Predicting combinatorial binding of transcription factors to regulatory elements in the human genome by association rule mining

• Published in: BMC bioinformatics Vol. 8; no. 1; p. 445
• Main Authors: Morgan, Xochitl C, Ni, Shulin, Miranker, Daniel P, Iyer, Vishwanath R
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 15.11.2007; BioMed Central; BMC
• Subjects: Algorithms; Automatic Data Processing - methods; Binding Sites; Computational biology; Databases, Factual; DNA binding proteins; Forecasting - methods; Gene Expression Profiling; Genome, Human; Human genome; Humans; Microarray Analysis; Models, Biological; Protein Binding; Regulatory Elements, Transcriptional; Transcription Factors - metabolism; United States
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-8-445

Cis-acting transcriptional regulatory elements in mammalian genomes typically contain specific combinations of binding sites for various transcription factors. Although some cis-regulatory 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. 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. 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.