The CUT&RUN suspect list of problematic regions of the genome

Genome Biology

Research

Pierfrancesco Pagella¹  Gianluca Zambanini¹  Anna Nordin¹  Claudio Cantù¹ 

[1] Wallenberg Centre for Molecular Medicine, Linköping University, Linköping, Sweden;Department of Biomedical and Clinical Sciences, Division of Molecular Medicine and Virology, Faculty of Medicine and Health Sciences, Linköping University, Linköping, Sweden;

关键词: CUT&RUN;    Chromatin;    Bioinformatics;    Peak calling;    Blacklist;    Suspect list;   

DOI  :  10.1186/s13059-023-03027-3

 received in 2022-11-11, accepted in 2023-07-28,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundCleavage Under Targets and Release Using Nuclease (CUT&RUN) is an increasingly popular technique to map genome-wide binding profiles of histone modifications, transcription factors, and co-factors. The ENCODE project and others have compiled blacklists for ChIP-seq which have been widely adopted: these lists contain regions of high and unstructured signal, regardless of cell type or protein target, indicating that these are false positives. While CUT&RUN obtains similar results to ChIP-seq, its biochemistry and subsequent data analyses are different. We found that this results in a CUT&RUN-specific set of undesired high-signal regions.ResultsWe compile suspect lists based on CUT&RUN data for the human and mouse genomes, identifying regions consistently called as peaks in negative controls. Using published CUT&RUN data from our and other labs, we show that the CUT&RUN suspect regions can persist even when peak calling is performed with SEACR or MACS2 against a negative control and after ENCODE blacklist removal. Moreover, we experimentally validate the CUT&RUN suspect lists by performing reiterative negative control experiments in which no specific protein is targeted, showing that they capture more than 80% of the peaks identified.ConclusionsWe propose that removing these problematic regions can substantially improve peak calling in CUT&RUN experiments, resulting in more reliable datasets.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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