MersV1, PubMed, Corpus, bibRecord, 001F52

Learning "graph-mer" motifs that predict gene expression trajectories in development.

Identifieur interne : 001F52 ( PubMed/Corpus ); précédent : 001F51; suivant : 001F53

Learning "graph-mer" motifs that predict gene expression trajectories in development.

Auteurs : Xuejing Li ; Casandra Panea ; Chris H. Wiggins ; Valerie Reinke ; Christina Leslie

Source :

PLoS computational biology [ 1553-7358 ] ; 2010.

RBID : pubmed:20454681

English descriptors

KwdEn :
- Algorithms, Animals, Caenorhabditis elegans (genetics), Gene Expression Profiling (methods), Least-Squares Analysis, Male, Multigene Family, Multivariate Analysis, Oocytes, Principal Component Analysis, Promoter Regions, Genetic, Regression Analysis, Reproducibility of Results, Spermatozoa.
MESH :
- genetics : Caenorhabditis elegans.
- methods : Gene Expression Profiling.
- Algorithms, Animals, Least-Squares Analysis, Male, Multigene Family, Multivariate Analysis, Oocytes, Principal Component Analysis, Promoter Regions, Genetic, Regression Analysis, Reproducibility of Results, Spermatozoa.

Abstract

A key problem in understanding transcriptional regulatory networks is deciphering what cis 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 cis 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 k-mers, or "graph-mers"--that predict gene expression trajectories. Applying the approach to wildtype germline development in Caenorhabditis elegans, 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 in situ 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.

DOI: 10.1371/journal.pcbi.1000761
PubMed: 20454681

Links to Exploration step

pubmed:20454681

Le document en format XML

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<author><name sortKey="Li, Xuejing" sort="Li, Xuejing" uniqKey="Li X" first="Xuejing" last="Li">Xuejing Li</name>
<affiliation><nlm:affiliation>Department of Physics, Columbia University, New York, New York, United States of America.</nlm:affiliation>
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<author><name sortKey="Panea, Casandra" sort="Panea, Casandra" uniqKey="Panea C" first="Casandra" last="Panea">Casandra Panea</name>
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<author><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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<author><name sortKey="Reinke, Valerie" sort="Reinke, Valerie" uniqKey="Reinke V" first="Valerie" last="Reinke">Valerie Reinke</name>
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<front><div type="abstract" xml:lang="en">A key problem in understanding transcriptional regulatory networks is deciphering what cis 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 cis 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 k-mers, or "graph-mers"--that predict gene expression trajectories. Applying the approach to wildtype germline development in Caenorhabditis elegans, 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 in situ 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.</div>
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Learning "graph-mer" motifs that predict gene expression trajectories in development.

Learning "graph-mer" motifs that predict gene expression trajectories in development.

Source :

English descriptors

Abstract

Links to Exploration step

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri

Pour générer des pages wiki