{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:35Z","timestamp":1772138075636,"version":"3.50.1"},"reference-count":12,"publisher":"Oxford University Press (OUP)","issue":"7","license":[{"start":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T00:00:00Z","timestamp":1750896000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000024","name":"Canadian Institutes of Health Research","doi-asserted-by":"publisher","award":["CIHR | PJT-173283"],"award-info":[{"award-number":["CIHR | PJT-173283"]}],"id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000024","name":"Canadian Institutes of Health Research","doi-asserted-by":"publisher","award":["PJT-190226"],"award-info":[{"award-number":["PJT-190226"]}],"id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Cleavage Under Targets and Release Using Nuclease (CUT&amp;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&amp;RUN data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Here, we benchmark four prominent peak calling tools, MACS2, SEACR, GoPeaks, and LanceOtron, evaluating their performance in identifying peaks from CUT&amp;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&amp;RUN experiments, ultimately enhancing the study of chromatin dynamics and transcriptional regulation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The CUT&amp;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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf375","type":"journal-article","created":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T07:59:07Z","timestamp":1750838347000},"source":"Crossref","is-referenced-by-count":1,"title":["Benchmarking peak calling methods for CUT&amp;RUN"],"prefix":"10.1093","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-4265-2406","authenticated-orcid":false,"given":"Amin","family":"Nooranikhojasteh","sequence":"first","affiliation":[{"name":"Princess Margaret Cancer Centre, University Health Network (UHN) , Toronto, ON M5G 1L7,","place":["Canada"]}]},{"given":"Ghazaleh","family":"Tavallaee","sequence":"additional","affiliation":[{"name":"Princess Margaret Cancer Centre, University Health Network (UHN) , Toronto, ON M5G 1L7,","place":["Canada"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7413-1383","authenticated-orcid":false,"given":"Elias","family":"Orouji","sequence":"additional","affiliation":[{"name":"Princess Margaret Cancer Centre, University Health Network (UHN) , Toronto, ON M5G 1L7,","place":["Canada"]}]}],"member":"286","published-online":{"date-parts":[[2025,6,26]]},"reference":[{"key":"2025071304171257300_btaf375-B1","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1038\/nature23884","article-title":"The 4D nucleome project","volume":"549","author":"Dekker","year":"2017","journal-title":"Nature"},{"key":"2025071304171257300_btaf375-B2","doi-asserted-by":"crossref","first-page":"4255","DOI":"10.1093\/bioinformatics\/btac525","article-title":"LanceOtron: a deep learning peak caller for genome sequencing experiments","volume":"38","author":"Hentges","year":"2022","journal-title":"Bioinformatics"},{"key":"2025071304171257300_btaf375-B3","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1093\/bioinformatics\/btw672","article-title":"Optimizing ChIP-seq peak detectors using visual labels and supervised machine learning","volume":"33","author":"Hocking","year":"2017","journal-title":"Bioinformatics"},{"key":"2025071304171257300_btaf375-B4","doi-asserted-by":"crossref","first-page":"e1003118","DOI":"10.1371\/journal.pcbi.1003118","article-title":"Software for computing and annotating genomic ranges","volume":"9","author":"Lawrence","year":"2013","journal-title":"PLoS Comput Biol"},{"key":"2025071304171257300_btaf375-B5","doi-asserted-by":"crossref","DOI":"10.7554\/eLife.46314","article-title":"Improved CUT&RUN chromatin profiling tools","volume":"8","author":"Meers","year":"2019","journal-title":"Elife"},{"key":"2025071304171257300_btaf375-B6","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1186\/s13072-019-0287-4","article-title":"Peak calling by sparse enrichment analysis for CUT&RUN chromatin profiling","volume":"12","author":"Meers","year":"2019","journal-title":"Epigenetics Chromatin"},{"key":"2025071304171257300_btaf375-B7","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1093\/bioinformatics\/btq033","article-title":"BEDTools: a flexible suite of utilities for comparing genomic features","volume":"26","author":"Quinlan","year":"2010","journal-title":"Bioinformatics"},{"key":"2025071304171257300_btaf375-B8","doi-asserted-by":"crossref","first-page":"W160","DOI":"10.1093\/nar\/gkw257","article-title":"deepTools2: a next generation web server for deep-sequencing data analysis","volume":"44","author":"Ram\u00edrez","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2025071304171257300_btaf375-B9","doi-asserted-by":"crossref","first-page":"1006","DOI":"10.1038\/nprot.2018.015","article-title":"Targeted in situ genome-wide profiling with high efficiency for low cell numbers","volume":"13","author":"Skene","year":"2018","journal-title":"Nat Protoc"},{"key":"2025071304171257300_btaf375-B10","first-page":"1133","article-title":"Unsupervised contrastive peak caller for ATAC-seq","volume":"33","author":"Vu","year":"2023","journal-title":"Genome Res"},{"key":"2025071304171257300_btaf375-B11","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1186\/s13059-022-02707-w","article-title":"GoPeaks: histone modification peak calling for CUT&tag","volume":"23","author":"Yashar","year":"2022","journal-title":"Genome Biol"},{"key":"2025071304171257300_btaf375-B12","doi-asserted-by":"crossref","first-page":"R137","DOI":"10.1186\/gb-2008-9-9-r137","article-title":"Model-based analysis of ChIP-Seq (MACS)","volume":"9","author":"Zhang","year":"2008","journal-title":"Genome Biol"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btaf375\/63590816\/btaf375.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/41\/7\/btaf375\/63590816\/btaf375.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/41\/7\/btaf375\/63590816\/btaf375.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,13]],"date-time":"2025-07-13T08:17:20Z","timestamp":1752394640000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/doi\/10.1093\/bioinformatics\/btaf375\/8174968"}},"subtitle":[],"editor":[{"given":"Can","family":"Alkan","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2025,6,26]]},"references-count":12,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,7,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btaf375","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2024.11.13.622880","asserted-by":"object"}]},"ISSN":["1367-4811"],"issn-type":[{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,7]]},"published":{"date-parts":[[2025,6,26]]},"article-number":"btaf375"}}