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The CUT&RUN suspect list of problematic regions of the genome.
Nordin, Anna; Zambanini, Gianluca; Pagella, Pierfrancesco; Cantù, Claudio.
Affiliation
  • Nordin A; Wallenberg Centre for Molecular Medicine, Linköping University, Linköping, Sweden.
  • Zambanini G; Department of Biomedical and Clinical Sciences, Division of Molecular Medicine and Virology, Faculty of Medicine and Health Sciences, Linköping University, Linköping, Sweden.
  • Pagella P; Wallenberg Centre for Molecular Medicine, Linköping University, Linköping, Sweden.
  • Cantù C; Department of Biomedical and Clinical Sciences, Division of Molecular Medicine and Virology, Faculty of Medicine and Health Sciences, Linköping University, Linköping, Sweden.
Genome Biol ; 24(1): 185, 2023 08 10.
Article in En | MEDLINE | ID: mdl-37563719
ABSTRACT

BACKGROUND:

Cleavage 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.

RESULTS:

We 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.

CONCLUSIONS:

We propose that removing these problematic regions can substantially improve peak calling in CUT&RUN experiments, resulting in more reliable datasets.
Subject(s)
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Transcription Factors / Genome Type of study: Prognostic_studies Limits: Animals / Humans Language: En Journal: Genome Biol Journal subject: BIOLOGIA MOLECULAR / GENETICA Year: 2023 Document type: Article Affiliation country:

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Transcription Factors / Genome Type of study: Prognostic_studies Limits: Animals / Humans Language: En Journal: Genome Biol Journal subject: BIOLOGIA MOLECULAR / GENETICA Year: 2023 Document type: Article Affiliation country: