koslicki</name><affiliation wicri:level="1"><nlm:aff id="Aff5"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2112 1969</institution-id><institution-id institution-id-type="GRID">grid.4391.f</institution-id><institution>Department of Mathematics,</institution><institution>Oregon State University,</institution></institution-wrap>Corvallis, 97331 OR USA</nlm:aff><country>États-Unis</country><placeName><region type="state">Oregon</region></placeName><wicri:regionArea>Corvallis</wicri:regionArea></affiliation></author><author><name sortKey="Eskin, Eleazar" sort="Eskin, Eleazar" uniqKey="Eskin E" first="Eleazar" last="Eskin">Eleazar Eskin</name><affiliation wicri:level="1"><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="ISNI">0000 0000 9632 6718</institution-id><institution-id institution-id-type="GRID">grid.19006.3e</institution-id><institution>Department of Computer Science,</institution><institution>University of California,</institution></institution-wrap>Los Angeles, 90095 CA USA</nlm:aff><country>États-Unis</country><placeName><region type="state">Californie</region></placeName><wicri:regionArea>Los Angeles</wicri:regionArea></affiliation><affiliation wicri:level="1"><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="ISNI">0000 0000 9632 6718</institution-id><institution-id institution-id-type="GRID">grid.19006.3e</institution-id><institution>Department of Human Genetics,</institution><institution>University of California,</institution></institution-wrap>Los Angeles, 90095 CA USA</nlm:aff><country>États-Unis</country><placeName><region type="state">Californie</region></placeName><wicri:regionArea>Los Angeles</wicri:regionArea></affiliation></author></analytic><series><title level="j">BMC Genomics</title><idno type="eISSN">1471-2164</idno><imprint><date when="2019">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="KwdFr" xml:lang="fr"><term>Algorithmes</term><term>Analyse de séquence d'ADN ()</term><term>Biologie informatique ()</term><term>Champignons ()</term><term>Champignons (génétique)</term><term>Génome fongique</term><term>Génome viral</term><term>Humains</term><term>Marqueurs génétiques</term><term>Microbiote</term><term>Métagénomique ()</term><term>Séquençage nucléotidique à haut débit ()</term><term>Virus ()</term><term>Virus (génétique)</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="génétique" xml:lang="fr"><term>Champignons</term><term>Virus</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><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Analyse de séquence d'ADN</term><term>Biologie informatique</term><term>Champignons</term><term>Génome fongique</term><term>Génome viral</term><term>Humains</term><term>Marqueurs génétiques</term><term>Microbiote</term><term>Métagénomique</term><term>Séquençage nucléotidique à haut débit</term><term>Virus</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background</title><p>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.</p></sec><sec><title>Results</title><p>Here we present a method, MiCoP (<bold>Mi</bold>crobiome <bold>Co</bold>mmunity <bold>P</bold>rofiling), 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.</p></sec><sec><title>Conclusions</title><p>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: <ext-link ext-link-type="uri" xlink:href="https://github.com/smangul1/MiCoP">https://github.com/smangul1/MiCoP</ext-link>.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Handelsman, J" uniqKey="Handelsman J">J Handelsman</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Wooley, Jc" uniqKey="Wooley J">JC Wooley</name></author><author><name sortKey="Godzik, A" uniqKey="Godzik A">A Godzik</name></author><author><name sortKey="Friedberg, I" uniqKey="Friedberg I">I Friedberg</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Stewart, Ej" uniqKey="Stewart E">EJ Stewart</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Venter, Jc" uniqKey="Venter J">JC Venter</name></author><author><name sortKey="Remington, K" uniqKey="Remington K">K 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sortKey="Langmead, B" uniqKey="Langmead B">B Langmead</name></author><author><name sortKey="Salzberg, Sl" uniqKey="Salzberg S">SL Salzberg</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Buchfink, B" uniqKey="Buchfink B">B Buchfink</name></author><author><name sortKey="Xie, C" uniqKey="Xie C">C Xie</name></author><author><name sortKey="Huson, Dh" uniqKey="Huson D">DH Huson</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Angly, Fe" uniqKey="Angly F">FE Angly</name></author><author><name sortKey="Willner, D" uniqKey="Willner D">D Willner</name></author><author><name sortKey="Rohwer, F" uniqKey="Rohwer F">F Rohwer</name></author><author><name sortKey="Hugenholtz, P" uniqKey="Hugenholtz P">P Hugenholtz</name></author><author><name sortKey="Tyson, Gw" uniqKey="Tyson G">GW Tyson</name></author></analytic></biblStruct></listBibl></div1></back></TEI><affiliations><list><country><li>Suisse</li><li>États-Unis</li></country><region><li>Californie</li><li>Oregon</li></region></list><tree><noCountry><name sortKey="Wu, Nicholas C" sort="Wu, Nicholas C" uniqKey="Wu N" first="Nicholas C." last="Wu">Nicholas C. Wu</name></noCountry><country name="États-Unis"><region name="Californie"><name sortKey="Lapierre, Nathan" sort="Lapierre, Nathan" uniqKey="Lapierre N" first="Nathan" last="Lapierre">Nathan Lapierre</name></region><name sortKey="Eskin, Eleazar" sort="Eskin, Eleazar" uniqKey="Eskin E" first="Eleazar" last="Eskin">Eleazar Eskin</name><name sortKey="Eskin, Eleazar" sort="Eskin, Eleazar" uniqKey="Eskin E" first="Eleazar" last="Eskin">Eleazar Eskin</name><name sortKey="Koslicki, David" sort="Koslicki, David" uniqKey="Koslicki D" first="David" last="Koslicki">David Koslicki</name><name sortKey="Mandric, Igor" sort="Mandric, Igor" uniqKey="Mandric I" first="Igor" last="Mandric">Igor Mandric</name><name sortKey="Mangul, Serghei" sort="Mangul, Serghei" uniqKey="Mangul S" first="Serghei" last="Mangul">Serghei Mangul</name></country><country name="Suisse"><noRegion><name sortKey="Alser, Mohammed" sort="Alser, Mohammed" uniqKey="Alser M" first="Mohammed" last="Alser">Mohammed Alser</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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