Linked Life Data

15759655

Source:http://linkedlifedata.com/resource/pubmed/id/15759655

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2005-3-11
pubmed:abstractText
Sigma factors, often in conjunction with other transcription factors, regulate gene expression in prokaryotes at the transcriptional level. Specific transcription factors tend to co-occur with specific sigma factors. To predict new members of the transcription factor regulon, we applied Bayes rule to combine the Bayesian probability of sigma factor prediction calculated from microarray data and the sigma factor binding sequence motif, the motif score of the transcription factor associated with the sigma factor, the empirically determined distance between the transcription start site to the cis-regulatory region, and the tendency for specific sigma factors and transcription factors to co-occur. By combining these information sources, we improve the accuracy of predicting regulation by transcription factors, and also confirm the sigma factor prediction. We applied our proposed method to all genes in Bacillus subtilis to find currently unknown gene regulations by transcription factors and sigma factors.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
1793-5091
pubmed:author
pubmed:issnType
Print
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
507-18
pubmed:dateRevised
2007-9-12
pubmed:meshHeading
pubmed:year
2005
pubmed:articleTitle
Bayesian joint prediction of associated transcription factors in Bacillus subtilis.
pubmed:affiliation
Human Genome Center, Institute of Medical Science, University of Tokyo Minato-ku, Tokyo 108-8639, Japan.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't