Learning "graph-mer" motifs that predict gene expression trajectories in development.
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 profi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2010-04-01
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| Series: | PLoS Computational Biology |
| Online Access: | http://europepmc.org/articles/PMC2861633?pdf=render |
| _version_ | 1828300246319366144 |
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| author | Xuejing Li Casandra Panea Chris H Wiggins Valerie Reinke Christina Leslie |
| author_facet | Xuejing Li Casandra Panea Chris H Wiggins Valerie Reinke Christina Leslie |
| author_sort | Xuejing Li |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T13:06:41Z |
| format | Article |
| id | doaj.art-8f9b13f146d9491184cfa67bc4c4e84c |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-13T13:06:41Z |
| publishDate | 2010-04-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-8f9b13f146d9491184cfa67bc4c4e84c2022-12-22T02:45:45ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582010-04-0164e100076110.1371/journal.pcbi.1000761Learning "graph-mer" motifs that predict gene expression trajectories in development.Xuejing LiCasandra PaneaChris H WigginsValerie ReinkeChristina LeslieA 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.http://europepmc.org/articles/PMC2861633?pdf=render |
| spellingShingle | Xuejing Li Casandra Panea Chris H Wiggins Valerie Reinke Christina Leslie Learning "graph-mer" motifs that predict gene expression trajectories in development. PLoS Computational Biology |
| title | Learning "graph-mer" motifs that predict gene expression trajectories in development. |
| title_full | Learning "graph-mer" motifs that predict gene expression trajectories in development. |
| title_fullStr | Learning "graph-mer" motifs that predict gene expression trajectories in development. |
| title_full_unstemmed | Learning "graph-mer" motifs that predict gene expression trajectories in development. |
| title_short | Learning "graph-mer" motifs that predict gene expression trajectories in development. |
| title_sort | learning graph mer motifs that predict gene expression trajectories in development |
| url | http://europepmc.org/articles/PMC2861633?pdf=render |
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