Comparative analysis of regulatory motif discovery tools for transcription factor binding sites.
- Author:
Wei WEI
1
;
Xiao-Dan YU
Author Information
1. Department of Pathobiology, Center of Computational Biology, Institute of Basic Medical Sciences, Beijing 100850, China.
- Publication Type:Journal Article
- MeSH:
Algorithms;
Amino Acid Motifs;
Base Sequence;
Binding Sites;
Computational Biology;
Internet;
Molecular Sequence Data;
Protein Binding;
Regulatory Elements, Transcriptional;
genetics;
Sequence Analysis, DNA;
methods;
Sequence Homology, Nucleic Acid;
Transcription Factors;
metabolism
- From:
Genomics, Proteomics & Bioinformatics
2007;5(2):131-142
- CountryChina
- Language:English
-
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.
