Efficient Identification of Transcription Factor Binding Sites with a Graph Theoretic Approach
Identifying transcription factor binding sites with experimental methods is often expensive and time consuming. Although many computational approaches and tools have been developed for this problem, the prediction accuracy is not satisfactory. In this paper, we develop a new computational approach t...
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| Format: | Online |
| Language: | English |
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Hindawi Publishing Corporation
2013
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3549379/ |
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pubmed-35493792013-01-30 Efficient Identification of Transcription Factor Binding Sites with a Graph Theoretic Approach Song, Jia Xu, Li Sun, Hong Research Article Identifying transcription factor binding sites with experimental methods is often expensive and time consuming. Although many computational approaches and tools have been developed for this problem, the prediction accuracy is not satisfactory. In this paper, we develop a new computational approach that can model the relationships among all short sequence segments in the promoter regions with a graph theoretic model. Based on this model, finding the locations of transcription factor binding site is reduced to computing maximum weighted cliques in a graph with weighted edges. We have implemented this approach and used it to predict the binding sites in two organisms, Caenorhabditis elegans and mus musculus. We compared the prediction accuracy with that of the Gibbs Motif Sampler. We found that the accuracy of our approach is higher than or comparable with that of the Gibbs Motif Sampler for most of tested data and can accurately identify binding sites in cases where the Gibbs Motif Sampler has difficulty to predict their locations. Hindawi Publishing Corporation 2013 2013-01-03 /pmc/articles/PMC3549379/ /pubmed/23365625 http://dx.doi.org/10.1155/2013/856281 Text en Copyright © 2013 Jia Song et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Song, Jia Xu, Li Sun, Hong |
| spellingShingle |
Song, Jia Xu, Li Sun, Hong Efficient Identification of Transcription Factor Binding Sites with a Graph Theoretic Approach |
| author_facet |
Song, Jia Xu, Li Sun, Hong |
| author_sort |
Song, Jia |
| title |
Efficient Identification of Transcription Factor Binding Sites with a Graph Theoretic Approach |
| title_short |
Efficient Identification of Transcription Factor Binding Sites with a Graph Theoretic Approach |
| title_full |
Efficient Identification of Transcription Factor Binding Sites with a Graph Theoretic Approach |
| title_fullStr |
Efficient Identification of Transcription Factor Binding Sites with a Graph Theoretic Approach |
| title_full_unstemmed |
Efficient Identification of Transcription Factor Binding Sites with a Graph Theoretic Approach |
| title_sort |
efficient identification of transcription factor binding sites with a graph theoretic approach |
| description |
Identifying transcription factor binding sites with experimental methods is often expensive and time consuming. Although many computational approaches and tools have been developed for this problem, the prediction accuracy is not satisfactory. In this paper, we develop a new computational approach that can model the relationships among all short sequence segments in the promoter regions with a graph theoretic model. Based on this model, finding the locations of transcription factor binding site is reduced to computing maximum weighted cliques in a graph with weighted edges. We have implemented this approach and used it to predict the binding sites in two organisms, Caenorhabditis elegans and mus musculus. We compared the prediction accuracy with that of the Gibbs Motif Sampler. We found that the accuracy of our approach is higher than or comparable with that of the Gibbs Motif Sampler for most of tested data and can accurately identify binding sites in cases where the Gibbs Motif Sampler has difficulty to predict their locations. |
| publisher |
Hindawi Publishing Corporation |
| publishDate |
2013 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3549379/ |
| _version_ |
1611948237992755200 |