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Licensed Unlicensed Requires Authentication Published by De Gruyter March 2, 2021

Computational prediction of CRISPR-impaired non-coding regulatory regions

Nina Baumgarten ORCID logo, Florian Schmidt ORCID logo, Martin Wegner ORCID logo, Marie Hebel, Manuel Kaulich ORCID logo and Marcel H. Schulz
From the journal Biological Chemistry

Abstract

Genome-wide CRISPR screens are becoming more widespread and allow the simultaneous interrogation of thousands of genomic regions. Although recent progress has been made in the analysis of CRISPR screens, it is still an open problem how to interpret CRISPR mutations in non-coding regions of the genome. Most of the tools concentrate on the interpretation of mutations introduced in gene coding regions. We introduce a computational pipeline that uses epigenomic information about regulatory elements for the interpretation of CRISPR mutations in non-coding regions. We illustrate our analysis protocol on the analysis of a genome-wide CRISPR screen in hTERT-RPE1 cells and reveal novel regulatory elements that mediate chemoresistance against doxorubicin in these cells. We infer links to established and to novel chemoresistance genes. Our analysis protocol is general and can be applied on any cell type and with different CRISPR enzymes.


Corresponding author: Marcel H. Schulz, Institute for Cardiovascular Regeneration, Goethe University, 60590Frankfurt am Main, Germany; German Center for Cardiovascular Research, Partner site Rhein-Main, 60590Frankfurt am Main, Germany; Cluster of Excellence MMCI, Saarland University, and Max Planck Institute for Informatics, Saarland Informatics Campus, 66123Saarbrücken, Germany; and Cardiopulmonary Institute (CPI), Goethe University, 60590 Frankfurt am Main, Germany, E-mail:
Nina Baumgarten and Florian Schmidt contributed equally to this work.

Funding source: Deutsches Zentrum für Herz-Kreislaufforschung

Award Identifier / Grant number: 81Z0200101

Funding source: Clusters of Excellence on Multimodal Computing and Interaction

Award Identifier / Grant number: EXC248

Funding source: Cardio-Pulmonary Institute (CPI)

Award Identifier / Grant number: EXC2026

Acknowledgements

We are thankful to the ENCODE consortia for sharing the epigenomic data used in this work.

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: This work has been supported by the DZHK (German Centre for Cardiovascular Research, 81Z0200101) and the DFG Clusters of Excellence on Multimodal Computing and Interaction [EXC248] and Cardio-Pulmonary Institute (CPI) [EXC2026].

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

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Supplementary Material

The online version of this article offers supplementary material (https://doi.org/10.1515/hsz-2020-0392).


Received: 2020-12-22
Accepted: 2021-02-18
Published Online: 2021-03-02
Published in Print: 2021-07-27

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