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Motif elucidation in ChIP-seq datasets with a knockout control

View ORCID ProfileDanielle Denisko, View ORCID ProfileCoby Viner, View ORCID ProfileMichael M. Hoffman
doi: https://doi.org/10.1101/721720
Danielle Denisko
1Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
2Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
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  • ORCID record for Danielle Denisko
Coby Viner
2Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
3Department of Computer Science, University of Toronto, Toronto, ON, Canada
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Michael M. Hoffman
1Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
2Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
3Department of Computer Science, University of Toronto, Toronto, ON, Canada
4Vector Institute, Toronto, ON, Canada
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  • For correspondence: michael.hoffman@utoronto.ca
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Abstract

Chromatin immunoprecipitation-sequencing (ChIP-seq) 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 experiments can generate improved motifs but require optimal differential analysis. We introduce peaKO—a computational method to automatically optimize motif analyses with knockout controls, which we compare to two other methods. PeaKO often improves elucidation of the target factor and highlights the benefits of knockout controls, which far outperform input controls. It is freely available at https://peako.hoffmanlab.org.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵† The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors.

  • Added an additional potential future direction, mentioned some further recent work, and expanded upon technical considerations in our Discussion. Also, made some minor textual revisions to improve the clarity in some areas.

  • https://peako.hoffmanlab.org/

  • https://doi.org/10.5281/zenodo.3338324

  • https://doi.org/10.5281/zenodo.3338330

  • https://doi.org/10.5281/zenodo.3356995

  • https://github.com/hoffmangroup/peako

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted February 09, 2022.
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Motif elucidation in ChIP-seq datasets with a knockout control
Danielle Denisko, Coby Viner, Michael M. Hoffman
bioRxiv 721720; doi: https://doi.org/10.1101/721720
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Motif elucidation in ChIP-seq datasets with a knockout control
Danielle Denisko, Coby Viner, Michael M. Hoffman
bioRxiv 721720; doi: https://doi.org/10.1101/721720

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