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

Morgan XC, Ni S, Miranker DP, Iyer VR - BMC Bioinformatics (2007)

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute for Cellular and Molecular Biology and Center for Systems and Synthetic Biology, The University of Texas at Austin, Austin, Texas 78712-0159, USA. morganx@mail.utexas.edu

ABSTRACT

Background: 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.

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.

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Overview. Patser is used to map all possible binding sites in the genome for each of 83 position weight matrices (PWMs) from TRANSFAC. The genome is then scored 100 bp at a time for the presence or absence of each PWM, and association rules are used to mine the genome for frequently co-occurring pairs.
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Figure 1: Overview. Patser is used to map all possible binding sites in the genome for each of 83 position weight matrices (PWMs) from TRANSFAC. The genome is then scored 100 bp at a time for the presence or absence of each PWM, and association rules are used to mine the genome for frequently co-occurring pairs.

Mentions: Determining over-represented transcription factor partners may help to reveal biological roles for less well-studied transcription factors. Therefore, in our studies, we used data mining to determine whether two transcription factors whose experimentally determined binding motifs were frequently proximal to one another were also likely to have biologically meaningful interactions. For example, the rule "Nuclear Factor Kappa B ⇒ Ap-1" would indicate "Where there is a motif for NFκB, there is often also an Ap-1 motif." To allow application of association rules to transcription factor motifs in the human genome, we divided the genome into segments and scored each segment for the presence or absence of each of 83 transcription factor binding motifs (Figure 1). Thus, the set of 83 motifs becomes I, each individual transcription factor binding motif becomes an item, and each small segment of genome becomes a transaction T whose contents X are the motifs located within.


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

Morgan XC, Ni S, Miranker DP, Iyer VR - BMC Bioinformatics (2007)

Overview. Patser is used to map all possible binding sites in the genome for each of 83 position weight matrices (PWMs) from TRANSFAC. The genome is then scored 100 bp at a time for the presence or absence of each PWM, and association rules are used to mine the genome for frequently co-occurring pairs.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2211755&req=5

Figure 1: Overview. Patser is used to map all possible binding sites in the genome for each of 83 position weight matrices (PWMs) from TRANSFAC. The genome is then scored 100 bp at a time for the presence or absence of each PWM, and association rules are used to mine the genome for frequently co-occurring pairs.
Mentions: Determining over-represented transcription factor partners may help to reveal biological roles for less well-studied transcription factors. Therefore, in our studies, we used data mining to determine whether two transcription factors whose experimentally determined binding motifs were frequently proximal to one another were also likely to have biologically meaningful interactions. For example, the rule "Nuclear Factor Kappa B ⇒ Ap-1" would indicate "Where there is a motif for NFκB, there is often also an Ap-1 motif." To allow application of association rules to transcription factor motifs in the human genome, we divided the genome into segments and scored each segment for the presence or absence of each of 83 transcription factor binding motifs (Figure 1). Thus, the set of 83 motifs becomes I, each individual transcription factor binding motif becomes an item, and each small segment of genome becomes a transaction T whose contents X are the motifs located within.

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute for Cellular and Molecular Biology and Center for Systems and Synthetic Biology, The University of Texas at Austin, Austin, Texas 78712-0159, USA. morganx@mail.utexas.edu

ABSTRACT

Background: 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.

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.

Show MeSH
Related in: MedlinePlus