Position dependencies in transcription factor binding sites.

@article{citeulike:1745290, abstract = {MOTIVATION: Most of the available tools for transcription factor binding site prediction are based on methods which assume no sequence dependence between the binding site base positions. Our primary objective was to investigate the statistical basis for either a claim of dependence or independence, to determine whether such a claim is generally true, and to use the resulting data to develop improved scoring functions for binding-site prediction. RESULTS: Using three statistical tests, we analyzed the number of binding sites showing dependent positions. We analyzed transcription factor-DNA crystal structures for evidence of position dependence. Our final conclusions were that some factors show evidence of dependencies whereas others do not. We observed that the conformational energy (Z-score) of the transcription factor-DNA complexes was lower (better) for sequences that showed dependency than for those that did not (P < 0.02). We suggest that where evidence exists for dependencies, these should be modeled to improve binding-site predictions. However, when no significant dependency is found, this correction should be omitted. This may be done by converting any existing scoring function which assumes independence into a form which includes a dependency correction. We present an example of such an algorithm and its implementation as a web tool. AVAILABILITY: http://promoterplot.fmi.ch/cgi-bin/dep.html}, address = {Friedrich Miescher Institute for Biomedical Research, Novartis Research Foundation, Basel, Switzerland.}, author = {Tomovic, A. and Oakeley, E. J. }, citeulike-article-id = {1745290}, issn = {1460-2059}, journal = {Bioinformatics}, keywords = {mining, motifs}, month = {April}, number = {8}, pages = {933--941}, posted-at = {2008-01-08 00:29:53}, priority = {2}, title = {Position dependencies in transcription factor binding sites.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/17308339}, volume = {23}, year = {2007} }

TY - JOUR ID - citeulike:1745290 L3 - citeulike-article-id:1745290 TI - Position dependencies in transcription factor binding sites. JF - Bioinformatics VL - 23 IS - 8 SP - 933 EP - 941 AD - Friedrich Miescher Institute for Biomedical Research, Novartis Research Foundation, Basel, Switzerland. SN - 1460-2059 N2 - MOTIVATION: Most of the available tools for transcription factor binding site prediction are based on methods which assume no sequence dependence between the binding site base positions. Our primary objective was to investigate the statistical basis for either a claim of dependence or independence, to determine whether such a claim is generally true, and to use the resulting data to develop improved scoring functions for binding-site prediction. RESULTS: Using three statistical tests, we analyzed the number of binding sites showing dependent positions. We analyzed transcription factor-DNA crystal structures for evidence of position dependence. Our final conclusions were that some factors show evidence of dependencies whereas others do not. We observed that the conformational energy (Z-score) of the transcription factor-DNA complexes was lower (better) for sequences that showed dependency than for those that did not (P < 0.02). We suggest that where evidence exists for dependencies, these should be modeled to improve binding-site predictions. However, when no significant dependency is found, this correction should be omitted. This may be done by converting any existing scoring function which assumes independence into a form which includes a dependency correction. We present an example of such an algorithm and its implementation as a web tool. AVAILABILITY: http://promoterplot.fmi.ch/cgi-bin/dep.html KW - mining KW - motifs AU - Tomovic, A AU - Oakeley, EJ PY - 2007/04/15/ UR - http://view.ncbi.nlm.nih.gov/pubmed/17308339 ER -