{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T00:13:46Z","timestamp":1778285626157,"version":"3.51.4"},"reference-count":56,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2021,3,4]],"date-time":"2021-03-04T00:00:00Z","timestamp":1614816000000},"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\/501100001809","name":"National Nature Scientific Foundation of China","doi-asserted-by":"crossref","award":["61772119"],"award-info":[{"award-number":["61772119"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Distinguished Young Scholars","award":["2020JDJQ0012"],"award-info":[{"award-number":["2020JDJQ0012"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,9,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>DNase I hypersensitive site (DHS) refers to the hypersensitive region of chromatin for the DNase I enzyme. It is an important part of the noncoding region and contains a variety of regulatory elements, such as promoter, enhancer, and transcription factor-binding site, etc. Moreover, the related locus of disease (or trait) are usually enriched in the DHS regions. Therefore, the detection of DHS region is of great significance. In this study, we develop a deep learning-based algorithm to identify whether an unknown sequence region would be potential DHS. The proposed method showed high prediction performance on both training datasets and independent datasets in different cell types and developmental stages, demonstrating that the method has excellent superiority in the identification of DHSs. Furthermore, for the convenience of related wet-experimental researchers, the user-friendly web-server iDHS-Deep was established at http:\/\/lin-group.cn\/server\/iDHS-Deep\/, by which users can easily distinguish DHS and non-DHS and obtain the corresponding developmental stage ofDHS.<\/jats:p>","DOI":"10.1093\/bib\/bbab047","type":"journal-article","created":{"date-parts":[[2021,1,29]],"date-time":"2021-01-29T20:12:34Z","timestamp":1611951154000},"source":"Crossref","is-referenced-by-count":35,"title":["iDHS-Deep: an integrated tool for predicting DNase I hypersensitive sites by deep neural network"],"prefix":"10.1093","volume":"22","author":[{"given":"Fu-Ying","family":"Dao","sequence":"first","affiliation":[{"name":"Informational Biology at University of Electronic Science and Technology of China, China"}]},{"given":"Hao","family":"Lv","sequence":"additional","affiliation":[{"name":"Informational Biology at University of Electronic Science and Technology of China, China"}]},{"given":"Wei","family":"Su","sequence":"additional","affiliation":[{"name":"Informational Biology at University of Electronic Science and Technology of China, China"}]},{"given":"Zi-Jie","family":"Sun","sequence":"additional","affiliation":[{"name":"Informational Biology at University of Electronic Science and Technology of China, China"}]},{"given":"Qin-Lai","family":"Huang","sequence":"additional","affiliation":[{"name":"Informational Biology at University of Electronic Science and Technology of China, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6265-2862","authenticated-orcid":false,"given":"Hao","family":"Lin","sequence":"additional","affiliation":[{"name":"Informational Biology at University of Electronic Science and Technology of China, China"}]}],"member":"286","published-online":{"date-parts":[[2021,3,4]]},"reference":[{"key":"2021090815131312000_ref1","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1016\/0092-8674(81)90381-0","article-title":"DNAase I-hypersensitive sites of chromatin","volume":"27","author":"Elgin","year":"1981","journal-title":"Cell"},{"key":"2021090815131312000_ref2","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1038\/nrg3095","article-title":"Cis-regulatory elements: molecular mechanisms and evolutionary processes underlying divergence","volume":"13","author":"Wittkopp","year":"2012","journal-title":"Nat Rev Genet"},{"key":"2021090815131312000_ref3","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.1016\/j.csbj.2019.09.002","article-title":"The spatial binding model of the pioneer factor Oct4 with its target genes during cell reprogramming","volume":"17","author":"Li","year":"2019","journal-title":"Comput Struct Biotechnol J"},{"key":"2021090815131312000_ref4","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1038\/s41586-020-2559-3","article-title":"Index and biological spectrum of human DNase I hypersensitive sites","volume":"584","author":"Meuleman","year":"2020","journal-title":"Nature"},{"key":"2021090815131312000_ref5","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/s41467-017-00100-x","article-title":"Identifying DNase I hypersensitive sites as driver distal regulatory elements in breast cancer","volume":"8","author":"M","year":"2017","journal-title":"Nat Commun"},{"key":"2021090815131312000_ref6","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1016\/j.jalz.2016.10.005","article-title":"A candidate regulatory variant at the TREM gene cluster associates with decreased Alzheimer's disease risk and increased TREML1 and TREM2 brain gene expression","volume":"13","author":"Carrasquillo","year":"2017","journal-title":"Alzheimers Dement"},{"key":"2021090815131312000_ref7","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0165893","article-title":"Extensive association of common disease variants with regulatory sequence","volume":"11","author":"Mokry","year":"2016","journal-title":"PLoS One"},{"key":"2021090815131312000_ref8","doi-asserted-by":"crossref","first-page":"308","DOI":"10.3389\/fgene.2014.00308","article-title":"Genome-wide mapping of DNase I hypersensitive sites and association analysis with gene expression in MSB1 cells","volume":"5","author":"He","year":"2014","journal-title":"Front Genet"},{"key":"2021090815131312000_ref9","doi-asserted-by":"crossref","first-page":"1375","DOI":"10.1016\/j.cell.2016.05.050","article-title":"Establishing chromatin regulatory landscape during mouse preimplantation development","volume":"165","author":"Lu","year":"2016","journal-title":"Cell"},{"key":"2021090815131312000_ref10","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1186\/s12920-016-0220-7","article-title":"Immunoseq: the identification of functionally relevant variants through targeted capture and sequencing of active regulatory regions in human immune cells","volume":"9","author":"Morin","year":"2016","journal-title":"BMC Med Genomics"},{"key":"2021090815131312000_ref11","doi-asserted-by":"crossref","DOI":"10.1101\/pdb.prot5384","article-title":"DNase-seq: a high-resolution technique for mapping active gene regulatory elements across the genome from mammalian cells","volume":"2010","author":"Song","year":"2010","journal-title":"Cold Spring Harb Protoc"},{"key":"2021090815131312000_ref12","first-page":"87","article-title":"Unveiling the gene regulatory landscape in diseases through the identification of DNase I-hypersensitive sites","volume":"11","author":"Chen","year":"2019","journal-title":"Biomed Rep"},{"issue":"Suppl 1","key":"2021090815131312000_ref13","doi-asserted-by":"crossref","first-page":"i338","DOI":"10.1093\/bioinformatics\/bti1047","article-title":"Predicting the in vivo signature of human gene regulatory sequences","volume":"21","author":"Noble","year":"2005","journal-title":"Bioinformatics"},{"key":"2021090815131312000_ref14","doi-asserted-by":"crossref","first-page":"740506","DOI":"10.1155\/2014\/740506","article-title":"Prediction of DNase I hypersensitive sites by using pseudo nucleotide compositions","volume":"2014","author":"Feng","year":"2014","journal-title":"Scientific World Journal"},{"key":"2021090815131312000_ref15","doi-asserted-by":"crossref","first-page":"2411","DOI":"10.1093\/bioinformatics\/btw186","article-title":"iDHS-EL: identifying DNase I hypersensitive sites by fusing three different modes of pseudo nucleotide composition into an ensemble learning framework","volume":"32","author":"Liu","year":"2016","journal-title":"Bioinformatics"},{"key":"2021090815131312000_ref16","doi-asserted-by":"crossref","DOI":"10.2174\/1570178614666170213102455","article-title":"iDHSs-PseTNC: identifying DNase I hypersensitive sites with pseuo trinucleotide component by deep sparse auto-encoder","volume":"14","author":"Xu","year":"2017","journal-title":"Letters in Organic Chemistry"},{"key":"2021090815131312000_ref17","doi-asserted-by":"crossref","first-page":"1944","DOI":"10.18632\/oncotarget.23099","article-title":"DHSpred: support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest","volume":"9","author":"Manavalan","year":"2018","journal-title":"Oncotarget"},{"key":"2021090815131312000_ref18","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1080\/1062936X.2019.1615546","article-title":"iDHS-DMCAC: identifying DNase I hypersensitive sites with balanced dinucleotide-based detrending moving-average cross-correlation coefficient","volume":"30","author":"Liang","year":"2019","journal-title":"SAR QSAR Environ Res"},{"key":"2021090815131312000_ref19","doi-asserted-by":"crossref","first-page":"1282","DOI":"10.1016\/j.ygeno.2019.07.017","article-title":"iDHS-DSAMS: identifying DNase I hypersensitive sites based on the dinucleotide property matrix and ensemble bagged tree","volume":"112","author":"Zhang","year":"2020","journal-title":"Genomics"},{"key":"2021090815131312000_ref20","doi-asserted-by":"crossref","first-page":"1431","DOI":"10.1007\/s00438-020-01711-8","article-title":"Use Chou's 5-steps rule to identify DNase I hypersensitive sites via dinucleotide property matrix and extreme gradient boosting","volume":"295","author":"Zhang","year":"2020","journal-title":"Mol Genet Genomics"},{"key":"2021090815131312000_ref21","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.jtbi.2017.05.030","article-title":"pDHS-SVM: a prediction method for plant DNase I hypersensitive sites based on support vector machine","volume":"426","author":"Zhang","year":"2017","journal-title":"J Theor Biol"},{"key":"2021090815131312000_ref22","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.ab.2018.03.025","article-title":"Prediction of DNase I hypersensitive sites in plant genome using multiple modes of pseudo components","volume":"549","author":"Zhang","year":"2018","journal-title":"Anal Biochem"},{"key":"2021090815131312000_ref23","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1007\/s00438-018-1436-3","article-title":"pDHS-ELM: computational predictor for plant DNase I hypersensitive sites based on extreme learning machines","volume":"293","author":"Zhang","year":"2018","journal-title":"Mol Genet Genomics"},{"key":"2021090815131312000_ref24","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.ab.2018.10.018","article-title":"pDHS-DSET: prediction of DNase I hypersensitive sites in plant genome using DS evidence theory","volume":"564-565","author":"Zhang","year":"2019","journal-title":"Anal Biochem"},{"key":"2021090815131312000_ref25","doi-asserted-by":"publisher","DOI":"10.1101\/2020.06.26.172718","article-title":"Atlas and developmental dynamics of mouse DNase I hypersensitive sites","author":"Breeze","year":"2020","journal-title":"bioRxiv"},{"key":"2021090815131312000_ref26","doi-asserted-by":"crossref","first-page":"3150","DOI":"10.1093\/bioinformatics\/bts565","article-title":"CD-HIT: accelerated for clustering the next-generation sequencing data","volume":"28","author":"Fu","year":"2012","journal-title":"Bioinformatics"},{"key":"2021090815131312000_ref27","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaa017","article-title":"A computational platform to identify origins of replication sites in ukaryotes","author":"Dao","year":"2020","journal-title":"Brief Bioinform"},{"key":"2021090815131312000_ref28","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.1093\/bioinformatics\/btz721","article-title":"DeepCleave: a deep learning predictor for caspase and matrix metalloprotease substrates and cleavage sites","volume":"36","author":"Li","year":"2020","journal-title":"Bioinformatics"},{"key":"2021090815131312000_ref29","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-020-60598-y","article-title":"Deep learning to predict protein backbone structure from high-resolution Cryo-EM density maps","volume":"10","author":"Si","year":"2020","journal-title":"Sci Rep"},{"key":"2021090815131312000_ref30","doi-asserted-by":"crossref","first-page":"185","DOI":"10.2174\/1389200219666180820112457","article-title":"Survey of machine learning techniques in drug discovery","volume":"20","author":"Stephenson","year":"2019","journal-title":"Curr Drug Metab"},{"key":"2021090815131312000_ref31","doi-asserted-by":"crossref","DOI":"10.1186\/s12859-016-1405-y","article-title":"DeepQA: improving the estimation of single protein model quality with deep belief networks","volume":"17","author":"Cao","year":"2016","journal-title":"BMC Bioinformatics"},{"key":"2021090815131312000_ref32","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaa356","article-title":"DeepYY1: a deep learning approach to identify YY1-mediated chromatin loops","author":"Dao","year":"2020","journal-title":"Brief Bioinform"},{"key":"2021090815131312000_ref33","doi-asserted-by":"crossref","first-page":"68","DOI":"10.2174\/1574893614666190227160538","article-title":"Predicting drug-target interactions via FM-DNN learning","volume":"15","author":"Wang","year":"2020","journal-title":"Current Bioinformatics"},{"key":"2021090815131312000_ref34","doi-asserted-by":"crossref","first-page":"176","DOI":"10.2174\/157489361403190220112855","article-title":"Latest machine learning techniques for biomedicine and bioinformatics","volume":"14","author":"Zou","year":"2019","journal-title":"Current Bioinformatics"},{"key":"2021090815131312000_ref35","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.matcom.2020.04.031","article-title":"Application of the residue number system to reduce hardware costs of the convolutional neural network implementation","volume":"177","author":"Valueva","year":"2020","journal-title":"Mathematics and Computers in Simulation"},{"key":"2021090815131312000_ref36","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput"},{"key":"2021090815131312000_ref37","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: an overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw"},{"key":"2021090815131312000_ref38","first-page":"1047","article-title":"iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA","volume":"21","author":"Chen","year":"2020","journal-title":"RNA and protein sequence data, Brief Bioinform"},{"key":"2021090815131312000_ref39","doi-asserted-by":"crossref","DOI":"10.1093\/nar\/gkz740","article-title":"BioSeq-Analysis2.0: an updated platform for analyzing DNA, RNA and protein sequences at sequence level and residue level based on machine learning approaches","volume":"47","author":"Liu","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2021090815131312000_ref40","doi-asserted-by":"crossref","first-page":"100991","DOI":"10.1016\/j.isci.2020.100991","article-title":"iDNA-MS: an integrated computational tool for detecting DNA modification sites in multiple genomes","volume":"23","author":"Lv","year":"2020","journal-title":"iScience"},{"key":"2021090815131312000_ref41","doi-asserted-by":"crossref","first-page":"1826","DOI":"10.1093\/bib\/bby053","article-title":"Function determinants of TET proteins: the arrangements of sequence motifs with specific codes","volume":"20","author":"Liu","year":"2019","journal-title":"Brief Bioinform"},{"key":"2021090815131312000_ref42","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1109\/TPAMI.2016.2599174","article-title":"Long-term recurrent convolutional networks for visual recognition and description","volume":"39","author":"Donahue","year":"2017","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2021090815131312000_ref43","article-title":"Fully connected deep structured networks","author":"Schwing","year":"2015","journal-title":"arXiv preprint"},{"key":"2021090815131312000_ref44","article-title":"Keras: Deep learning library for theano and tensorflow","author":"Chollet","year":"2015"},{"key":"2021090815131312000_ref45","author":"Girija"},{"key":"2021090815131312000_ref46","article-title":"Deep learning using rectified linear units (relu)","author":"Agarap","year":"2018","journal-title":"arXiv preprint"},{"key":"2021090815131312000_ref47","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1111\/j.2517-6161.1974.tb00994.x","article-title":"Cross-validatory choice and assessment of statistical predictions","volume":"36","author":"Stone","year":"1974","journal-title":"J R Stat Soc B Methodol"},{"key":"2021090815131312000_ref48","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.1109\/TCBB.2018.2816032","article-title":"Computational prediction of Sigma-54 promoters in bacterial genomes by integrating motif finding and machine learning strategies","volume":"16","author":"Liu","year":"2019","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2021090815131312000_ref49","doi-asserted-by":"crossref","first-page":"113747","DOI":"10.1016\/j.ab.2020.113747","article-title":"iTTCA-Hybrid: improved and robust identification of tumor T cell antigens by utilizing hybrid feature representation","volume":"599","author":"Charoenkwan","year":"2020","journal-title":"Anal Biochem"},{"key":"2021090815131312000_ref50","doi-asserted-by":"crossref","first-page":"3336","DOI":"10.1093\/bioinformatics\/btaa155","article-title":"iMRM: a platform for simultaneously identifying multiple kinds of RNA modifications","volume":"36","author":"Liu","year":"2020","journal-title":"Bioinformatics"},{"key":"2021090815131312000_ref51","doi-asserted-by":"crossref","first-page":"2715","DOI":"10.1021\/acs.jproteome.8b00148","article-title":"Machine-learning-based prediction of cell-penetrating peptides and their uptake efficiency with improved accuracy","volume":"17","author":"Manavalan","year":"2018","journal-title":"J Proteome Res"},{"key":"2021090815131312000_ref52","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1007\/978-1-4939-9442-7_11","article-title":"Curves for the statistical analysis of microarray data","volume":"1986","author":"Cao","year":"2019","journal-title":"Methods Mol Biol"},{"key":"2021090815131312000_ref53","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.cmpb.2018.08.006","article-title":"Transfer learning for classification of cardiovascular tissues in histological images","volume":"165","author":"Mazo","year":"2018","journal-title":"Comput Methods Programs Biomed"},{"key":"2021090815131312000_ref54","doi-asserted-by":"crossref","first-page":"D876","DOI":"10.1093\/nar\/gkq963","article-title":"The UCSC genome browser database: update 2011","volume":"39","author":"Fujita","year":"2011","journal-title":"Nucleic Acids Res"},{"key":"2021090815131312000_ref55","doi-asserted-by":"crossref","first-page":"992","DOI":"10.1073\/pnas.0307540100","article-title":"Identifying gene regulatory elements by genome-wide recovery of DNase hypersensitive sites","volume":"101","author":"Crawford","year":"2004","journal-title":"Proc Natl Acad Sci U S A"},{"key":"2021090815131312000_ref56","doi-asserted-by":"crossref","first-page":"1307","DOI":"10.1016\/j.cell.2018.05.012","article-title":"Mapping the mouse cell atlas by microwell-Seq","volume":"173","author":"Han","year":"2018","journal-title":"Cell"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bib\/article-pdf\/22\/5\/bbab047\/40261547\/bbab047.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"http:\/\/academic.oup.com\/bib\/article-pdf\/22\/5\/bbab047\/40261547\/bbab047.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T06:30:32Z","timestamp":1724394632000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbab047\/6158360"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,4]]},"references-count":56,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2021,9,2]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbab047","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2021,9]]},"published":{"date-parts":[[2021,3,4]]},"article-number":"bbab047"}}