Motif elucidation in ChIP-seq datasets with a knockout control

Danielle Denisko; Coby Viner; Michael M Hoffman

doi:10.1093/bioadv/vbad031

Motif elucidation in ChIP-seq datasets with a knockout control

Bioinform Adv. 2023 Mar 16;3(1):vbad031. doi: 10.1093/bioadv/vbad031. eCollection 2023.

Authors

Danielle Denisko^{1

2}, Coby Viner^{2

3}, Michael M Hoffman^{1

2

3

4}

Affiliations

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

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.