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Benchmarking Peak Calling Methods for CUT&RUN

ABSTRACT: Benchmarking Peak Calling Methods for CUT&RUN

PROVIDER: PRJNA1190453 | ENA |

REPOSITORIES: ENA

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			Action	DRS
		Other
	SRR31504516_1.fastq.gz	Fastqsanger.gz
	SRR31504516_2.fastq.gz	Fastqsanger.gz

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Benchmarking Peak Calling Methods for CUT&RUN

Project description:Cleavage Under Targets and Release Using Nuclease (CUT&RUN) has rapidly gained prominence as an effective approach for mapping protein-DNA interactions, especially histone modifications, offering substantial improvements over conventional chromatin immunoprecipitation sequencing (ChIP-seq). However, the effectiveness of this technique is contingent upon accurate peak identification, necessitating the use of optimal peak calling methods tailored to the unique characteristics of CUT&RUN data. Here, we benchmark four prominent peak calling tools - MACS2, SEACR, GoPeaks, and LanceOtron - evaluating their performance in identifying peaks from CUT&RUN datasets. Our analysis utilizes in-house data of three histone marks (H3K4me3, H3K27ac, and H3K27me3) from mouse brain tissue, as well as samples from the 4DNucleome database. We systematically assess these tools based on parameters such as the number of peaks called, peak length distribution, signal enrichment, and reproducibility across biological replicates. Our findings reveal substantial variability in peak calling efficacy, with each method demonstrating distinct strengths in sensitivity, precision, and applicability depending on the histone mark in question. These insights provide a comprehensive evaluation that will assist in selecting the most suitable peak caller for high-confidence identification of regions of interest in CUT&RUN experiments, ultimately enhancing the study of chromatin dynamics and transcriptional regulation.

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GoPeaks: histone modification peak calling for CUT&Tag

Project description:Peak calling tool for CUT&Tag histone modifications.

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Benchmarking normalisation methods for differential binding analysis in CUT&RUN [ER]

Project description:CUT&RUN (Cleavage Under Targets and Release Using Nuclease) is an increasingly popular method for profiling protein interactions (transcription factors, histone modifications, etc) with DNA across the whole genome. When performing differential binding analysis of CUT&RUN data to identify genomic regions where interaction profiles vary between conditions, data normalisation is essential for accurate biological interpretations. Despite this, there are no clear guidelines on the optimal normalisation method for CUT&RUN datasets. Here, we examine five normalisation approaches (spike-in, library size, background, reads-in-peak and greenlist) and highlight that different methods can result in widely discrepant interpretations of the data. We test these normalisation methods by simulating a variety of plausible differential binding scenarios as well as an in-house generated dataset. We determined that normalisation by either (i) library size or (ii) background to be the most robust. Importantly, we find spike-in normalisation to be the least reliable method. Our findings inform the use of normalisation methods for CUT&RUN data and should thus facilitate reproducible and robust analysis.

2026-05-20 | GSE331454 | GEO

Benchmarking normalisation methods for differential binding analysis in CUT&RUN [BRG1]

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Benchmarking normalisation methods for differential binding analysis in CUT&RUN [BRG1]

Project description:Benchmarking normalisation methods for differential binding analysis in CUT&RUN [BRG1]

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Benchmarking normalisation methods for differential binding analysis in CUT&RUN [ER]

Project description:Benchmarking normalisation methods for differential binding analysis in CUT&RUN [ER]

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peakC: a flexible, non-parametric peak calling package for 4C and Capture-C data

Project description:High-resolution methods such as 4C and Capture-C enable the study of chromatin loops such as those formed between promoters and enhancers or CTCF/cohesin binding sites. An important aspect of 4C/CapC analyses is the identification of robust peaks in the data for the identification of chromatin loops. Here we present an R package for the analysis of 4C/CapC data. We generated 4C data for 10 viewpoints in 2 tissues in triplicate to test our methods. We developed a non-parametric peak caller based on rank-products. Sampling analysis shows that not read depth but template quality is the most important determinant of success in 4C experiments. By performing peak calling on single experiments we show that the peak calling results are similar to the replicate experiments, but that false positive rates are significantly reduced by performing replicates.

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GoPeaks: histone modification peak calling for CUT&Tag

Project description:GoPeaks: histone modification peak calling for CUT&Tag

| PRJNA788483 | ENA