A multi-Poisson dynamic mixture model to cluster developmental patterns of gene expression by RNA-seq.
Identifieur interne : 001F16 ( Main/Exploration ); précédent : 001F15; suivant : 001F17A multi-Poisson dynamic mixture model to cluster developmental patterns of gene expression by RNA-seq.
Auteurs : Meixia Ye ; Zhong Wang ; Yaqun Wang ; Rongling WuSource :
- Briefings in bioinformatics [ 1477-4054 ] ; 2015.
Descripteurs français
- KwdFr :
- ARN des plantes (génétique), Algorithmes (MeSH), Analyse de profil d'expression de gènes (statistiques et données numériques), Analyse de regroupements (MeSH), Analyse de séquence d'ARN (statistiques et données numériques), Biologie informatique (MeSH), Famille multigénique (MeSH), Gènes de plante (MeSH), Loi de Poisson (MeSH), Modèles génétiques (MeSH), Modèles statistiques (MeSH), Populus (croissance et développement), Populus (génétique), Régulation de l'expression des gènes au cours du développement (MeSH), Régulation de l'expression des gènes végétaux (MeSH), Simulation numérique (MeSH), Séquençage nucléotidique à haut débit (statistiques et données numériques).
- MESH :
- croissance et développement : Populus.
- génétique : ARN des plantes, Populus.
- statistiques et données numériques : Analyse de profil d'expression de gènes, Analyse de séquence d'ARN, Séquençage nucléotidique à haut débit.
- Algorithmes, Analyse de regroupements, Biologie informatique, Famille multigénique, Gènes de plante, Loi de Poisson, Modèles génétiques, Modèles statistiques, Régulation de l'expression des gènes au cours du développement, Régulation de l'expression des gènes végétaux, Simulation numérique.
English descriptors
- KwdEn :
- Algorithms (MeSH), Cluster Analysis (MeSH), Computational Biology (MeSH), Computer Simulation (MeSH), Gene Expression Profiling (statistics & numerical data), Gene Expression Regulation, Developmental (MeSH), Gene Expression Regulation, Plant (MeSH), Genes, Plant (MeSH), High-Throughput Nucleotide Sequencing (statistics & numerical data), Models, Genetic (MeSH), Models, Statistical (MeSH), Multigene Family (MeSH), Poisson Distribution (MeSH), Populus (genetics), Populus (growth & development), RNA, Plant (genetics), Sequence Analysis, RNA (statistics & numerical data).
- MESH :
- chemical , genetics : RNA, Plant.
- genetics : Populus.
- growth & development : Populus.
- statistics & numerical data : Gene Expression Profiling, High-Throughput Nucleotide Sequencing, Sequence Analysis, RNA.
- Algorithms, Cluster Analysis, Computational Biology, Computer Simulation, Gene Expression Regulation, Developmental, Gene Expression Regulation, Plant, Genes, Plant, Models, Genetic, Models, Statistical, Multigene Family, Poisson Distribution.
Abstract
Dynamic changes of gene expression reflect an intrinsic mechanism of how an organism responds to developmental and environmental signals. With the increasing availability of expression data across a time-space scale by RNA-seq, the classification of genes as per their biological function using RNA-seq data has become one of the most significant challenges in contemporary biology. Here we develop a clustering mixture model to discover distinct groups of genes expressed during a period of organ development. By integrating the density function of multivariate Poisson distribution, the model accommodates the discrete property of read counts characteristic of RNA-seq data. The temporal dependence of gene expression is modeled by the first-order autoregressive process. The model is implemented with the Expectation-Maximization algorithm and model selection to determine the optimal number of gene clusters and obtain the estimates of Poisson parameters that describe the pattern of time-dependent expression of genes from each cluster. The model has been demonstrated by analyzing a real data from an experiment aimed to link the pattern of gene expression to catkin development in white poplar. The usefulness of the model has been validated through computer simulation. The model provides a valuable tool for clustering RNA-seq data, facilitating our global view of expression dynamics and understanding of gene regulation mechanisms.
DOI: 10.1093/bib/bbu013
PubMed: 24817567
Affiliations:
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scheme="KwdFr" xml:lang="fr"><term>ARN des plantes (génétique)</term><term>Algorithmes (MeSH)</term><term>Analyse de profil d'expression de gènes (statistiques et données numériques)</term><term>Analyse de regroupements (MeSH)</term><term>Analyse de séquence d'ARN (statistiques et données numériques)</term><term>Biologie informatique (MeSH)</term><term>Famille multigénique (MeSH)</term><term>Gènes de plante (MeSH)</term><term>Loi de Poisson (MeSH)</term><term>Modèles génétiques (MeSH)</term><term>Modèles statistiques (MeSH)</term><term>Populus (croissance et développement)</term><term>Populus (génétique)</term><term>Régulation de l'expression des gènes au cours du développement (MeSH)</term><term>Régulation de l'expression des gènes végétaux (MeSH)</term><term>Simulation numérique (MeSH)</term><term>Séquençage nucléotidique à haut débit (statistiques et données numériques)</term></keywords><keywords scheme="MESH" type="chemical" qualifier="genetics" xml:lang="en"><term>RNA, Plant</term></keywords><keywords scheme="MESH" qualifier="croissance et développement" xml:lang="fr"><term>Populus</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Populus</term></keywords><keywords scheme="MESH" qualifier="growth & development" xml:lang="en"><term>Populus</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>ARN des plantes</term><term>Populus</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Gene Expression Profiling</term><term>High-Throughput Nucleotide Sequencing</term><term>Sequence Analysis, RNA</term></keywords><keywords scheme="MESH" qualifier="statistiques et données numériques" xml:lang="fr"><term>Analyse de profil d'expression de gènes</term><term>Analyse de séquence d'ARN</term><term>Séquençage nucléotidique à haut débit</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Cluster Analysis</term><term>Computational Biology</term><term>Computer Simulation</term><term>Gene Expression Regulation, Developmental</term><term>Gene Expression Regulation, Plant</term><term>Genes, Plant</term><term>Models, Genetic</term><term>Models, Statistical</term><term>Multigene Family</term><term>Poisson Distribution</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Analyse de regroupements</term><term>Biologie informatique</term><term>Famille multigénique</term><term>Gènes de plante</term><term>Loi de Poisson</term><term>Modèles génétiques</term><term>Modèles statistiques</term><term>Régulation de l'expression des gènes au cours du développement</term><term>Régulation de l'expression des gènes végétaux</term><term>Simulation numérique</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Dynamic changes of gene expression reflect an intrinsic mechanism of how an organism responds to developmental and environmental signals. With the increasing availability of expression data across a time-space scale by RNA-seq, the classification of genes as per their biological function using RNA-seq data has become one of the most significant challenges in contemporary biology. Here we develop a clustering mixture model to discover distinct groups of genes expressed during a period of organ development. By integrating the density function of multivariate Poisson distribution, the model accommodates the discrete property of read counts characteristic of RNA-seq data. The temporal dependence of gene expression is modeled by the first-order autoregressive process. The model is implemented with the Expectation-Maximization algorithm and model selection to determine the optimal number of gene clusters and obtain the estimates of Poisson parameters that describe the pattern of time-dependent expression of genes from each cluster. The model has been demonstrated by analyzing a real data from an experiment aimed to link the pattern of gene expression to catkin development in white poplar. The usefulness of the model has been validated through computer simulation. The model provides a valuable tool for clustering RNA-seq data, facilitating our global view of expression dynamics and understanding of gene regulation mechanisms. </div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">24817567</PMID><DateCompleted><Year>2016</Year><Month>04</Month><Day>18</Day></DateCompleted><DateRevised><Year>2015</Year><Month>03</Month><Day>19</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1477-4054</ISSN><JournalIssue CitedMedium="Internet"><Volume>16</Volume><Issue>2</Issue><PubDate><Year>2015</Year><Month>Mar</Month></PubDate></JournalIssue><Title>Briefings in bioinformatics</Title><ISOAbbreviation>Brief Bioinform</ISOAbbreviation></Journal><ArticleTitle>A multi-Poisson dynamic mixture model to cluster developmental patterns of gene expression by RNA-seq.</ArticleTitle><Pagination><MedlinePgn>205-15</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1093/bib/bbu013</ELocationID><Abstract><AbstractText>Dynamic changes of gene expression reflect an intrinsic mechanism of how an organism responds to developmental and environmental signals. With the increasing availability of expression data across a time-space scale by RNA-seq, the classification of genes as per their biological function using RNA-seq data has become one of the most significant challenges in contemporary biology. Here we develop a clustering mixture model to discover distinct groups of genes expressed during a period of organ development. By integrating the density function of multivariate Poisson distribution, the model accommodates the discrete property of read counts characteristic of RNA-seq data. The temporal dependence of gene expression is modeled by the first-order autoregressive process. The model is implemented with the Expectation-Maximization algorithm and model selection to determine the optimal number of gene clusters and obtain the estimates of Poisson parameters that describe the pattern of time-dependent expression of genes from each cluster. The model has been demonstrated by analyzing a real data from an experiment aimed to link the pattern of gene expression to catkin development in white poplar. The usefulness of the model has been validated through computer simulation. The model provides a valuable tool for clustering RNA-seq data, facilitating our global view of expression dynamics and understanding of gene regulation mechanisms. </AbstractText><CopyrightInformation>© The Author 2014. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Ye</LastName><ForeName>Meixia</ForeName><Initials>M</Initials></Author><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Zhong</ForeName><Initials>Z</Initials></Author><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Yaqun</ForeName><Initials>Y</Initials></Author><Author ValidYN="Y"><LastName>Wu</LastName><ForeName>Rongling</ForeName><Initials>R</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType><PublicationType UI="D013486">Research Support, U.S. Gov't, Non-P.H.S.</PublicationType></PublicationTypeList><ArticleDate 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model</Keyword><Keyword MajorTopicYN="N">multivariate poisson</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2014</Year><Month>5</Month><Day>13</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2014</Year><Month>5</Month><Day>13</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2016</Year><Month>4</Month><Day>19</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">24817567</ArticleId><ArticleId IdType="pii">bbu013</ArticleId><ArticleId IdType="doi">10.1093/bib/bbu013</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list></list><tree><noCountry><name sortKey="Wang, Yaqun" sort="Wang, Yaqun" uniqKey="Wang Y" first="Yaqun" last="Wang">Yaqun Wang</name><name sortKey="Wang, Zhong" sort="Wang, Zhong" uniqKey="Wang Z" first="Zhong" last="Wang">Zhong Wang</name><name sortKey="Wu, Rongling" sort="Wu, Rongling" uniqKey="Wu R" first="Rongling" last="Wu">Rongling Wu</name><name sortKey="Ye, Meixia" sort="Ye, Meixia" uniqKey="Ye M" first="Meixia" last="Ye">Meixia Ye</name></noCountry></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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