{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T08:19:07Z","timestamp":1776154747562,"version":"3.50.1"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"18","license":[{"start":{"date-parts":[[2022,7,22]],"date-time":"2022-07-22T00:00:00Z","timestamp":1658448000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R24DK106766"],"award-info":[{"award-number":["R24DK106766"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000265","name":"Medical Research Council","doi-asserted-by":"publisher","award":["MC_UU_12025"],"award-info":[{"award-number":["MC_UU_12025"]}],"id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000265","name":"Medical Research Council","doi-asserted-by":"publisher","award":["MC_UU_00016\/14"],"award-info":[{"award-number":["MC_UU_00016\/14"]}],"id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010269","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["106130\/Z\/14\/Z"],"award-info":[{"award-number":["106130\/Z\/14\/Z"]}],"id":[{"id":"10.13039\/100010269","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,9,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Genome sequencing experiments have revolutionized molecular biology by allowing researchers to identify important DNA-encoded elements genome wide. Regions where these elements are found appear as peaks in the analog signal of an assay\u2019s coverage track, and despite the ease with which humans can visually categorize these patterns, the size of many genomes necessitates algorithmic implementations. Commonly used methods focus on statistical tests to classify peaks, discounting that the background signal does not completely follow any known probability distribution and reducing the information-dense peak shapes to simply maximum height. Deep learning has been shown to be highly accurate for many pattern recognition tasks, on par or even exceeding human capabilities, providing an opportunity to reimagine and improve peak calling.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We present the peak calling framework LanceOtron, which combines deep learning for recognizing peak shape with multifaceted enrichment calculations for assessing significance. In benchmarking ATAC-seq, ChIP-seq and DNase-seq, LanceOtron outperforms long-standing, gold-standard peak callers through its improved selectivity and near-perfect sensitivity.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>A fully featured web application is freely available from LanceOtron.molbiol.ox.ac.uk, command line interface via python is pip installable from PyPI at https:\/\/pypi.org\/project\/lanceotron\/, and source code and benchmarking tests are available at https:\/\/github.com\/LHentges\/LanceOtron.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac525","type":"journal-article","created":{"date-parts":[[2022,7,22]],"date-time":"2022-07-22T14:19:17Z","timestamp":1658499557000},"page":"4255-4263","source":"Crossref","is-referenced-by-count":43,"title":["LanceOtron: a deep learning peak caller for genome sequencing experiments"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6327-6774","authenticated-orcid":false,"given":"Lance D","family":"Hentges","sequence":"first","affiliation":[{"name":"MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford , Oxford OX3 9DS, UK"},{"name":"MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford , Oxford OX3 9DS, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7264-2668","authenticated-orcid":false,"given":"Martin J","family":"Sergeant","sequence":"additional","affiliation":[{"name":"MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford , Oxford OX3 9DS, UK"},{"name":"MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford , Oxford OX3 9DS, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6733-633X","authenticated-orcid":false,"given":"Christopher B","family":"Cole","sequence":"additional","affiliation":[{"name":"MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford , Oxford OX3 9DS, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5034-0869","authenticated-orcid":false,"given":"Damien J","family":"Downes","sequence":"additional","affiliation":[{"name":"MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford , Oxford OX3 9DS, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8955-7256","authenticated-orcid":false,"given":"Jim R","family":"Hughes","sequence":"additional","affiliation":[{"name":"MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford , Oxford OX3 9DS, UK"},{"name":"MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford , Oxford OX3 9DS, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3559-4334","authenticated-orcid":false,"given":"Stephen","family":"Taylor","sequence":"additional","affiliation":[{"name":"MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford , Oxford OX3 9DS, UK"}]}],"member":"286","published-online":{"date-parts":[[2022,7,22]]},"reference":[{"key":"2023110303230463900_btac525-B1","doi-asserted-by":"crossref","first-page":"2407","DOI":"10.1016\/j.jmb.2019.04.045","article-title":"refTSS: a reference data set for human and mouse transcription start sites","volume":"431","author":"Abugessaisa","year":"2019","journal-title":"J. Mol. Biol"},{"key":"2023110303230463900_btac525-B2","doi-asserted-by":"crossref","first-page":"9354","DOI":"10.1038\/s41598-019-45839-z","article-title":"The ENCODE blacklist: identification of problematic regions of the","volume":"9","author":"Amemiya","year":"2019","journal-title":"Sci. Rep"},{"key":"2023110303230463900_btac525-B3","doi-asserted-by":"crossref","first-page":"14926","DOI":"10.1073\/pnas.0905443106","article-title":"Mapping accessible chromatin regions using Sono-Seq","volume":"106","author":"Auerbach","year":"2009","journal-title":"Proc. Natl. Acad. Sci. U S A"},{"key":"2023110303230463900_btac525-B4","doi-asserted-by":"crossref","first-page":"W39","DOI":"10.1093\/nar\/gkv416","article-title":"The MEME suite","volume":"43","author":"Bailey","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2023110303230463900_btac525-B5","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1145\/2988450.2988454","volume-title":"Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, DLRS 2016","author":"Cheng","year":"2016"},{"key":"2023110303230463900_btac525-B6","doi-asserted-by":"crossref","first-page":"D794","DOI":"10.1093\/nar\/gkx1081","article-title":"The Encyclopedia of DNA Elements (ENCODE): data portal update","volume":"46","author":"Davis","year":"2018","journal-title":"Nucleic Acids Res"},{"key":"2023110303230463900_btac525-B7","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1038\/nature11247","article-title":"An integrated Encyclopedia of DNA Elements in the human genome","volume":"489","author":"ENCODE Project Consortium","year":"2012","journal-title":"Nature"},{"key":"2023110303230463900_btac525-B8","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.1093\/bioinformatics\/btr064","article-title":"FIMO: scanning for occurrences of a given motif","volume":"27","author":"Grant","year":"2011","journal-title":"Bioinformatics"},{"key":"2023110303230463900_btac525-B9","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1016\/j.molcel.2010.05.004","article-title":"Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities","volume":"38","author":"Heinz","year":"2010","journal-title":"Mol. Cell"},{"key":"2023110303230463900_btac525-B10","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":"2023110303230463900_btac525-B11","doi-asserted-by":"crossref","first-page":"20120369","DOI":"10.1098\/rstb.2012.0369","article-title":"CTCF: the protein, the binding partners, the binding sites and their chromatin loops","volume":"368","author":"Holwerda","year":"2013","journal-title":"Philos. Trans. R Soc. Lond. B Biol. Sci"},{"key":"2023110303230463900_btac525-B12","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1038\/s41586-021-03639-4","article-title":"Defining genome architecture at base-pair resolution","volume":"595","author":"Hua","year":"2021","journal-title":"Nature"},{"key":"2023110303230463900_btac525-B13","first-page":"20150202","article-title":"Principal component analysis: a review and recent developments","volume":"374","author":"Jolliffe","year":"2016","journal-title":"Philos. Trans. A Math. Phys. Eng. Sci"},{"key":"2023110303230463900_btac525-B14","doi-asserted-by":"crossref","first-page":"990","DOI":"10.1101\/gr.200535.115","article-title":"Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks","volume":"26","author":"Kelley","year":"2016","journal-title":"Genome Res"},{"key":"2023110303230463900_btac525-B15","doi-asserted-by":"crossref","first-page":"996","DOI":"10.1101\/gr.229102","article-title":"The human genome browser at UCSC","volume":"12","author":"Kent","year":"2002","journal-title":"Genome Res"},{"key":"2023110303230463900_btac525-B16","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1007\/s10577-019-09619-9","article-title":"Genomic methods in profiling DNA accessibility and factor localization","volume":"28","author":"Klein","year":"2020","journal-title":"Chromosome Res"},{"key":"2023110303230463900_btac525-B17","doi-asserted-by":"crossref","first-page":"1813","DOI":"10.1101\/gr.136184.111","article-title":"ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia","volume":"22","author":"Landt","year":"2012","journal-title":"Genome Res"},{"key":"2023110303230463900_btac525-B18","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1038\/nmeth.1923","article-title":"Fast gapped-read alignment with bowtie 2","volume":"9","author":"Langmead","year":"2012","journal-title":"Nat. Methods"},{"key":"2023110303230463900_btac525-B19","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"2023110303230463900_btac525-B20","doi-asserted-by":"crossref","first-page":"1752","DOI":"10.1214\/11-AOAS466","article-title":"Measuring reproducibility of high-throughput experiments","volume":"5","author":"Li","year":"2011","journal-title":"Ann. Appl. Stat"},{"key":"2023110303230463900_btac525-B21","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res"},{"key":"2023110303230463900_btac525-B22","author":"McInnes","year":"2018"},{"key":"2023110303230463900_btac525-B23","doi-asserted-by":"crossref","first-page":"7933","DOI":"10.1038\/s41598-020-64655-4","article-title":"CNN-Peaks: ChIP-Seq peak detection pipeline using convolutional neural networks that imitate human visual inspection","volume":"10","author":"Oh","year":"2020","journal-title":"Sci. Rep"},{"key":"2023110303230463900_btac525-B24","doi-asserted-by":"crossref","first-page":"3120","DOI":"10.1111\/febs.15544","article-title":"Serum response factor-cofactor interactions and their implications in disease","volume":"288","author":"Onuh","year":"2021","journal-title":"FEBS J"},{"key":"2023110303230463900_btac525-B25","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1038\/nrg2641","article-title":"ChIP\u2013seq: advantages and challenges of a maturing technology","volume":"10","author":"Park","year":"2009","journal-title":"Nat. Rev. Genet"},{"key":"2023110303230463900_btac525-B26","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":"2023110303230463900_btac525-B27","doi-asserted-by":"crossref","first-page":"W187","DOI":"10.1093\/nar\/gku365","article-title":"deepTools: a flexible platform for exploring deep-sequencing data","volume":"42","author":"Ram\u00edrez","year":"2014","journal-title":"Nucleic Acids Res"},{"key":"2023110303230463900_btac525-B28","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1038\/nbt.1754","article-title":"Integrative genomics viewer","volume":"29","author":"Robinson","year":"2011","journal-title":"Nat. Biotechnol"},{"key":"2023110303230463900_btac525-B29","doi-asserted-by":"crossref","first-page":"e25","DOI":"10.1093\/nar\/gkq1187","article-title":"A manually curated ChIP-seq benchmark demonstrates room for improvement in current peak-finder programs","volume":"39","author":"Rye","year":"2011","journal-title":"Nucleic Acids Res"},{"key":"2023110303230463900_btac525-B30","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1038\/s42003-021-02097-y","article-title":"Multi locus view: an extensible web-based tool for the analysis of genomic data","volume":"4","author":"Sergeant","year":"2021","journal-title":"Commun. Biol"},{"key":"2023110303230463900_btac525-B31","doi-asserted-by":"crossref","first-page":"e173","DOI":"10.1093\/nar\/gkx799","article-title":"Ritornello: high fidelity control-free chromatin immunoprecipitation peak calling","volume":"45","author":"Stanton","year":"2017","journal-title":"Nucleic Acids Res"},{"key":"2023110303230463900_btac525-B32","doi-asserted-by":"crossref","first-page":"e91","DOI":"10.1093\/nar\/gkz533","article-title":"HMMRATAC: a hidden Markov ModeleR for ATAC-seq","volume":"47","author":"Tarbell","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2023110303230463900_btac525-B33","first-page":"441","article-title":"Features that define the best ChIP-seq peak calling algorithms","volume":"18","author":"Thomas","year":"2017","journal-title":"Brief. Bioinform"},{"key":"2023110303230463900_btac525-B34","doi-asserted-by":"crossref","first-page":"e5241","DOI":"10.1371\/journal.pone.0005241","article-title":"Inherent signals in sequencing-based chromatin-immunoprecipitation control libraries","volume":"4","author":"Vega","year":"2009","journal-title":"PLoS One"},{"key":"2023110303230463900_btac525-B35","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1038\/nbt.4233","article-title":"Deep learning in biomedicine","volume":"36","author":"Wainberg","year":"2018","journal-title":"Nat. Biotechnol"},{"key":"2023110303230463900_btac525-B36","doi-asserted-by":"crossref","first-page":"e11471","DOI":"10.1371\/journal.pone.0011471","article-title":"Evaluation of algorithm performance in ChIP-seq peak detection","volume":"5","author":"Wilbanks","year":"2010","journal-title":"PLoS One"},{"key":"2023110303230463900_btac525-B37","doi-asserted-by":"crossref","first-page":"e0169249","DOI":"10.1371\/journal.pone.0169249","article-title":"Accurate promoter and enhancer identification in 127 ENCODE and roadmap epigenomics cell types and tissues by GenoSTAN","volume":"12","author":"Zacher","year":"2017","journal-title":"PLoS One"},{"key":"2023110303230463900_btac525-B38","doi-asserted-by":"crossref","first-page":"1952","DOI":"10.1093\/bioinformatics\/btp340","article-title":"A clustering approach for identification of enriched domains from histone modification ChIP-Seq data","volume":"25","author":"Zang","year":"2009","journal-title":"Bioinformatics"},{"key":"2023110303230463900_btac525-B39","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"},{"key":"2023110303230463900_btac525-B40","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1186\/s12859-021-04097-5","article-title":"A flexible ChIP-sequencing simulation toolkit","volume":"22","author":"Zheng","year":"2021","journal-title":"BMC Bioinformatics"},{"key":"2023110303230463900_btac525-B41","doi-asserted-by":"crossref","first-page":"931","DOI":"10.1038\/nmeth.3547","article-title":"Predicting effects of noncoding variants with deep learning\u2013based sequence model","volume":"12","author":"Zhou","year":"2015","journal-title":"Nat. Methods"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btac525\/45494376\/btac525.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/38\/18\/4255\/52710834\/btac525.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/38\/18\/4255\/52710834\/btac525.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,3]],"date-time":"2023-11-03T03:23:29Z","timestamp":1698981809000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/38\/18\/4255\/6648462"}},"subtitle":[],"editor":[{"given":"Valentina","family":"Boeva","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2022,7,22]]},"references-count":41,"journal-issue":{"issue":"18","published-print":{"date-parts":[[2022,9,15]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btac525","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,9,15]]},"published":{"date-parts":[[2022,7,22]]}}}