Predicting the binding preference of transcription factors to individual DNA k-mers
Motivation: Recognition of specific DNA sequences is a central mechanism by which transcription factors (TFs) control gene expression. Many TF-binding preferences, however, are unknown or poorly characterized, in part due to the difficulty associated with determining their specificity experimentally...
| Main Authors: | , , , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Oxford University Press (OUP)
2012
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| Online Access: | http://hdl.handle.net/1721.1/73182 |
| _version_ | 1826201794455273472 |
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| author | Bulyk, Martha L. Philippakis, Anthony A. Alleyne, Trevis M. Peña-Castillo, Lourdes Badis, Gwenael Talukder, Shaheynoor Berger, Michael F. Gehrke, Andrew R. Morris, Quaid D. Hughes, Timothy R. |
| author2 | Harvard University--MIT Division of Health Sciences and Technology |
| author_facet | Harvard University--MIT Division of Health Sciences and Technology Bulyk, Martha L. Philippakis, Anthony A. Alleyne, Trevis M. Peña-Castillo, Lourdes Badis, Gwenael Talukder, Shaheynoor Berger, Michael F. Gehrke, Andrew R. Morris, Quaid D. Hughes, Timothy R. |
| author_sort | Bulyk, Martha L. |
| collection | MIT |
| description | Motivation: Recognition of specific DNA sequences is a central mechanism by which transcription factors (TFs) control gene expression. Many TF-binding preferences, however, are unknown or poorly characterized, in part due to the difficulty associated with determining their specificity experimentally, and an incomplete understanding of the mechanisms governing sequence specificity. New techniques that estimate the affinity of TFs to all possible k-mers provide a new opportunity to study DNA–protein interaction mechanisms, and may facilitate inference of binding preferences for members of a given TF family when such information is available for other family members.
Results: We employed a new dataset consisting of the relative preferences of mouse homeodomains for all eight-base DNA sequences in order to ask how well we can predict the binding profiles of homeodomains when only their protein sequences are given. We evaluated a panel of standard statistical inference techniques, as well as variations of the protein features considered. Nearest neighbour among functionally important residues emerged among the most effective methods. Our results underscore the complexity of TF–DNA recognition, and suggest a rational approach for future analyses of TF families.
Contact: t.hughes@utorotno.ca
Supplementary information: Supplementary data are available at Bioinformatics online. |
| first_indexed | 2024-09-23T11:57:19Z |
| format | Article |
| id | mit-1721.1/73182 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:57:19Z |
| publishDate | 2012 |
| publisher | Oxford University Press (OUP) |
| record_format | dspace |
| spelling | mit-1721.1/731822022-09-27T23:02:36Z Predicting the binding preference of transcription factors to individual DNA k-mers Bulyk, Martha L. Philippakis, Anthony A. Alleyne, Trevis M. Peña-Castillo, Lourdes Badis, Gwenael Talukder, Shaheynoor Berger, Michael F. Gehrke, Andrew R. Morris, Quaid D. Hughes, Timothy R. Harvard University--MIT Division of Health Sciences and Technology Bulyk, Martha L. Philippakis, Anthony A. Motivation: Recognition of specific DNA sequences is a central mechanism by which transcription factors (TFs) control gene expression. Many TF-binding preferences, however, are unknown or poorly characterized, in part due to the difficulty associated with determining their specificity experimentally, and an incomplete understanding of the mechanisms governing sequence specificity. New techniques that estimate the affinity of TFs to all possible k-mers provide a new opportunity to study DNA–protein interaction mechanisms, and may facilitate inference of binding preferences for members of a given TF family when such information is available for other family members. Results: We employed a new dataset consisting of the relative preferences of mouse homeodomains for all eight-base DNA sequences in order to ask how well we can predict the binding profiles of homeodomains when only their protein sequences are given. We evaluated a panel of standard statistical inference techniques, as well as variations of the protein features considered. Nearest neighbour among functionally important residues emerged among the most effective methods. Our results underscore the complexity of TF–DNA recognition, and suggest a rational approach for future analyses of TF families. Contact: t.hughes@utorotno.ca Supplementary information: Supplementary data are available at Bioinformatics online. Canadian Institutes of Health Research Ontario Research Fund National Institutes of Health (U.S.) National Human Genome Research Institute (U.S.) 2012-09-26T14:41:46Z 2012-09-26T14:41:46Z 2008-12 2008-11 Article http://purl.org/eprint/type/JournalArticle 1367-4803 1460-2059 http://hdl.handle.net/1721.1/73182 Alleyne, T. M. et al. “Predicting the Binding Preference of Transcription Factors to Individual DNA K-mers.” Bioinformatics 25.8 (2008): 1012–1018. en_US http://dx.doi.org/10.1093/bioinformatics/btn645 Bioinformatics Creative Commons Attribution Non-Commercial http://creativecommons.org/licenses/by-nc/2.5 application/pdf Oxford University Press (OUP) Oxford |
| spellingShingle | Bulyk, Martha L. Philippakis, Anthony A. Alleyne, Trevis M. Peña-Castillo, Lourdes Badis, Gwenael Talukder, Shaheynoor Berger, Michael F. Gehrke, Andrew R. Morris, Quaid D. Hughes, Timothy R. Predicting the binding preference of transcription factors to individual DNA k-mers |
| title | Predicting the binding preference of transcription factors to individual DNA k-mers |
| title_full | Predicting the binding preference of transcription factors to individual DNA k-mers |
| title_fullStr | Predicting the binding preference of transcription factors to individual DNA k-mers |
| title_full_unstemmed | Predicting the binding preference of transcription factors to individual DNA k-mers |
| title_short | Predicting the binding preference of transcription factors to individual DNA k-mers |
| title_sort | predicting the binding preference of transcription factors to individual dna k mers |
| url | http://hdl.handle.net/1721.1/73182 |
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