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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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Support of TF pairs across chromosomes. The support of a given TF pair is highly correlated between chromosomes (B, D). This is also true for support in promoter regions versus the entire human genome (A) as well as support between human and mouse chromosomes (C).
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Figure 3: Support of TF pairs across chromosomes. The support of a given TF pair is highly correlated between chromosomes (B, D). This is also true for support in promoter regions versus the entire human genome (A) as well as support between human and mouse chromosomes (C).

Mentions: Due to the size of the human genome and the tendency of PWMs to match at a large number of genomic locations, all TF pairs showed some co-occurrence. This support for possible transcription factor PWM pairs ranged from 9 x 10-6 to 0.2. Support for the association of a given pair of transcription factors was highly conserved, not only between promoters and the entire genome (Figure 3A), but also between the human chromosomes and mouse chromosome 1 (Figure 3C) and between individual human chromosomes (Figure 3B, Figure 3D), suggesting that the associations revealed by mining are biologically relevant.


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)

Support of TF pairs across chromosomes. The support of a given TF pair is highly correlated between chromosomes (B, D). This is also true for support in promoter regions versus the entire human genome (A) as well as support between human and mouse chromosomes (C).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Support of TF pairs across chromosomes. The support of a given TF pair is highly correlated between chromosomes (B, D). This is also true for support in promoter regions versus the entire human genome (A) as well as support between human and mouse chromosomes (C).
Mentions: Due to the size of the human genome and the tendency of PWMs to match at a large number of genomic locations, all TF pairs showed some co-occurrence. This support for possible transcription factor PWM pairs ranged from 9 x 10-6 to 0.2. Support for the association of a given pair of transcription factors was highly conserved, not only between promoters and the entire genome (Figure 3A), but also between the human chromosomes and mouse chromosome 1 (Figure 3C) and between individual human chromosomes (Figure 3B, Figure 3D), suggesting that the associations revealed by mining are biologically relevant.

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