Learning “graph-mer” Motifs that Predict Gene Expression Trajectories in Development
Identifieur interne : 002625 ( Main/Exploration ); précédent : 002624; suivant : 002626Learning “graph-mer” Motifs that Predict Gene Expression Trajectories in Development
Auteurs : Xuejing Li [États-Unis] ; Casandra Panea [États-Unis] ; Chris H. Wiggins [États-Unis] ; Valerie Reinke [États-Unis] ; Christina Leslie [États-Unis]Source :
- PLoS Computational Biology [ 1553-734X ] ; 2010.
Descripteurs français
- KwdFr :
- Algorithmes, Analyse de profil d'expression de gènes (), Analyse de régression, Analyse en composantes principales, Analyse multivariée, Animaux, Caenorhabditis elegans (génétique), Famille multigénique, Mâle, Méthode des moindres carrés, Ovocytes, Reproductibilité des résultats, Régions promotrices (génétique), Spermatozoïdes.
- MESH :
- génétique : Caenorhabditis elegans.
- Algorithmes, Analyse de profil d'expression de gènes, Analyse de régression, Analyse en composantes principales, Analyse multivariée, Animaux, Famille multigénique, Mâle, Méthode des moindres carrés, Ovocytes, Reproductibilité des résultats, Régions promotrices (génétique), Spermatozoïdes.
English descriptors
- KwdEn :
- MESH :
Abstract
A key problem in understanding transcriptional regulatory networks is deciphering what
Url:
DOI: 10.1371/journal.pcbi.1000761
PubMed: 20454681
PubMed Central: 2861633
Affiliations:
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Le document en format XML
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<term>Animals</term>
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<term>Gene Expression Profiling (methods)</term>
<term>Least-Squares Analysis</term>
<term>Male</term>
<term>Multigene Family</term>
<term>Multivariate Analysis</term>
<term>Oocytes</term>
<term>Principal Component Analysis</term>
<term>Promoter Regions, Genetic</term>
<term>Regression Analysis</term>
<term>Reproducibility of Results</term>
<term>Spermatozoa</term>
</keywords>
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<term>Analyse de profil d'expression de gènes ()</term>
<term>Analyse de régression</term>
<term>Analyse en composantes principales</term>
<term>Analyse multivariée</term>
<term>Animaux</term>
<term>Caenorhabditis elegans (génétique)</term>
<term>Famille multigénique</term>
<term>Mâle</term>
<term>Méthode des moindres carrés</term>
<term>Ovocytes</term>
<term>Reproductibilité des résultats</term>
<term>Régions promotrices (génétique)</term>
<term>Spermatozoïdes</term>
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<keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Gene Expression Profiling</term>
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<keywords scheme="MESH" xml:lang="en"><term>Algorithms</term>
<term>Animals</term>
<term>Least-Squares Analysis</term>
<term>Male</term>
<term>Multigene Family</term>
<term>Multivariate Analysis</term>
<term>Oocytes</term>
<term>Principal Component Analysis</term>
<term>Promoter Regions, Genetic</term>
<term>Regression Analysis</term>
<term>Reproducibility of Results</term>
<term>Spermatozoa</term>
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<term>Analyse de profil d'expression de gènes</term>
<term>Analyse de régression</term>
<term>Analyse en composantes principales</term>
<term>Analyse multivariée</term>
<term>Animaux</term>
<term>Famille multigénique</term>
<term>Mâle</term>
<term>Méthode des moindres carrés</term>
<term>Ovocytes</term>
<term>Reproductibilité des résultats</term>
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<front><div type="abstract" xml:lang="en"><p>A key problem in understanding transcriptional regulatory networks is deciphering what <italic>cis</italic>
regulatory logic is encoded in gene promoter sequences and how this sequence information maps to expression. A typical computational approach to this problem involves clustering genes by their expression profiles and then searching for overrepresented motifs in the promoter sequences of genes in a cluster. However, genes with similar expression profiles may be controlled by distinct regulatory programs. Moreover, if many gene expression profiles in a data set are highly correlated, as in the case of whole organism developmental time series, it may be difficult to resolve fine-grained clusters in the first place. We present a predictive framework for modeling the natural flow of information, from promoter sequence to expression, to learn <italic>cis</italic>
regulatory motifs and characterize gene expression patterns in developmental time courses. We introduce a cluster-free algorithm based on a graph-regularized version of partial least squares (PLS) regression to learn sequence patterns—represented by graphs of <italic>k</italic>
-mers, or “graph-mers”—that predict gene expression trajectories. Applying the approach to wildtype germline development in <italic>Caenorhabditis elegans</italic>
, we found that the first and second latent PLS factors mapped to expression profiles for oocyte and sperm genes, respectively. We extracted both known and novel motifs from the graph-mers associated to these germline-specific patterns, including novel CG-rich motifs specific to oocyte genes. We found evidence supporting the functional relevance of these putative regulatory elements through analysis of positional bias, motif conservation and <italic>in situ</italic>
gene expression. This study demonstrates that our regression model can learn biologically meaningful latent structure and identify potentially functional motifs from subtle developmental time course expression data.</p>
</div>
</front>
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<author><name sortKey="Sha, F" uniqKey="Sha F">F Sha</name>
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<affiliations><list><country><li>États-Unis</li>
</country>
<region><li>Connecticut</li>
<li>État de New York</li>
</region>
<settlement><li>New York</li>
</settlement>
<orgName><li>Université Columbia</li>
</orgName>
</list>
<tree><country name="États-Unis"><region name="État de New York"><name sortKey="Li, Xuejing" sort="Li, Xuejing" uniqKey="Li X" first="Xuejing" last="Li">Xuejing Li</name>
</region>
<name sortKey="Leslie, Christina" sort="Leslie, Christina" uniqKey="Leslie C" first="Christina" last="Leslie">Christina Leslie</name>
<name sortKey="Panea, Casandra" sort="Panea, Casandra" uniqKey="Panea C" first="Casandra" last="Panea">Casandra Panea</name>
<name sortKey="Reinke, Valerie" sort="Reinke, Valerie" uniqKey="Reinke V" first="Valerie" last="Reinke">Valerie Reinke</name>
<name sortKey="Wiggins, Chris H" sort="Wiggins, Chris H" uniqKey="Wiggins C" first="Chris H." last="Wiggins">Chris H. Wiggins</name>
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