cDREM: inferring dynamic combinatorial gene regulation.
Identifieur interne : 000430 ( PubMed/Corpus ); précédent : 000429; suivant : 000431cDREM: inferring dynamic combinatorial gene regulation.
Auteurs : Aaron Wise ; Ziv Bar-JosephSource :
- Journal of computational biology : a journal of computational molecular cell biology [ 1557-8666 ] ; 2015.
English descriptors
- KwdEn :
- Chromatin Immunoprecipitation, Gene Expression Profiling, Gene Expression Regulation, Gene Ontology, Gene Regulatory Networks, Humans, Influenza, Human (immunology), Influenza, Human (metabolism), Markov Chains, Models, Genetic, Saccharomyces cerevisiae (genetics), Saccharomyces cerevisiae (metabolism), Saccharomyces cerevisiae Proteins (genetics), Saccharomyces cerevisiae Proteins (metabolism), Software, Stress, Physiological, Transcription Factors (genetics), Transcription Factors (metabolism).
- MESH :
- chemical , genetics : Saccharomyces cerevisiae Proteins, Transcription Factors.
- genetics : Saccharomyces cerevisiae.
- immunology : Influenza, Human.
- metabolism : Influenza, Human, Saccharomyces cerevisiae, Saccharomyces cerevisiae Proteins, Transcription Factors.
- Chromatin Immunoprecipitation, Gene Expression Profiling, Gene Expression Regulation, Gene Ontology, Gene Regulatory Networks, Humans, Markov Chains, Models, Genetic, Software, Stress, Physiological.
Abstract
Genes are often combinatorially regulated by multiple transcription factors (TFs). Such combinatorial regulation plays an important role in development and facilitates the ability of cells to respond to different stresses. While a number of approaches have utilized sequence and ChIP-based datasets to study combinational regulation, these have often ignored the combinational logic and the dynamics associated with such regulation. Here we present cDREM, a new method for reconstructing dynamic models of combinatorial regulation. cDREM integrates time series gene expression data with (static) protein interaction data. The method is based on a hidden Markov model and utilizes the sparse group Lasso to identify small subsets of combinatorially active TFs, their time of activation, and the logical function they implement. We tested cDREM on yeast and human data sets. Using yeast we show that the predicted combinatorial sets agree with other high throughput genomic datasets and improve upon prior methods developed to infer combinatorial regulation. Applying cDREM to study human response to flu, we were able to identify several combinatorial TF sets, some of which were known to regulate immune response while others represent novel combinations of important TFs.
DOI: 10.1089/cmb.2015.0010
PubMed: 25844671
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pubmed:25844671Le document en format XML
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Such combinatorial regulation plays an important role in development and facilitates the ability of cells to respond to different stresses. While a number of approaches have utilized sequence and ChIP-based datasets to study combinational regulation, these have often ignored the combinational logic and the dynamics associated with such regulation. Here we present cDREM, a new method for reconstructing dynamic models of combinatorial regulation. cDREM integrates time series gene expression data with (static) protein interaction data. The method is based on a hidden Markov model and utilizes the sparse group Lasso to identify small subsets of combinatorially active TFs, their time of activation, and the logical function they implement. We tested cDREM on yeast and human data sets. Using yeast we show that the predicted combinatorial sets agree with other high throughput genomic datasets and improve upon prior methods developed to infer combinatorial regulation. Applying cDREM to study human response to flu, we were able to identify several combinatorial TF sets, some of which were known to regulate immune response while others represent novel combinations of important TFs. </div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">25844671</PMID><DateCompleted><Year>2016</Year><Month>01</Month><Day>04</Day></DateCompleted><DateRevised><Year>2018</Year><Month>11</Month><Day>13</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1557-8666</ISSN><JournalIssue CitedMedium="Internet"><Volume>22</Volume><Issue>4</Issue><PubDate><Year>2015</Year><Month>Apr</Month></PubDate></JournalIssue><Title>Journal of computational biology : a journal of computational molecular cell biology</Title><ISOAbbreviation>J. Comput. Biol.</ISOAbbreviation></Journal><ArticleTitle>cDREM: inferring dynamic combinatorial gene regulation.</ArticleTitle><Pagination><MedlinePgn>324-33</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1089/cmb.2015.0010</ELocationID><Abstract><AbstractText>Genes are often combinatorially regulated by multiple transcription factors (TFs). Such combinatorial regulation plays an important role in development and facilitates the ability of cells to respond to different stresses. While a number of approaches have utilized sequence and ChIP-based datasets to study combinational regulation, these have often ignored the combinational logic and the dynamics associated with such regulation. Here we present cDREM, a new method for reconstructing dynamic models of combinatorial regulation. cDREM integrates time series gene expression data with (static) protein interaction data. The method is based on a hidden Markov model and utilizes the sparse group Lasso to identify small subsets of combinatorially active TFs, their time of activation, and the logical function they implement. We tested cDREM on yeast and human data sets. Using yeast we show that the predicted combinatorial sets agree with other high throughput genomic datasets and improve upon prior methods developed to infer combinatorial regulation. Applying cDREM to study human response to flu, we were able to identify several combinatorial TF sets, some of which were known to regulate immune response while others represent novel combinations of important TFs. </AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Wise</LastName><ForeName>Aaron</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Lane Center for Computational Biology, Carnegie Mellon University , Pittsburgh, Pennsylvania.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Bar-Joseph</LastName><ForeName>Ziv</ForeName><Initials>Z</Initials></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>U54 HL127624</GrantID><Acronym>HL</Acronym><Agency>NHLBI NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>1 U54 HL127624-01</GrantID><Acronym>HL</Acronym><Agency>NHLBI NIH HHS</Agency><Country>United 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