cDREM: inferring dynamic combinatorial gene regulation.
Identifieur interne : 000C95 ( Main/Exploration ); précédent : 000C94; suivant : 000C96cDREM: inferring dynamic combinatorial gene regulation.
Auteurs : Aaron Wise [États-Unis] ; Ziv Bar-JosephSource :
- Journal of computational biology : a journal of computational molecular cell biology [ 1557-8666 ] ; 2015.
Descripteurs français
- KwdFr :
- Analyse de profil d'expression de gènes, Chaines de Markov, Facteurs de transcription (génétique), Facteurs de transcription (métabolisme), Gene Ontology, Grippe humaine (immunologie), Grippe humaine (métabolisme), Humains, Immunoprécipitation de la chromatine, Logiciel, Modèles génétiques, Protéines de Saccharomyces cerevisiae (génétique), Protéines de Saccharomyces cerevisiae (métabolisme), Régulation de l'expression des gènes, Réseaux de régulation génique, Saccharomyces cerevisiae (génétique), Saccharomyces cerevisiae (métabolisme), Stress physiologique.
- MESH :
- génétique : Facteurs de transcription, Protéines de Saccharomyces cerevisiae, Saccharomyces cerevisiae.
- immunologie : Grippe humaine.
- métabolisme : Facteurs de transcription, Grippe humaine, Protéines de Saccharomyces cerevisiae, Saccharomyces cerevisiae.
- Analyse de profil d'expression de gènes, Chaines de Markov, Gene Ontology, Humains, Immunoprécipitation de la chromatine, Logiciel, Modèles génétiques, Régulation de l'expression des gènes, Réseaux de régulation génique, Stress physiologique.
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
Affiliations:
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Le document en format XML
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first="Aaron" last="Wise">Aaron Wise</name><affiliation wicri:level="2"><nlm:affiliation>Lane Center for Computational Biology, Carnegie Mellon University , Pittsburgh, Pennsylvania.</nlm:affiliation><country xml:lang="fr">États-Unis</country><placeName><region type="state">Pennsylvanie</region></placeName><wicri:cityArea>Lane Center for Computational Biology, Carnegie Mellon University , Pittsburgh</wicri:cityArea></affiliation></author><author><name sortKey="Bar Joseph, Ziv" sort="Bar Joseph, Ziv" uniqKey="Bar Joseph Z" first="Ziv" last="Bar-Joseph">Ziv Bar-Joseph</name></author></analytic><series><title level="j">Journal of computational biology : a journal of computational molecular cell biology</title><idno type="eISSN">1557-8666</idno><imprint><date when="2015" type="published">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Chromatin Immunoprecipitation</term><term>Gene Expression Profiling</term><term>Gene Expression Regulation</term><term>Gene Ontology</term><term>Gene Regulatory Networks</term><term>Humans</term><term>Influenza, Human (immunology)</term><term>Influenza, Human (metabolism)</term><term>Markov Chains</term><term>Models, Genetic</term><term>Saccharomyces cerevisiae (genetics)</term><term>Saccharomyces cerevisiae (metabolism)</term><term>Saccharomyces cerevisiae Proteins (genetics)</term><term>Saccharomyces cerevisiae Proteins (metabolism)</term><term>Software</term><term>Stress, Physiological</term><term>Transcription Factors (genetics)</term><term>Transcription Factors (metabolism)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Analyse de profil d'expression de gènes</term><term>Chaines de Markov</term><term>Facteurs de transcription (génétique)</term><term>Facteurs de transcription (métabolisme)</term><term>Gene Ontology</term><term>Grippe humaine (immunologie)</term><term>Grippe humaine (métabolisme)</term><term>Humains</term><term>Immunoprécipitation de la chromatine</term><term>Logiciel</term><term>Modèles génétiques</term><term>Protéines de Saccharomyces cerevisiae (génétique)</term><term>Protéines de Saccharomyces cerevisiae (métabolisme)</term><term>Régulation de l'expression des gènes</term><term>Réseaux de régulation génique</term><term>Saccharomyces cerevisiae (génétique)</term><term>Saccharomyces cerevisiae (métabolisme)</term><term>Stress physiologique</term></keywords><keywords scheme="MESH" type="chemical" qualifier="genetics" xml:lang="en"><term>Saccharomyces cerevisiae Proteins</term><term>Transcription Factors</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Saccharomyces cerevisiae</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Facteurs de transcription</term><term>Protéines de Saccharomyces cerevisiae</term><term>Saccharomyces cerevisiae</term></keywords><keywords scheme="MESH" qualifier="immunologie" xml:lang="fr"><term>Grippe humaine</term></keywords><keywords scheme="MESH" qualifier="immunology" xml:lang="en"><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="metabolism" xml:lang="en"><term>Influenza, Human</term><term>Saccharomyces cerevisiae</term><term>Saccharomyces cerevisiae Proteins</term><term>Transcription Factors</term></keywords><keywords scheme="MESH" qualifier="métabolisme" xml:lang="fr"><term>Facteurs de transcription</term><term>Grippe humaine</term><term>Protéines de Saccharomyces cerevisiae</term><term>Saccharomyces cerevisiae</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Chromatin Immunoprecipitation</term><term>Gene Expression Profiling</term><term>Gene Expression Regulation</term><term>Gene Ontology</term><term>Gene Regulatory Networks</term><term>Humans</term><term>Markov Chains</term><term>Models, Genetic</term><term>Software</term><term>Stress, Physiological</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Analyse de profil d'expression de gènes</term><term>Chaines de Markov</term><term>Gene Ontology</term><term>Humains</term><term>Immunoprécipitation de la chromatine</term><term>Logiciel</term><term>Modèles génétiques</term><term>Régulation de l'expression des gènes</term><term>Réseaux de régulation génique</term><term>Stress physiologique</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">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. </div></front></TEI><affiliations><list><country><li>États-Unis</li></country><region><li>Pennsylvanie</li></region></list><tree><noCountry><name sortKey="Bar Joseph, Ziv" sort="Bar Joseph, Ziv" uniqKey="Bar Joseph Z" first="Ziv" last="Bar-Joseph">Ziv Bar-Joseph</name></noCountry><country name="États-Unis"><region name="Pennsylvanie"><name sortKey="Wise, Aaron" sort="Wise, Aaron" uniqKey="Wise A" first="Aaron" last="Wise">Aaron Wise</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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