An integrated approach to identifying cis-regulatory modules in the human genome.
In eukaryotic genomes, it is challenging to accurately determine target sites of transcription factors (TFs) by only using sequence information. Previous efforts were made to tackle this task by considering the fact that TF binding sites tend to be more conserved than other functional sites and the...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2009-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC2677454?pdf=render |
| _version_ | 1828375419935522816 |
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| author | Kyoung-Jae Won Saurabh Agarwal Li Shen Robert Shoemaker Bing Ren Wei Wang |
| author_facet | Kyoung-Jae Won Saurabh Agarwal Li Shen Robert Shoemaker Bing Ren Wei Wang |
| author_sort | Kyoung-Jae Won |
| collection | DOAJ |
| description | In eukaryotic genomes, it is challenging to accurately determine target sites of transcription factors (TFs) by only using sequence information. Previous efforts were made to tackle this task by considering the fact that TF binding sites tend to be more conserved than other functional sites and the binding sites of several TFs are often clustered. Recently, ChIP-chip and ChIP-sequencing experiments have been accumulated to identify TF binding sites as well as survey the chromatin modification patterns at the regulatory elements such as promoters and enhancers. We propose here a hidden Markov model (HMM) to incorporate sequence motif information, TF-DNA interaction data and chromatin modification patterns to precisely identify cis-regulatory modules (CRMs). We conducted ChIP-chip experiments on four TFs, CREB, E2F1, MAX, and YY1 in 1% of the human genome. We then trained a hidden Markov model (HMM) to identify the labels of the CRMs by incorporating the sequence motifs recognized by these TFs and the ChIP-chip ratio. Chromatin modification data was used to predict the functional sites and to further remove false positives. Cross-validation showed that our integrated HMM had a performance superior to other existing methods on predicting CRMs. Incorporating histone signature information successfully penalized false prediction and improved the whole performance. The dataset we used and the software are available at http://nash.ucsd.edu/CIS/. |
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| format | Article |
| id | doaj.art-b6a527048043436ba4e25cba2bbbb5b2 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-14T07:46:24Z |
| publishDate | 2009-01-01 |
| publisher | Public Library of Science (PLoS) |
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| series | PLoS ONE |
| spelling | doaj.art-b6a527048043436ba4e25cba2bbbb5b22022-12-22T02:05:19ZengPublic Library of Science (PLoS)PLoS ONE1932-62032009-01-0145e550110.1371/journal.pone.0005501An integrated approach to identifying cis-regulatory modules in the human genome.Kyoung-Jae WonSaurabh AgarwalLi ShenRobert ShoemakerBing RenWei WangIn eukaryotic genomes, it is challenging to accurately determine target sites of transcription factors (TFs) by only using sequence information. Previous efforts were made to tackle this task by considering the fact that TF binding sites tend to be more conserved than other functional sites and the binding sites of several TFs are often clustered. Recently, ChIP-chip and ChIP-sequencing experiments have been accumulated to identify TF binding sites as well as survey the chromatin modification patterns at the regulatory elements such as promoters and enhancers. We propose here a hidden Markov model (HMM) to incorporate sequence motif information, TF-DNA interaction data and chromatin modification patterns to precisely identify cis-regulatory modules (CRMs). We conducted ChIP-chip experiments on four TFs, CREB, E2F1, MAX, and YY1 in 1% of the human genome. We then trained a hidden Markov model (HMM) to identify the labels of the CRMs by incorporating the sequence motifs recognized by these TFs and the ChIP-chip ratio. Chromatin modification data was used to predict the functional sites and to further remove false positives. Cross-validation showed that our integrated HMM had a performance superior to other existing methods on predicting CRMs. Incorporating histone signature information successfully penalized false prediction and improved the whole performance. The dataset we used and the software are available at http://nash.ucsd.edu/CIS/.http://europepmc.org/articles/PMC2677454?pdf=render |
| spellingShingle | Kyoung-Jae Won Saurabh Agarwal Li Shen Robert Shoemaker Bing Ren Wei Wang An integrated approach to identifying cis-regulatory modules in the human genome. PLoS ONE |
| title | An integrated approach to identifying cis-regulatory modules in the human genome. |
| title_full | An integrated approach to identifying cis-regulatory modules in the human genome. |
| title_fullStr | An integrated approach to identifying cis-regulatory modules in the human genome. |
| title_full_unstemmed | An integrated approach to identifying cis-regulatory modules in the human genome. |
| title_short | An integrated approach to identifying cis-regulatory modules in the human genome. |
| title_sort | integrated approach to identifying cis regulatory modules in the human genome |
| url | http://europepmc.org/articles/PMC2677454?pdf=render |
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