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    Predicting Combinatorial Binding of Transcription Factors to Regulatory Elements in the Human Genome by Association Rule Mining

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    Date
    2007-11
    Author
    Morgan, Xochitl C.
    Ni, Sshulin
    Miranker, Daniel P.
    Iyer, Vishwanath R.
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    Abstract
    Cis-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.
    Department
    Cellular and Molecular Biology
    Subject
    nf-kappa-b
    necrosis-factor-alpha
    protein interaction network
    cu/zn-superoxide-dismutase
    zinc-finger protein
    breast-cancer cells
    gene-expression
    promoter activity
    functional-analysis
    coactivator
    proteins
    biochemical research methods
    biotechnology & applied microbiology
    mathematical & computational biology
    URI
    http://hdl.handle.net/2152/43200
    Citation
    Morgan, 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.
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