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dc.creatorMorgan, Xochitl C.en_US
dc.creatorNi, Sshulinen_US
dc.creatorMiranker, Daniel P.en_US
dc.creatorIyer, Vishwanath R.en_US
dc.date.accessioned2016-10-28T19:50:33Z
dc.date.available2016-10-28T19:50:33Z
dc.date.issued2007-11en_US
dc.identifierdoi:10.15781/T2X921M94
dc.identifier.citationMorgan, Xochitl C., Shulin Ni, Daniel P. Miranker, and Vishwanath R. Iyer. "Predicting combinatorial binding of transcription factors to regulatory elements in the human genome by association rule mining." BMC bioinformatics, Vol. 8, No. 1 (Nov., 2007): 1.en_US
dc.identifier.issn1471-2105en_US
dc.identifier.urihttp://hdl.handle.net/2152/43200
dc.description.abstractCis-acting transcriptional regulatory elements in mammalian genomes typically contain specific combinations of binding sites for various transcription factors. Although some cisregulatory elements have been well studied, the combinations of transcription factors that regulate normal expression levels for the vast majority of the 20,000 genes in the human genome are unknown. We hypothesized that it should be possible to discover transcription factor combinations that regulate gene expression in concert by identifying over-represented combinations of sequence motifs that occur together in the genome. In order to detect combinations of transcription factor binding motifs, we developed a data mining approach based on the use of association rules, which are typically used in market basket analysis. We scored each segment of the genome for the presence or absence of each of 83 transcription factor binding motifs, then used association rule mining algorithms to mine this dataset, thus identifying frequently occurring pairs of distinct motifs within a segment. Results: Support for most pairs of transcription factor binding motifs was highly correlated across different chromosomes although pair significance varied. Known true positive motif pairs showed higher association rule support, confidence, and significance than background. Our subsets of high-confidence, high-significance mined pairs of transcription factors showed enrichment for co-citation in PubMed abstracts relative to all pairs, and the predicted associations were often readily verifiable in the literature. Conclusion: Functional elements in the genome where transcription factors bind to regulate expression in a combinatorial manner are more likely to be predicted by identifying statistically and biologically significant combinations of transcription factor binding motifs than by simply scanning the genome for the occurrence of binding sites for a single transcription factor.en_US
dc.description.sponsorshipNIAAA Alcohol Training Granten_US
dc.description.sponsorshipNational Science Foundationen_US
dc.language.isoEnglishen_US
dc.relation.ispartofen_US
dc.rightsAdministrative deposit of works to Texas ScholarWorks: This works author(s) is or was a University faculty member, student or staff member; this article is already available through open access or the publisher allows a PDF version of the article to be freely posted online. The library makes the deposit as a matter of fair use (for scholarly, educational, and research purposes), and to preserve the work and further secure public access to the works of the University.en_US
dc.subjectnf-kappa-ben_US
dc.subjectnecrosis-factor-alphaen_US
dc.subjectprotein interaction networken_US
dc.subjectcu/zn-superoxide-dismutaseen_US
dc.subjectzinc-finger proteinen_US
dc.subjectbreast-cancer cellsen_US
dc.subjectgene-expressionen_US
dc.subjectpromoter activityen_US
dc.subjectfunctional-analysisen_US
dc.subjectcoactivatoren_US
dc.subjectproteinsen_US
dc.subjectbiochemical research methodsen_US
dc.subjectbiotechnology & applied microbiologyen_US
dc.subjectmathematical & computational biologyen_US
dc.titlePredicting Combinatorial Binding of Transcription Factors to Regulatory Elements in the Human Genome by Association Rule Miningen_US
dc.typeReviewen_US
dc.description.departmentCellular and Molecular Biologyen_US
dc.rights.restrictionOpenen_US
dc.identifier.doi10.1186/1471-2105-8-445en_US
dc.contributor.utaustinauthorMorgan, Xochitl C.en_US
dc.contributor.utaustinauthorNi, Sshulinen_US
dc.contributor.utaustinauthorMiranker, Daniel P.en_US
dc.contributor.utaustinauthorIyer, Vishwanath R.en_US
dc.relation.ispartofserialBMC Bioinformaticsen_US


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