Eukaryotic transcription factor binding sites - modeling and integrative search methods

@article{citeulike:2709889, abstract = {A comprehensive knowledge of transcription factor binding sites is important for a mechanistic understanding of transcriptional regulation as well as for inferring gene regulatory networks. Because the DNA motif recognized by a transcription factor is typically short and degenerate, computational approaches for identifying binding sites based only on the sequence motif inevitably suffer from high error rates. Current state-of-the-art techniques for improving computational identification of binding sites can be broadly categorized into two classes: (1) Approaches that aim to improve binding motif models by extracting maximal sequence information from experimentally determined binding sites and (2) Approaches that supplement binding motif models with additional genomic or other attributes (such as evolutionary conservation). In this review we will discuss recent attempts to improve computational identification of transcription factor binding sites through these two types of approaches and conclude with thoughts on future development. 10.1093/bioinformatics/btn198}, author = {Hannenhalli, Sridhar }, citeulike-article-id = {2709889}, doi = {http://dx.doi.org/10.1093/bioinformatics/btn198}, journal = {Bioinformatics}, keywords = {transcription\_factor}, month = {April}, pages = {btn198+}, posted-at = {2008-06-02 12:00:07}, priority = {2}, title = {Eukaryotic transcription factor binding sites - modeling and integrative search methods}, url = {http://dx.doi.org/10.1093/bioinformatics/btn198}, year = {2008} }

TY - JOUR ID - citeulike:2709889 L3 - citeulike-article-id:2709889 TI - Eukaryotic transcription factor binding sites - modeling and integrative search methods JF - Bioinformatics SP - btn198 N2 - A comprehensive knowledge of transcription factor binding sites is important for a mechanistic understanding of transcriptional regulation as well as for inferring gene regulatory networks. Because the DNA motif recognized by a transcription factor is typically short and degenerate, computational approaches for identifying binding sites based only on the sequence motif inevitably suffer from high error rates. Current state-of-the-art techniques for improving computational identification of binding sites can be broadly categorized into two classes: (1) Approaches that aim to improve binding motif models by extracting maximal sequence information from experimentally determined binding sites and (2) Approaches that supplement binding motif models with additional genomic or other attributes (such as evolutionary conservation). In this review we will discuss recent attempts to improve computational identification of transcription factor binding sites through these two types of approaches and conclude with thoughts on future development. 10.1093/bioinformatics/btn198 KW - transcription_factor AU - Hannenhalli, Sridhar PY - 2008/04/21/ UR - http://dx.doi.org/10.1093/bioinformatics/btn198 ER -