Swneke D Bailey1, Carl Virtanen2, Benjamin Haibe-Kains1, Mathieu Lupien3. 1. Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada, Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada and Ontario Institute for Cancer Research, Toronto, ON, Canada Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada, Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada and Ontario Institute for Cancer Research, Toronto, ON, Canada. 2. Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada, Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada and Ontario Institute for Cancer Research, Toronto, ON, Canada. 3. Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada, Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada and Ontario Institute for Cancer Research, Toronto, ON, Canada Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada, Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada and Ontario Institute for Cancer Research, Toronto, ON, Canada Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada, Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada and Ontario Institute for Cancer Research, Toronto, ON, Canada.
Abstract
MOTIVATION: Detection of allelic imbalances in ChIP-Seq reads is a powerful approach to identify functional non-coding single nucleotide variants (SNVs), either polymorphisms or mutations, which modulate the affinity of transcription factors for chromatin. We present ABC, a computational tool that identifies allele-specific binding of transcription factors from aligned ChIP-Seq reads at heterozygous SNVs. ABC controls for potential false positives resulting from biases introduced by the use of short sequencing reads in ChIP-Seq and can efficiently process a large number of heterozygous SNVs. RESULTS: ABC successfully identifies previously characterized functional SNVs, such as the rs4784227 breast cancer risk associated SNP that modulates the affinity of FOXA1 for the chromatin. AVAILABILITY AND IMPLEMENTATION: The code is open-source under an Artistic-2.0 license and versioned on GitHub (https://github.com/mlupien/ABC/). ABC is written in PERL and can be run on any platform with both PERL (≥5.18.1) and R (≥3.1.1) installed. The script requires the PERL Statistics::R module. CONTACT: mlupien@uhnres.utoronto.ca SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Detection of allelic imbalances in ChIP-Seq reads is a powerful approach to identify functional non-coding single nucleotide variants (SNVs), either polymorphisms or mutations, which modulate the affinity of transcription factors for chromatin. We present ABC, a computational tool that identifies allele-specific binding of transcription factors from aligned ChIP-Seq reads at heterozygous SNVs. ABC controls for potential false positives resulting from biases introduced by the use of short sequencing reads in ChIP-Seq and can efficiently process a large number of heterozygous SNVs. RESULTS:ABC successfully identifies previously characterized functional SNVs, such as the rs4784227breast cancer risk associated SNP that modulates the affinity of FOXA1 for the chromatin. AVAILABILITY AND IMPLEMENTATION: The code is open-source under an Artistic-2.0 license and versioned on GitHub (https://github.com/mlupien/ABC/). ABC is written in PERL and can be run on any platform with both PERL (≥5.18.1) and R (≥3.1.1) installed. The script requires the PERL Statistics::R module. CONTACT: mlupien@uhnres.utoronto.ca SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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