Linking Associations of Rare Low-Abundance Species to Their Environments by Association Networks.
Identifieur interne : 000D99 ( Main/Exploration ); précédent : 000D98; suivant : 000E00Linking Associations of Rare Low-Abundance Species to Their Environments by Association Networks.
Auteurs : Tatiana V. Karpinets [États-Unis] ; Vancheswaran Gopalakrishnan [États-Unis] ; Jennifer Wargo [États-Unis] ; Andrew P. Futreal [États-Unis] ; Christopher W. Schadt [États-Unis] ; Jianhua Zhang [États-Unis]Source :
- Frontiers in microbiology [ 1664-302X ] ; 2018.
Abstract
Studies of microbial communities by targeted sequencing of rRNA genes lead to recovering numerous rare low-abundance taxa with unknown biological roles. We propose to study associations of such rare organisms with their environments by a computational framework based on transformation of the data into qualitative variables. Namely, we analyze the sparse table of putative species or OTUs (operational taxonomic units) and samples generated in such studies, also known as an OTU table, by collecting statistics on co-occurrences of the species and on shared species richness across samples. Based on the statistics we built two association networks, of the rare putative species and of the samples respectively, using a known computational technique, Association networks (Anets) developed for analysis of qualitative data. Clusters of samples and clusters of OTUs are then integrated and combined with metadata of the study to produce a map of associated putative species in their environments. We tested and validated the framework on two types of microbiomes, of human body sites and that of the Populus tree root systems. We show that in both studies the associations of OTUs can separate samples according to environmental or physiological characteristics of the studied systems.
DOI: 10.3389/fmicb.2018.00297
PubMed: 29563898
PubMed Central: PMC5850922
Affiliations:
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We propose to study associations of such rare organisms with their environments by a computational framework based on transformation of the data into qualitative variables. Namely, we analyze the sparse table of putative species or OTUs (operational taxonomic units) and samples generated in such studies, also known as an OTU table, by collecting statistics on co-occurrences of the species and on shared species richness across samples. Based on the statistics we built two association networks, of the rare putative species and of the samples respectively, using a known computational technique, Association networks (Anets) developed for analysis of qualitative data. Clusters of samples and clusters of OTUs are then integrated and combined with metadata of the study to produce a map of associated putative species in their environments. We tested and validated the framework on two types of microbiomes, of human body sites and that of the <i>Populus</i> tree root systems. We show that in both studies the associations of OTUs can separate samples according to environmental or physiological characteristics of the studied systems.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">29563898</PMID><DateRevised><Year>2020</Year><Month>09</Month><Day>28</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>Linking Associations of Rare Low-Abundance Species to Their Environments by Association Networks.</ArticleTitle><Pagination><MedlinePgn>297</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.3389/fmicb.2018.00297</ELocationID><Abstract><AbstractText>Studies of microbial communities by targeted sequencing of rRNA genes lead to recovering numerous rare low-abundance taxa with unknown biological roles. We propose to study associations of such rare organisms with their environments by a computational framework based on transformation of the data into qualitative variables. Namely, we analyze the sparse table of putative species or OTUs (operational taxonomic units) and samples generated in such studies, also known as an OTU table, by collecting statistics on co-occurrences of the species and on shared species richness across samples. Based on the statistics we built two association networks, of the rare putative species and of the samples respectively, using a known computational technique, Association networks (Anets) developed for analysis of qualitative data. Clusters of samples and clusters of OTUs are then integrated and combined with metadata of the study to produce a map of associated putative species in their environments. We tested and validated the framework on two types of microbiomes, of human body sites and that of the <i>Populus</i> tree root systems. We show that in both studies the associations of OTUs can separate samples according to environmental or physiological characteristics of the studied systems.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Karpinets</LastName><ForeName>Tatiana V</ForeName><Initials>TV</Initials><AffiliationInfo><Affiliation>Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Gopalakrishnan</LastName><ForeName>Vancheswaran</ForeName><Initials>V</Initials><AffiliationInfo><Affiliation>Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Epidemiology, Human Genetics and Environmental Sciences, University of Texas School of Public Health, Dallas, TX, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Wargo</LastName><ForeName>Jennifer</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Futreal</LastName><ForeName>Andrew P</ForeName><Initials>AP</Initials><AffiliationInfo><Affiliation>Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Schadt</LastName><ForeName>Christopher W</ForeName><Initials>CW</Initials><AffiliationInfo><Affiliation>Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Microbiology, University of Tennessee, Knoxville, Knoxville, TN, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zhang</LastName><ForeName>Jianhua</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2018</Year><Month>03</Month><Day>07</Day></ArticleDate></Article><MedlineJournalInfo><Country>Switzerland</Country><MedlineTA>Front 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|texte= Linking Associations of Rare Low-Abundance Species to Their Environments by Association Networks.
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