{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:49Z","timestamp":1772138089589,"version":"3.50.1"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"20","license":[{"start":{"date-parts":[[2018,5,10]],"date-time":"2018-05-10T00:00:00Z","timestamp":1525910400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["GM113245-01"],"award-info":[{"award-number":["GM113245-01"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,10,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Transcription factor (TF) binds to the promoter region of a gene to control gene expression. Identifying precise TF binding sites (TFBSs) is essential for understanding the detailed mechanisms of TF-mediated gene regulation. However, there is a shortage of computational approach that can deliver single base pair resolution prediction of TFBS.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In this paper, we propose DeepSNR, a Deep Learning algorithm for predicting TF binding location at Single Nucleotide Resolution de novo from DNA sequence. DeepSNR adopts a novel deconvolutional network (deconvNet) model and is inspired by the similarity to image segmentation by deconvNet. The proposed deconvNet architecture is constructed on top of \u2018DeepBind\u2019 and we trained the entire model using TF-specific data from ChIP-exonuclease (ChIP-exo) experiments. DeepSNR has been shown to outperform motif search\u2013based methods for several evaluation metrics. We have also demonstrated the usefulness of DeepSNR in the regulatory analysis of TFBS as well as in improving the TFBS prediction specificity using ChIP-seq data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>DeepSNR is available open source in the GitHub repository (https:\/\/github.com\/sirajulsalekin\/DeepSNR)<\/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\/bty383","type":"journal-article","created":{"date-parts":[[2018,5,3]],"date-time":"2018-05-03T19:48:46Z","timestamp":1525376926000},"page":"3446-3453","source":"Crossref","is-referenced-by-count":26,"title":["Base-pair resolution detection of transcription factor binding site by deep deconvolutional network"],"prefix":"10.1093","volume":"34","author":[{"given":"Sirajul","family":"Salekin","sequence":"first","affiliation":[{"name":"Electrical and Computer Engineering Department, University of Texas at San Antonio, San Antonio, TX, USA"}]},{"given":"Jianqiu Michelle","family":"Zhang","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering Department, University of Texas at San Antonio, San Antonio, TX, USA"}]},{"given":"Yufei","family":"Huang","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering Department, University of Texas at San Antonio, San Antonio, TX, USA"},{"name":"Department of Epidemiology and Biostatistics, University of Texas Health Science Center, San Antonio, TX, USA"}]}],"member":"286","published-online":{"date-parts":[[2018,5,10]]},"reference":[{"key":"2023012712425953800_bty383-B1","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. 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