Motif elucidation in ChIP-seq datasets with a knockout control

Author:

Denisko Danielle12ORCID,Viner Coby23ORCID,Hoffman Michael M1234ORCID

Affiliation:

1. Department of Medical Biophysics, University of Toronto , Toronto, ON M5G 1L7, Canada

2. Princess Margaret Cancer Centre, University Health Network , Toronto, ON M5G 1L7, Canada

3. Department of Computer Science, University of Toronto , Toronto, ON M5S 3G4, Canada

4. Vector Institute for Artificial Intelligence , Toronto, ON M5G 1M1, Canada

Abstract

Abstract Summary Chromatin immunoprecipitation-sequencing is widely used to find transcription factor binding sites, but suffers from various sources of noise. Knocking out the target factor mitigates noise by acting as a negative control. Paired wild-type and knockout (KO) experiments can generate improved motifs but require optimal differential analysis. We introduce peaKO—a computational method to automatically optimize motif analyses with KO controls, which we compare to two other methods. PeaKO often improves elucidation of the target factor and highlights the benefits of KO controls, which far outperform input controls. Availability and implementation PeaKO is freely available at https://peako.hoffmanlab.org. Contact michael.hoffman@utoronto.ca

Funder

Natural Sciences and Engineering Research Council of Canada

Alexander Graham Bell Canada Graduate Scholarships

Canadian Institutes of Health Research

Undergraduate Summer Studentship Award

Ontario Ministry of Training, Colleges and Universities

Ontario Ministry of Research, Innovation and Science

University of Toronto Undergraduate Research Opportunities Program

Princess Margaret Cancer Foundation

Publisher

Oxford University Press (OUP)

Subject

Cell Biology,Developmental Biology,Embryology,Anatomy

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