Identifying Group-Specific Sequences for Microbial Communities Using Long k-mer Sequence Signatures.
Identifieur interne : 000892 ( PubMed/Checkpoint ); précédent : 000891; suivant : 000893Identifying Group-Specific Sequences for Microbial Communities Using Long k-mer Sequence Signatures.
Auteurs : Ying Wang [République populaire de Chine] ; Lei Fu [République populaire de Chine] ; Jie Ren [États-Unis] ; Zhaoxia Yu [États-Unis] ; Ting Chen [États-Unis] ; Fengzhu Sun [États-Unis]Source :
- Frontiers in microbiology [ 1664-302X ] ; 2018.
Abstract
Comparing metagenomic samples is crucial for understanding microbial communities. For different groups of microbial communities, such as human gut metagenomic samples from patients with a certain disease and healthy controls, identifying group-specific sequences offers essential information for potential biomarker discovery. A sequence that is present, or rich, in one group, but absent, or scarce, in another group is considered "group-specific" in our study. Our main purpose is to discover group-specific sequence regions between control and case groups as disease-associated markers. We developed a long k-mer (k ≥ 30 bps)-based computational pipeline to detect group-specific sequences at strain resolution free from reference sequences, sequence alignments, and metagenome-wide de novo assembly. We called our method MetaGO: Group-specific oligonucleotide analysis for metagenomic samples. An open-source pipeline on Apache Spark was developed with parallel computing. We applied MetaGO to one simulated and three real metagenomic datasets to evaluate the discriminative capability of identified group-specific markers. In the simulated dataset, 99.11% of group-specific logical 40-mers covered 98.89% disease-specific regions from the disease-associated strain. In addition, 97.90% of group-specific numerical 40-mers covered 99.61 and 96.39% of differentially abundant genome and regions between two groups, respectively. For a large-scale metagenomic liver cirrhosis (LC)-associated dataset, we identified 37,647 group-specific 40-mer features. Any one of the features can predict disease status of the training samples with the average of sensitivity and specificity higher than 0.8. The random forests classification using the top 10 group-specific features yielded a higher AUC (from ∼0.8 to ∼0.9) than that of previous studies. All group-specific 40-mers were present in LC patients, but not healthy controls. All the assembled 11 LC-specific sequences can be mapped to two strains of Veillonella parvula: UTDB1-3 and DSM2008. The experiments on the other two real datasets related to Inflammatory Bowel Disease and Type 2 Diabetes in Women consistently demonstrated that MetaGO achieved better prediction accuracy with fewer features compared to previous studies. The experiments showed that MetaGO is a powerful tool for identifying group-specific k-mers, which would be clinically applicable for disease prediction. MetaGO is available at https://github.com/VVsmileyx/MetaGO.
DOI: 10.3389/fmicb.2018.00872
PubMed: 29774017
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last="Fu">Lei Fu</name><affiliation wicri:level="1"><nlm:affiliation>Department of Automation, Xiamen University, Xiamen, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Automation, Xiamen University, Xiamen</wicri:regionArea><wicri:noRegion>Xiamen</wicri:noRegion></affiliation></author><author><name sortKey="Ren, Jie" sort="Ren, Jie" uniqKey="Ren J" first="Jie" last="Ren">Jie Ren</name><affiliation wicri:level="4"><nlm:affiliation>Molecular and Computational Biology Program, University of Southern California, Los Angeles, CA, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Molecular and Computational Biology Program, University of Southern California, Los Angeles, CA</wicri:regionArea><placeName><region type="state">Californie</region><settlement type="city">Los Angeles</settlement></placeName><orgName type="university">Université de Californie du Sud</orgName></affiliation></author><author><name sortKey="Yu, Zhaoxia" sort="Yu, Zhaoxia" uniqKey="Yu Z" first="Zhaoxia" last="Yu">Zhaoxia Yu</name><affiliation wicri:level="2"><nlm:affiliation>Department of Statistics, University of California, Irvine, Irvine, CA, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Statistics, University of California, Irvine, Irvine, CA</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author><author><name sortKey="Chen, Ting" sort="Chen, Ting" uniqKey="Chen T" first="Ting" last="Chen">Ting Chen</name><affiliation wicri:level="4"><nlm:affiliation>Molecular and Computational Biology Program, University of Southern California, Los Angeles, CA, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Molecular and Computational Biology Program, University of Southern California, Los Angeles, CA</wicri:regionArea><placeName><region type="state">Californie</region><settlement type="city">Los Angeles</settlement></placeName><orgName type="university">Université de Californie du Sud</orgName></affiliation></author><author><name sortKey="Sun, Fengzhu" sort="Sun, Fengzhu" uniqKey="Sun F" first="Fengzhu" last="Sun">Fengzhu Sun</name><affiliation wicri:level="4"><nlm:affiliation>Molecular and Computational Biology Program, University of Southern California, Los Angeles, CA, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Molecular and Computational Biology Program, University of Southern California, Los Angeles, CA</wicri:regionArea><placeName><region type="state">Californie</region><settlement type="city">Los Angeles</settlement></placeName><orgName type="university">Université de Californie du Sud</orgName></affiliation></author></analytic><series><title level="j">Frontiers in microbiology</title><idno type="ISSN">1664-302X</idno><imprint><date when="2018" type="published">2018</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Comparing metagenomic samples is crucial for understanding microbial communities. For different groups of microbial communities, such as human gut metagenomic samples from patients with a certain disease and healthy controls, identifying <i>group-specific</i> sequences offers essential information for potential biomarker discovery. A sequence that is present, or rich, in one group, but absent, or scarce, in another group is considered "<i>group-specific</i>" in our study. Our main purpose is to discover <i>group-specific</i> sequence regions between control and case groups as disease-associated markers. We developed a long <i>k</i>-mer (<i>k</i> ≥ 30 bps)-based computational pipeline to detect <i>group-specific</i> sequences at strain resolution free from reference sequences, sequence alignments, and metagenome-wide <i>de novo</i> assembly. We called our method MetaGO: <i>Group-specific</i> oligonucleotide analysis for metagenomic samples. An open-source pipeline on <i>Apache Spark</i> was developed with parallel computing. We applied MetaGO to one simulated and three real metagenomic datasets to evaluate the discriminative capability of identified <i>group-specific</i> markers. In the simulated dataset, 99.11% of <i>group-specific</i> logical <i>40</i>-mers covered 98.89% <i>disease-specific</i> regions from the disease-associated strain. In addition, 97.90% of <i>group-specific</i> numerical <i>40</i>-mers covered 99.61 and 96.39% of differentially abundant genome and regions between two groups, respectively. For a large-scale metagenomic liver cirrhosis (LC)-associated dataset, we identified 37,647 <i>group-specific 40-</i>mer features. Any one of the features can predict disease status of the training samples with the average of sensitivity and specificity higher than 0.8. The random forests classification using the top 10 <i>group-specific</i> features yielded a higher AUC (from ∼0.8 to ∼0.9) than that of previous studies. All <i>group-specific 40-</i>mers were present in LC patients, but not healthy controls. All the assembled 11 <i>LC-specific</i> sequences can be mapped to two strains of <i>Veillonella parvula</i>: UTDB1-3 and DSM2008. The experiments on the other two real datasets related to Inflammatory Bowel Disease and Type 2 Diabetes in Women consistently demonstrated that MetaGO achieved better prediction accuracy with fewer features compared to previous studies. The experiments showed that MetaGO is a powerful tool for identifying <i>group-specific k</i>-mers, which would be clinically applicable for disease prediction. MetaGO is available at https://github.com/VVsmileyx/MetaGO.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">29774017</PMID><DateRevised><Year>2019</Year><Month>11</Month><Day>20</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Print">1664-302X</ISSN><JournalIssue CitedMedium="Print"><Volume>9</Volume><PubDate><Year>2018</Year></PubDate></JournalIssue><Title>Frontiers in microbiology</Title><ISOAbbreviation>Front Microbiol</ISOAbbreviation></Journal><ArticleTitle>Identifying <i>Group-Specific</i> Sequences for Microbial Communities Using Long <i>k</i>-mer Sequence Signatures.</ArticleTitle><Pagination><MedlinePgn>872</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.3389/fmicb.2018.00872</ELocationID><Abstract><AbstractText>Comparing metagenomic samples is crucial for understanding microbial communities. For different groups of microbial communities, such as human gut metagenomic samples from patients with a certain disease and healthy controls, identifying <i>group-specific</i> sequences offers essential information for potential biomarker discovery. A sequence that is present, or rich, in one group, but absent, or scarce, in another group is considered "<i>group-specific</i>" in our study. Our main purpose is to discover <i>group-specific</i> sequence regions between control and case groups as disease-associated markers. We developed a long <i>k</i>-mer (<i>k</i> ≥ 30 bps)-based computational pipeline to detect <i>group-specific</i> sequences at strain resolution free from reference sequences, sequence alignments, and metagenome-wide <i>de novo</i> assembly. We called our method MetaGO: <i>Group-specific</i> oligonucleotide analysis for metagenomic samples. An open-source pipeline on <i>Apache Spark</i> was developed with parallel computing. We applied MetaGO to one simulated and three real metagenomic datasets to evaluate the discriminative capability of identified <i>group-specific</i> markers. In the simulated dataset, 99.11% of <i>group-specific</i> logical <i>40</i>-mers covered 98.89% <i>disease-specific</i> regions from the disease-associated strain. In addition, 97.90% of <i>group-specific</i> numerical <i>40</i>-mers covered 99.61 and 96.39% of differentially abundant genome and regions between two groups, respectively. For a large-scale metagenomic liver cirrhosis (LC)-associated dataset, we identified 37,647 <i>group-specific 40-</i>mer features. Any one of the features can predict disease status of the training samples with the average of sensitivity and specificity higher than 0.8. The random forests classification using the top 10 <i>group-specific</i> features yielded a higher AUC (from ∼0.8 to ∼0.9) than that of previous studies. All <i>group-specific 40-</i>mers were present in LC patients, but not healthy controls. All the assembled 11 <i>LC-specific</i> sequences can be mapped to two strains of <i>Veillonella parvula</i>: UTDB1-3 and DSM2008. The experiments on the other two real datasets related to Inflammatory Bowel Disease and Type 2 Diabetes in Women consistently demonstrated that MetaGO achieved better prediction accuracy with fewer features compared to previous studies. The experiments showed that MetaGO is a powerful tool for identifying <i>group-specific k</i>-mers, which would be clinically applicable for disease prediction. MetaGO is available at https://github.com/VVsmileyx/MetaGO.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Ying</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>Department of Automation, Xiamen University, Xiamen, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Fu</LastName><ForeName>Lei</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>Department of Automation, Xiamen University, Xiamen, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Ren</LastName><ForeName>Jie</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Molecular and Computational Biology Program, University of Southern California, Los Angeles, CA, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Yu</LastName><ForeName>Zhaoxia</ForeName><Initials>Z</Initials><AffiliationInfo><Affiliation>Department of Statistics, University of California, Irvine, Irvine, CA, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Chen</LastName><ForeName>Ting</ForeName><Initials>T</Initials><AffiliationInfo><Affiliation>Molecular and Computational Biology Program, University of Southern California, Los Angeles, CA, United States.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Bioinformatics Division, Tsinghua National Laboratory of Information Science and Technology, Tsinghua University, Beijing, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Computer Science and Technology, Tsinghua University, Beijing, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Sun</LastName><ForeName>Fengzhu</ForeName><Initials>F</Initials><AffiliationInfo><Affiliation>Molecular and Computational Biology Program, University of Southern California, Los Angeles, CA, United States.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Center for Computational Systems Biology, Fudan University, Shanghai, China.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2018</Year><Month>05</Month><Day>03</Day></ArticleDate></Article><MedlineJournalInfo><Country>Switzerland</Country><MedlineTA>Front Microbiol</MedlineTA><NlmUniqueID>101548977</NlmUniqueID><ISSNLinking>1664-302X</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">classification</Keyword><Keyword MajorTopicYN="N">disease prediction</Keyword><Keyword MajorTopicYN="N">group-specific sequence</Keyword><Keyword MajorTopicYN="N">long k-mer</Keyword><Keyword MajorTopicYN="N">metagenomics</Keyword><Keyword MajorTopicYN="N">microbial community</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate 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Wang</name></noRegion><name sortKey="Fu, Lei" sort="Fu, Lei" uniqKey="Fu L" first="Lei" last="Fu">Lei Fu</name></country><country name="États-Unis"><region name="Californie"><name sortKey="Ren, Jie" sort="Ren, Jie" uniqKey="Ren J" first="Jie" last="Ren">Jie Ren</name></region><name sortKey="Chen, Ting" sort="Chen, Ting" uniqKey="Chen T" first="Ting" last="Chen">Ting Chen</name><name sortKey="Sun, Fengzhu" sort="Sun, Fengzhu" uniqKey="Sun F" first="Fengzhu" last="Sun">Fengzhu Sun</name><name sortKey="Yu, Zhaoxia" sort="Yu, Zhaoxia" uniqKey="Yu Z" first="Zhaoxia" last="Yu">Zhaoxia Yu</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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