{"title":"Regulatory element detection using a probabilistic segmentation model.","authors":"H J Bussemaker, H Li, E D Siggia","doi":"","DOIUrl":null,"url":null,"abstract":"<p><p>The availability of genome-wide mRNA expression data for organisms whose genome is fully sequenced provides a unique data set from which to decipher how transcription is regulated by the upstream control region of a gene. A new algorithm is presented which decomposes DNA sequence into the most probable \"dictionary\" of motifs or words. Identification of words is based on a probabilistic segmentation model in which the significance of longer words is deduced from the frequency of shorter words of various length. This eliminates the need for a separate set of reference data to define probabilities, and genome-wide applications are therefore possible. For the 6,000 upstream regulatory regions in the yeast genome, the 500 strongest motifs from a dictionary of size 1,200 match at a significance level of 15 standard deviations to a database of cis-regulatory elements. Analysis of sets of genes such as those up-regulated during sporulation reveals many new putative regulatory sites in addition to identifying previously known sites.</p>","PeriodicalId":79420,"journal":{"name":"Proceedings. International Conference on Intelligent Systems for Molecular Biology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2000-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. International Conference on Intelligent Systems for Molecular Biology","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The availability of genome-wide mRNA expression data for organisms whose genome is fully sequenced provides a unique data set from which to decipher how transcription is regulated by the upstream control region of a gene. A new algorithm is presented which decomposes DNA sequence into the most probable "dictionary" of motifs or words. Identification of words is based on a probabilistic segmentation model in which the significance of longer words is deduced from the frequency of shorter words of various length. This eliminates the need for a separate set of reference data to define probabilities, and genome-wide applications are therefore possible. For the 6,000 upstream regulatory regions in the yeast genome, the 500 strongest motifs from a dictionary of size 1,200 match at a significance level of 15 standard deviations to a database of cis-regulatory elements. Analysis of sets of genes such as those up-regulated during sporulation reveals many new putative regulatory sites in addition to identifying previously known sites.