Benchmarking peak calling methods for CUT&RUN.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40569178", "kind": "science-record", "revision": 2, "content_hash": "sha256:b2e2aa383f6af608e80908f70db76c2d25b754b697853518fbee1ce4d1ec23f3", "created_at": "2026-11-01T20:30:39Z", "updated_at": "2026-11-01T21:35:20Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Benchmarking peak calling methods for CUT&RUN.", "doi": "10.1093/bioinformatics/btaf375", "pmid": "40569178", "authors": ["Amin Nooranikhojasteh", "Ghazaleh Tavallaee", "Elias Orouji"], "journal": "Bioinformatics (Oxford, England)", "year": 2025, "abstract": "MOTIVATION: 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.\nRESULTS: 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 4D Nucleome 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.\nAVAILABILITY AND IMPLEMENTATION: The CUT&RUN data generated in this study have been deposited in the Gene Expression Omnibus (GEO) under the accession number GSE282809. All the 4D Nucleome datasets can be obtained from the 4D Nucleome Data Portal (https://data.4dnucleome.org/). All scripts used for data processing, figure generation, and analysis are available in the following GitHub repository: https://github.com/OroujiLab/CUTandRun_Peak_Calling/, and have also been archived on Zenodo.", "claims": [{"id": "claim-method", "statement": "Four peak calling methods were benchmarked in this study: MACS2, SEACR, GoPeaks, and LanceOtron. The reproducibility of peaks between biological replicates was assessed for each method using the Irreproducible Discovery Rate (IDR).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40569178", "locator": "sections[11] \"2.4 Peak calling methods\" paragraph 2", "quote": "Four peak calling methods were benchmarked in this study: MACS2, SEACR, GoPeaks, and LanceOtron."}, {"asset": "source:pmid:40569178", "locator": "sections[17] \"3.1.1 Metrics for benchmarking\" paragraph 2", "quote": "The reproducibility of peaks between biological replicates was assessed for each method using the Irreproducible Discovery Rate (IDR)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The CUT&RUN data generated in this study have been deposited in the GEO under the accession number GSE282809. This dataset includes processed peak files in BED format and genome-wide signal tracks in bigWig format for visualization. Specific datasets for H3K4me3, H3K27ac, and H3K27me3 across two biological replicates are available as part of this submission. The data will be made publicly available upon publication. All the 4D Nucleome datasets can be obtained from the 4D Nucleome Data Portal (https://data.4dnucleome.org/). The accession numbers for all 4D Nucleome datasets used are provided in Table S5, available as supplementary data at Bioinformatics online. All scripts used for data processing, figure generation, and analysis are available in the following GitHub repository: https://github.com/OroujiLab/CUTandRun_Peak_Calling/, and have also been archived on Zenodo (https://doi.org/10.5281/zenodo.15208150).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40569178", "locator": "sections[30] \"Data availability\" paragraph 2", "quote": "The CUT&RUN data generated in this study have been deposited in the GEO under the accession number GSE282809. This dataset includes processed peak files in BED format and genome-wide signal tracks in bigWig format for visualization. Specific datasets for H3K4me3, H3K27ac, and H3K27me3 across two biological replicates are available as part of this submission. The data will be made publicly available upon publication. All the 4D Nucleome datasets can be obtained from the 4D Nucleome Data Portal (https://data.4dnucleome.org/). The accession numbers for all 4D Nucleome datasets used are provided in Table S5, available as supplementary data at Bioinformatics online. All scripts used for data processing, figure generation, and analysis are available in the following GitHub repository: https://github.com/OroujiLab/CUTandRun_Peak_Calling/, and have also been archived on Zenodo (https://doi.org/10.5281/zenodo.15208150)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "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.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40569178", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "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."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "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.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:40569178"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE282809"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE282809"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40569178", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1093/bioinformatics/btaf375", "sha256": "sha256:68094b600831a7eed14de00ef69fc47c80d5f5f987f1183289fcb6669af38ee1", "bytes": 42633, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1093/bioinformatics/btaf375", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12255880", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE282809", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE282809", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE282809", "version": null, "note": "The CUT&RUN data generated in this study have been deposited in the GEO under the accession number GSE282809. This dataset includes processed peak files in BED format and genome-wide signal tracks in bigWig format for visualization. Specific datasets for H3K4me3, H3K27ac, and H3K27me3 across two biological replicates are available as part of this submission. The data will be made publicly available upon publication. All the 4D Nucleome datasets can be obtained from the 4D Nucleome Data Portal (https://data.4dnucleome.org/). The accession numbers for all 4D Nucleome datasets used are provided in Table S5, available as supplementary data at Bioinformatics online. All scripts used for data processing, figure generation, and analysis are available in the following GitHub repository: https://github.com/OroujiLab/CUTandRun_Peak_Calling/, and have also been archived on Zenodo (https://doi.org/10.5281/zenodo.15208150).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The CUT&RUN data generated in this study have been deposited in the GEO under the accession number GSE282809. This dataset includes processed peak files in BED format and genome-wide signal tracks in bigWig format for visualization. Specific datasets for H3K4me3, H3K27ac, and H3K27me3 across two biological replicates are available as part of this submission. The data will be made publicly available upon publication. All the 4D Nucleome datasets can be obtained from the 4D Nucleome Data Portal (https://data.4dnucleome.org/). 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All scripts used for data processing, figure generation, and analysis are available in the following GitHub repository: https://github.com/OroujiLab/CUTandRun_Peak_Calling/, and have also been archived on Zenodo (https://doi.org/10.5281/zenodo.15208150).", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM8650794", "GSM8650795", "GSM8650796", "GSM8650797", "GSM8650798", "GSM8650799"], "sample_count": 6, "group_sizes": null, "group_sizes_basis": null, "platform": "24247", "taxon": "Mus musculus", "title": "Benchmarking Peak Calling Methods for CUT&RUN", "resolution_checked_at": "2026-11-01T20:16:08Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE282809 not downloaded", "asset:data:geo:GSE282809 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": null, "specific": null, "label": "MACS2, SEACR, GoPeaks, and LanceOtron peak calling methods, evaluated using peak number, peak-length distribution, SNR, precision, recall, F1 score, overlap analysis, and the Irreproducible Discovery Rate (IDR)", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "reordered", "quote": "Four peak calling methods were benchmarked in this study: MACS2, SEACR, GoPeaks, and LanceOtron. 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However, the effectiveness of this techniq", "or each tool. MACS2 (Model-based Analysis of ChIP-Seq) (MACS2 v2.2.9.1) was used for peak calling"], "order_basis": "paper_text_order:295", "id": "assay:1"}, {"role": "data_generation", "definition_id": "animal_model", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["marks (H3K4me3, H3K27ac, and H3K27me3) from mouse brain tissue, as well as samples from the 4D", "ntal design 2.1.1 Mouse samples Adult C57BL6 mice were used for this study. Fresh brain tissue was", "ple collection and experimental design 2.1.1 Mouse samples Adult C57BL6 mice were used for this", "n CUT&RUN datasets. This study included both mouse (mm10) and human (hg38) samples. Below, we d"], "order_basis": "paper_text_order:783", "id": "assay:2"}, {"role": "data_generation", "definition_id": "cell_culture", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["4me3, and H3K27me3) and each sample (H1_ESC, K562, HFFc6, GM12878, and Mouse-Brain), provide k", "centage of overlapping peaks was observed in K562 with 16 959 (21%), followed by 8838 (12%) in"], "order_basis": "paper_text_order:6841", "id": "assay:3"}]}