Comparative analysis of regulatory motif discovery tools for transcription factor binding sites. Export

@article{citeulike:1701800, abstract = {In the post-genomic era, identification of specific regulatory motifs or transcription factor binding sites (TFBSs) in non-coding DNA sequences, which is essential to elucidate transcriptional regulatory networks, has emerged as an obstacle that frustrates many researchers. Consequently, numerous motif discovery tools and correlated databases have been applied to solving this problem. However, these existing methods, based on different computational algorithms, show diverse motif prediction efficiency in non-coding DNA sequences. Therefore, understanding the similarities and differences of computational algorithms and enriching the motif discovery literatures are important for users to choose the most appropriate one among the online available tools. Moreover, there still lacks credible criterion to assess motif discovery tools and instructions for researchers to choose the best according to their own projects. Thus integration of the related resources might be a good approach to improve accuracy of the application. Recent studies integrate regulatory motif discovery tools with experimental methods to offer a complementary approach for researchers, and also provide a much-needed model for current researches on transcriptional regulatory networks. Here we present a comparative analysis of regulatory motif discovery tools for TFBSs.}, address = {Department of Pathobiology, Center of Computational Biology, Institute of Basic Medical Sciences, Beijing 100850, China.}, author = {Wei, W. and Yu, X. D.}, citeulike-article-id = {1701800}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/S1672-0229(07)60023-0}, citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/S1672022907600230}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/17893078}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=17893078}, citeulike-linkout-4 = {http://www.sciencedirect.com/science/article/B82XM-4PR42DD-6/2/36e2194ae83a531ff0c3047796f0e9f3}, doi = {10.1016/S1672-0229(07)60023-0}, issn = {1672-0229}, journal = {Genomics Proteomics Bioinformatics}, keywords = {discovery, rennes, review}, month = {May}, number = {2}, pages = {131--142}, posted-at = {2007-09-27 15:56:13}, priority = {4}, title = {Comparative analysis of regulatory motif discovery tools for transcription factor binding sites.}, url = {http://dx.doi.org/10.1016/S1672-0229(07)60023-0}, volume = {5}, year = {2007} }

TY - JOUR ID - citeulike:1701800 L3 - citeulike-article-id:1701800 N2 - In the post-genomic era, identification of specific regulatory motifs or transcription factor binding sites (TFBSs) in non-coding DNA sequences, which is essential to elucidate transcriptional regulatory networks, has emerged as an obstacle that frustrates many researchers. Consequently, numerous motif discovery tools and correlated databases have been applied to solving this problem. However, these existing methods, based on different computational algorithms, show diverse motif prediction efficiency in non-coding DNA sequences. Therefore, understanding the similarities and differences of computational algorithms and enriching the motif discovery literatures are important for users to choose the most appropriate one among the online available tools. Moreover, there still lacks credible criterion to assess motif discovery tools and instructions for researchers to choose the best according to their own projects. Thus integration of the related resources might be a good approach to improve accuracy of the application. Recent studies integrate regulatory motif discovery tools with experimental methods to offer a complementary approach for researchers, and also provide a much-needed model for current researches on transcriptional regulatory networks. Here we present a comparative analysis of regulatory motif discovery tools for TFBSs. IS - 2 JF - Genomics Proteomics Bioinformatics SN - 1672-0229 AD - Department of Pathobiology, Center of Computational Biology, Institute of Basic Medical Sciences, Beijing 100850, China. EP - 142 TI - Comparative analysis of regulatory motif discovery tools for transcription factor binding sites. VL - 5 SP - 131 KW - discovery KW - rennes KW - review AU - Wei, W AU - Yu, XD PY - 2007/05// UR - http://dx.doi.org/10.1016/S1672-0229(07)60023-0 ER -