{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T02:37:25Z","timestamp":1773283045937,"version":"3.50.1"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"Supplement_2","license":[{"start":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T00:00:00Z","timestamp":1606780800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,12,30]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Transcription factor (TF) DNA-binding is a central mechanism in gene regulation. Biologists would like to know where and when these factors bind DNA. Hence, they require accurate DNA-binding models to enable binding prediction to any DNA sequence. Recent technological advancements measure the binding of a single TF to thousands of DNA sequences. One of the prevailing techniques, high-throughput SELEX, measures protein\u2013DNA binding by high-throughput sequencing over several cycles of enrichment. Unfortunately, current computational methods to infer the binding preferences from high-throughput SELEX data do not exploit the richness of these data, and are under-using the most advanced computational technique, deep neural networks.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>To better characterize the binding preferences of TFs from these experimental data, we developed DeepSELEX, a new algorithm to infer intrinsic DNA-binding preferences using deep neural networks. DeepSELEX takes advantage of the richness of high-throughput sequencing data and learns the DNA-binding preferences by observing the changes in DNA sequences through the experimental cycles. DeepSELEX outperforms extant methods for the task of DNA-binding inference from high-throughput SELEX data in binding prediction in vitro and is on par with the state of the art in in vivo binding prediction. Analysis of model parameters reveals it learns biologically relevant features that shed light on TFs\u2019 binding mechanism.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>DeepSELEX is available through github.com\/OrensteinLab\/DeepSELEX\/.<\/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\/btaa789","type":"journal-article","created":{"date-parts":[[2020,10,17]],"date-time":"2020-10-17T19:10:16Z","timestamp":1602961816000},"page":"i634-i642","source":"Crossref","is-referenced-by-count":28,"title":["DeepSELEX: inferring DNA-binding preferences from HT-SELEX data using multi-class CNNs"],"prefix":"10.1093","volume":"36","author":[{"given":"Maor","family":"Asif","sequence":"first","affiliation":[{"name":"School of Electrical and Computer Engineering, Ben-Gurion University of the Negev , Beer-Sheva 8410501, Israel"}]},{"given":"Yaron","family":"Orenstein","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, Ben-Gurion University of the Negev , Beer-Sheva 8410501, Israel"}]}],"member":"286","published-online":{"date-parts":[[2020,12,29]]},"reference":[{"key":"2023062409330040700_btaa789-B1","first-page":"2623","author":"Akiba","year":"2019"},{"key":"2023062409330040700_btaa789-B2","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1038\/nbt.3300","article-title":"Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning","volume":"33","author":"Alipanahi","year":"2015","journal-title":"Nat. Biotechnol"},{"key":"2023062409330040700_btaa789-B3","first-page":"357","author":"Barshai","year":"2019"},{"key":"2023062409330040700_btaa789-B4","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/S0092-8674(04)00304-6","article-title":"Predicting gene expression from sequence","volume":"117","author":"Beer","year":"2004","journal-title":"Cell"},{"key":"2023062409330040700_btaa789-B5","doi-asserted-by":"crossref","first-page":"1429","DOI":"10.1038\/nbt1246","article-title":"Compact, universal DNA microarrays to comprehensively determine transcription-factor binding site specificities","volume":"24","author":"Berger","year":"2006","journal-title":"Nat. Biotechnol"},{"key":"2023062409330040700_btaa789-B6","first-page":"281","article-title":"Random search for hyper-parameter optimization","volume":"13","author":"Bergstra","year":"2012","journal-title":"J. Mach. Learn. Res"},{"key":"2023062409330040700_btaa789-B7","doi-asserted-by":"crossref","first-page":"774","DOI":"10.1038\/316774a0","article-title":"Control of eukaryotic messenger RNA synthesis by sequence-specific DNA-binding proteins","volume":"316","author":"Dynan","year":"1985","journal-title":"Nature"},{"key":"2023062409330040700_btaa789-B8","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1101\/gr.247494.118","article-title":"Deep neural networks for interpreting RNA-binding protein target preferences","volume":"30","author":"Ghanbari","year":"2020","journal-title":"Genome Res"},{"key":"2023062409330040700_btaa789-B10","doi-asserted-by":"crossref","first-page":"D117","DOI":"10.1093\/nar\/gku1045","article-title":"UniPROBE, update 2015: new tools and content for the online database of protein-binding microarray data on protein\u2013DNA interactions","volume":"43","author":"Hume","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2023062409330040700_btaa789-B11","doi-asserted-by":"crossref","first-page":"1497","DOI":"10.1126\/science.1141319","article-title":"Genome-wide mapping of in vivo protein\u2013DNA interactions","volume":"316","author":"Johnson","year":"2007","journal-title":"Science"},{"key":"2023062409330040700_btaa789-B12","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/978-90-481-9069-0_7","volume-title":"A Handbook of Transcription Factors","author":"Jolma","year":"2011"},{"key":"2023062409330040700_btaa789-B13","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1101\/gr.100552.109","article-title":"Multiplexed massively parallel SELEX for characterization of human transcription factor binding specificities","volume":"20","author":"Jolma","year":"2010","journal-title":"Genome Res"},{"key":"2023062409330040700_btaa789-B14","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.cell.2012.12.009","article-title":"DNA-binding specificities of human transcription factors","volume":"152","author":"Jolma","year":"2013","journal-title":"Cell"},{"key":"2023062409330040700_btaa789-B15","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1016\/j.molcel.2014.04.016","article-title":"RNA Bind-n-Seq: quantitative assessment of the sequence and structural binding specificity of RNA binding proteins","volume":"54","author":"Lambert","year":"2014","journal-title":"Mol. Cell"},{"key":"2023062409330040700_btaa789-B16","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1126\/science.2667136","article-title":"Transcriptional regulation in mammalian cells by sequence-specific DNA binding proteins","volume":"245","author":"Mitchell","year":"1989","journal-title":"Science"},{"key":"2023062409330040700_btaa789-B17","first-page":"279","article-title":"Recent advances in ChIP-seq analysis: from quality management to whole-genome annotation","volume":"18","author":"Nakato","year":"2017","journal-title":"Brief. Bioinf"},{"key":"2023062409330040700_btaa789-B18","doi-asserted-by":"crossref","first-page":"e04837","DOI":"10.7554\/eLife.04837","article-title":"Conservation of transcription factor binding specificities across 600 million years of bilateria evolution","volume":"4","author":"Nitta","year":"2015","journal-title":"Elife"},{"key":"2023062409330040700_btaa789-B19","doi-asserted-by":"crossref","first-page":"e63","DOI":"10.1093\/nar\/gku117","article-title":"A comparative analysis of transcription factor binding models learned from PBM, HT-SELEX and ChIP data","volume":"42","author":"Orenstein","year":"2014","journal-title":"Nucleic Acids Res"},{"key":"2023062409330040700_btaa789-B20","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1038\/nrg2641","article-title":"ChIP-seq: advantages and challenges of a maturing technology","volume":"10","author":"Park","year":"2009","journal-title":"Nat. Rev. Genet"},{"key":"2023062409330040700_btaa789-B21","doi-asserted-by":"crossref","first-page":"E3692","DOI":"10.1073\/pnas.1714376115","article-title":"Accurate and sensitive quantification of protein\u2013DNA binding affinity","volume":"115","author":"Rastogi","year":"2018","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023062409330040700_btaa789-B22","doi-asserted-by":"crossref","first-page":"e06397","DOI":"10.7554\/eLife.06397","article-title":"Building accurate sequence-to-affinity models from high-throughput in vitro protein\u2013DNA binding data using FeatureREDUCE","volume":"4","author":"Riley","year":"2015","journal-title":"Elife"},{"key":"2023062409330040700_btaa789-B23","doi-asserted-by":"crossref","first-page":"2288","DOI":"10.1093\/bioinformatics\/btx191","article-title":"BEESEM: estimation of binding energy models using HT-SELEX data","volume":"33","author":"Ruan","year":"2017","journal-title":"Bioinformatics"},{"key":"2023062409330040700_btaa789-B24","author":"Shrikumar","year":"2018"},{"key":"2023062409330040700_btaa789-B25","doi-asserted-by":"crossref","first-page":"D726","DOI":"10.1093\/nar\/gkv1160","article-title":"Encode data at the ENCODE portal","volume":"44","author":"Sloan","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2023062409330040700_btaa789-B26","first-page":"2951","volume-title":"Advances in Neural Information Processing Systems 2012","author":"Snoek","year":"2012"},{"key":"2023062409330040700_btaa789-B27","doi-asserted-by":"crossref","first-page":"751","DOI":"10.1038\/nrg2845","article-title":"Determining the specificity of protein\u2013DNA interactions","volume":"11","author":"Stormo","year":"2010","journal-title":"Nat. Rev. Genet"},{"key":"2023062409330040700_btaa789-B28","first-page":"3319","author":"Sundararajan","year":"2017"},{"key":"2023062409330040700_btaa789-B29","doi-asserted-by":"crossref","first-page":"e44","DOI":"10.1093\/nar\/gky027","article-title":"Modular discovery of monomeric and dimeric transcription factor binding motifs for large data sets","volume":"46","author":"Toivonen","year":"2018","journal-title":"Nucleic Acids Res"},{"key":"2023062409330040700_btaa789-B30","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1038\/nbt.2486","article-title":"Evaluation of methods for modeling transcription factor sequence specificity","volume":"31","author":"Weirauch","year":"2013","journal-title":"Nat. Biotechnol"},{"key":"2023062409330040700_btaa789-B31","doi-asserted-by":"crossref","first-page":"1431","DOI":"10.1016\/j.cell.2014.08.009","article-title":"Determination and inference of eukaryotic transcription factor sequence specificity","volume":"158","author":"Weirauch","year":"2014","journal-title":"Cell"},{"key":"2023062409330040700_btaa789-B32","doi-asserted-by":"crossref","first-page":"910","DOI":"10.15252\/msb.20167238","article-title":"Transcription factor family-specific DNA shape readout revealed by quantitative specificity models","volume":"13","author":"Yang","year":"2017","journal-title":"Mol. Syst. Biol"},{"key":"2023062409330040700_btaa789-B33","doi-asserted-by":"crossref","first-page":"858","DOI":"10.1038\/s41592-019-0511-y","article-title":"BindSpace decodes transcription factor binding signals by large-scale sequence embedding","volume":"16","author":"Yuan","year":"2019","journal-title":"Nat. Methods"},{"key":"2023062409330040700_btaa789-B34","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1038\/nbt.1893","article-title":"Quantitative analysis demonstrates most transcription factors require only simple models of specificity","volume":"29","author":"Zhao","year":"2011","journal-title":"Nat. Biotechnol"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/36\/Supplement_2\/i634\/50693559\/btaa789.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/36\/Supplement_2\/i634\/50693559\/btaa789.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,24]],"date-time":"2023-06-24T23:55:44Z","timestamp":1687650944000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/36\/Supplement_2\/i634\/6055905"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12]]},"references-count":33,"journal-issue":{"issue":"Supplement_2","published-print":{"date-parts":[[2020,12,30]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btaa789","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2020,12]]},"published":{"date-parts":[[2020,12]]}}}