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Genome Biol. 2017 Feb 24;18(1):39. doi: 10.1186/s13059-017-1165-7.

BaalChIP: Bayesian analysis of allele-specific transcription factor binding in cancer genomes.

Author information

1
Cancer Research UK Cambridge Institute, University of Cambridge, Robinson Way, Cambridge, UK.
2
Present Address: Seven Bridges Genomics LTD, UK. 101 Euston Road NW1 2RA, London, UK.
3
Present Address: Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK.
4
Present Address: School of Computing Science, University of Glasgow, Glasgow, UK.
5
Cancer Research UK Cambridge Institute, University of Cambridge, Robinson Way, Cambridge, UK. Kerstin.Meyer@cruk.cam.ac.uk.
6
Cancer Research UK Cambridge Institute, University of Cambridge, Robinson Way, Cambridge, UK. florian.markowetz@cruk.cam.ac.uk.

Abstract

Allele-specific measurements of transcription factor binding from ChIP-seq data are key to dissecting the allelic effects of non-coding variants and their contribution to phenotypic diversity. However, most methods of detecting an allelic imbalance assume diploid genomes. This assumption severely limits their applicability to cancer samples with frequent DNA copy-number changes. Here we present a Bayesian statistical approach called BaalChIP to correct for the effect of background allele frequency on the observed ChIP-seq read counts. BaalChIP allows the joint analysis of multiple ChIP-seq samples across a single variant and outperforms competing approaches in simulations. Using 548 ENCODE ChIP-seq and six targeted FAIRE-seq samples, we show that BaalChIP effectively corrects allele-specific analysis for copy-number variation and increases the power to detect putative cis-acting regulatory variants in cancer genomes.

KEYWORDS:

Allele frequency; Allele-specific binding; Bayesian statistics; Cancer; ChIP-sequencing; Copy-number change; FAIRE-sequencing

PMID:
28235418
PMCID:
PMC5326502
DOI:
10.1186/s13059-017-1165-7
[Indexed for MEDLINE]
Free PMC Article

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