ArticleGenomics, proteomics & bioinformatics2007
Comparative analysis of regulatory motif discovery tools for transcription factor binding sites.
Article in Genomics, proteomics & bioinformatics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 36 citations in OpenAlex.
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- Sorting signal targeting mRNA into hepatic extracellular vesicles.RNA biology · 2014Article
- FITBAR: a web tool for the robust prediction of prokaryotic regulons.BMC bioinformatics · 2010Article
- BayesMotif: de novo protein sorting motif discovery from impure datasets.BMC bioinformatics · 2010Article
- The Escherichia coli RutR transcription factor binds at targets within genes as well as intergenic regions.Nucleic acids research · 2008Article
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Authors and funding
2 authors at 1 institution.
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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.
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