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Nucleic Acids Res. 2016 Dec 1;44(21):10106-10116. Epub 2016 Aug 4.

Evaluating the impact of single nucleotide variants on transcription factor binding.

Author information

1
Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics, Child & Family Research Institute, University of British Columbia, 950 28th Ave W, Vancouver, BC V5Z 4H4, Canada.
2
Bioinformatics Graduate Program, University of British Columbia, 2329 W Mall, Vancouver, BC V6T 1Z4, Canada.
3
Centre for Molecular Medicine Norway (NCMM), Nordic EMBL partnership, University of Oslo and Oslo University Hospital, Norway.
4
Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics, Child & Family Research Institute, University of British Columbia, 950 28th Ave W, Vancouver, BC V5Z 4H4, Canada wyeth@cmmt.ubc.ca.

Abstract

Diseases and phenotypes caused by disrupted transcription factor (TF) binding are being identified, but progress is hampered by our limited capacity to predict such functional alterations. Improving predictions may be dependent on expanding the set of bona fide TF binding alterations. Allele-specific binding (ASB) events, where TFs preferentially bind to one of the two alleles at heterozygous sites, reveal the impact of sequence variations in altered TF binding. Here, we present the largest ASB compilation to our knowledge, 10 765 ASB events retrieved from 45 ENCODE ChIP-Seq data sets. Our analysis showed that ASB events were frequently associated with motif alterations of the ChIP'ed TF and potential partner TFs, allelic difference of DNase I hypersensitivity and allelic difference of histone modifications. For TF dimers bound symmetrically to DNA, ASB data revealed that central positions of the TF binding motifs were disproportionately important for binding. Lastly, the impact of variation on TF binding was predicted by a classification model incorporating all the investigated features of ASB events. Classification models using only DNase I hypersensitivity and sequence data exhibited predictive accuracy approaching the models with substantially more features. Taken together, the combination of ASB data and the classification model represents an important step toward elucidating regulatory variants across the human genome.

PMID:
27492288
PMCID:
PMC5137422
DOI:
10.1093/nar/gkw691
[Indexed for MEDLINE]
Free PMC Article

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