Limits...
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.

Show MeSH

Related in: MedlinePlus

Fractions of TF pairs with significant co-citation P-values. Fractions of TF pairs with significant co-citation P-values (P < 0.05) in each dataset. Asterisks indicate a significant difference between all pairs and the selected subset as measured by a Chi square test. P-values significant after the Bonferroni correction for multiple hypothesis testing are indicated by "§".
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2211755&req=5

Figure 5: Fractions of TF pairs with significant co-citation P-values. Fractions of TF pairs with significant co-citation P-values (P < 0.05) in each dataset. Asterisks indicate a significant difference between all pairs and the selected subset as measured by a Chi square test. P-values significant after the Bonferroni correction for multiple hypothesis testing are indicated by "§".

Mentions: Figure 5 shows the fraction of total TF pairs with significant co-citation P-values (P < 0.05) in each dataset. Asterisks indicate a significant difference between all TF pairs and the selected subset as measured by a Chi square test. All sets indicated by "§" were significant after Bonferroni correction for multiple hypothesis testing. All three subsets showed substantially higher proportions of TF pairs enriched for low co-citation P-values in all cases than the set of all pairs, indicating that transcription factors binding to the PWMs that showed substantial association with one another on the genome were more likely to be co-cited in the literature, reflecting a likely biological association between them. This enrichment of "genomewide" was significant for most values at all adjustments. The subset "mouse" was enriched for significant concordances and Jaccard values when unadjusted or adjusted by paper size and was significant for all values when adjusted by both gene and paper size. The subset "promoter" was more significant after adjustments for gene size or both gene and paper size.


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)

Fractions of TF pairs with significant co-citation P-values. Fractions of TF pairs with significant co-citation P-values (P < 0.05) in each dataset. Asterisks indicate a significant difference between all pairs and the selected subset as measured by a Chi square test. P-values significant after the Bonferroni correction for multiple hypothesis testing are indicated by "§".
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: Fractions of TF pairs with significant co-citation P-values. Fractions of TF pairs with significant co-citation P-values (P < 0.05) in each dataset. Asterisks indicate a significant difference between all pairs and the selected subset as measured by a Chi square test. P-values significant after the Bonferroni correction for multiple hypothesis testing are indicated by "§".
Mentions: Figure 5 shows the fraction of total TF pairs with significant co-citation P-values (P < 0.05) in each dataset. Asterisks indicate a significant difference between all TF pairs and the selected subset as measured by a Chi square test. All sets indicated by "§" were significant after Bonferroni correction for multiple hypothesis testing. All three subsets showed substantially higher proportions of TF pairs enriched for low co-citation P-values in all cases than the set of all pairs, indicating that transcription factors binding to the PWMs that showed substantial association with one another on the genome were more likely to be co-cited in the literature, reflecting a likely biological association between them. This enrichment of "genomewide" was significant for most values at all adjustments. The subset "mouse" was enriched for significant concordances and Jaccard values when unadjusted or adjusted by paper size and was significant for all values when adjusted by both gene and paper size. The subset "promoter" was more significant after adjustments for gene size or both gene and paper size.

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