PMID:14962923: Difference between revisions

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<span style = 'font-size: large'> '''Jensen, ST and Liu, JS'''  (2004) BioOptimizer: a Bayesian scoring function approach to motif discovery.''Bioinformatics'' '''20''':1557-64</span>
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|{{TableHeadingColor}} |'''Abstract''' || <protect>MOTIVATION: Transcription factors (TFs) bind directly to short segments on the genome, often within hundreds to thousands of base pairs upstream of gene transcription start sites, to regulate gene expression. The experimental determination of TFs binding sites is expensive and time-consuming. Many motif-finding programs have been developed, but no program is clearly superior in all situations. Practitioners often find it difficult to judge which of the motifs predicted by these algorithms are more likely to be biologically relevant. RESULTS: We derive a comprehensive scoring function based on a full Bayesian model that can handle unknown site abundance, unknown motif width and two-block motifs with variable-length gaps. An algorithm called BioOptimizer is proposed to optimize this scoring function so as to reduce noise in the motif signal found by any motif-finding program. The accuracy of BioOptimizer, which can be used in conjunction with several existing programs, is shown to be superior to using any of these motif-finding programs alone when evaluated by both simulation studies and application to sets of co-regulated genes in bacteria. In addition, this scoring function formulation enables us to compare objectively different predicted motifs and select the optimal ones, effectively combining the strengths of existing programs. AVAILABILITY: BioOptimizer is available for download at www.fas.harvard.edu/~junliu/BioOptimizer/</protect>
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'''No author listed''' (2004) BioOptimizer: a Bayesian scoring function approach to motif discovery. ''Bioinformatics'' '''20''':1557-64
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|{{TableHeadingColor}} nowrap |'''Links''' || [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=14962923 PubMed]<br/>Online version:[http://dx.doi.org/10.1093/bioinformatics/bth127 10.1093/bioinformatics/bth127]
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Transcription factors (TFs) bind directly to short segments on the genome, often within hundreds to thousands of base pairs upstream of gene transcription start sites, to regulate gene expression. The experimental determination of TFs binding sites is expensive and time-consuming. Many motif-finding programs have been developed, but no program is clearly superior in all situations. Practitioners often find it difficult to judge which of the motifs predicted by these algorithms are more likely to be biologically relevant.
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[http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=14962923 PubMed]
Online version:[http://dx.doi.org/10.1093/bioinformatics/bth127 10.1093/bioinformatics/bth127]
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!align=left  |Keywords
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Algorithms; Amino Acid Motifs; Bayes Theorem; Binding Sites; DNA/chemistry; Protein Binding; Sequence Alignment/methods; Sequence Analysis, DNA/methods; Software; Transcription Factors/chemistry
 
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==Significance {{HelpIcon|Help:Literature}} ==
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Revision as of 18:31, 1 September 2013

Citation

No author listed (2004) BioOptimizer: a Bayesian scoring function approach to motif discovery. Bioinformatics 20:1557-64

Abstract

Transcription factors (TFs) bind directly to short segments on the genome, often within hundreds to thousands of base pairs upstream of gene transcription start sites, to regulate gene expression. The experimental determination of TFs binding sites is expensive and time-consuming. Many motif-finding programs have been developed, but no program is clearly superior in all situations. Practitioners often find it difficult to judge which of the motifs predicted by these algorithms are more likely to be biologically relevant.

Links

PubMed Online version:10.1093/bioinformatics/bth127

Keywords

Algorithms; Amino Acid Motifs; Bayes Theorem; Binding Sites; DNA/chemistry; Protein Binding; Sequence Alignment/methods; Sequence Analysis, DNA/methods; Software; Transcription Factors/chemistry

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