{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T17:13:16Z","timestamp":1780765996008,"version":"3.54.1"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2021,7,16]],"date-time":"2021-07-16T00:00:00Z","timestamp":1626393600000},"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 Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61922020"],"award-info":[{"award-number":["61922020"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61702058"],"award-info":[{"award-number":["61702058"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2017M612948"],"award-info":[{"award-number":["2017M612948"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Scientific Research Foundation for Education Department of Sichuan Province","award":["18ZA0098"],"award-info":[{"award-number":["18ZA0098"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,11,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Transcription factors (TFs) are essential proteins in regulating the spatiotemporal expression of genes. It is crucial to infer the potential transcription factor binding sites (TFBSs) with high resolution to promote biology and realize precision medicine. Recently, deep learning-based models have shown exemplary performance in the prediction of TFBSs at the base-pair level. However, the previous models fail to integrate nucleotide position information and semantic information without noisy responses. Thus, there is still room for improvement. Moreover, both the inner mechanism and prediction results of these models are challenging to interpret. To this end, the Deep Attentive Encoder-Decoder Neural Network (D-AEDNet) is developed to identify the location of TFs\u2013DNA binding sites in DNA sequences. In particular, our model adopts Skip Architecture to leverage the nucleotide position information in the encoder and removes noisy responses in the information fusion process by Attention Gate. Simultaneously, the Transcription Factor Motif Discovery based on Sliding Window (TF-MoDSW), an approach to discover TFs\u2013DNA binding motifs by utilizing the output of neural networks, is proposed to understand the biological meaning of the predicted result. On ChIP-exo datasets, experimental results show that D-AEDNet has better performance than competing methods. Besides, we authenticate that Attention Gate can improve the interpretability of our model by ways of visualization analysis. Furthermore, we confirm that ability of D-AEDNet to learn TFs\u2013DNA binding motifs outperform the state-of-the-art methods and availability of TF-MoDSW to discover biological sequence motifs in TFs\u2013DNA interaction by conducting experiment on ChIP-seq datasets.<\/jats:p>","DOI":"10.1093\/bib\/bbab273","type":"journal-article","created":{"date-parts":[[2021,6,26]],"date-time":"2021-06-26T19:10:08Z","timestamp":1624734608000},"source":"Crossref","is-referenced-by-count":36,"title":["High-resolution transcription factor binding sites prediction improved performance and interpretability by deep learning method"],"prefix":"10.1093","volume":"22","author":[{"given":"Yongqing","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science, Chengdu University of Information Technology, 610225, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zixuan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Chengdu University of Information Technology, 610225, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanqi","family":"Zeng","sequence":"additional","affiliation":[{"name":"School of Computer Science, Chengdu University of Information Technology, 610225, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiliu","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science, Chengdu University of Information Technology, 610225, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quan","family":"Zou","sequence":"additional","affiliation":[{"name":"Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, 610054, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,7,16]]},"reference":[{"issue":"9","key":"2021110815074101800_ref1","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/j.tibs.2014.07.002","article-title":"Absence of a simple code: how transcription factors read the genome","volume":"39","author":"Slattery","year":"2014","journal-title":"Trends Biochem Sci"},{"issue":"3","key":"2021110815074101800_ref2","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.cels.2016.07.001","article-title":"DNA shape features improve transcription factor binding site predictions in vivo","volume":"3","author":"Mathelier","year":"2016","journal-title":"Cell Syst."},{"issue":"D1","key":"2021110815074101800_ref3","doi-asserted-by":"crossref","first-page":"D139","DOI":"10.1093\/nar\/gkw1064","article-title":"Snp2tfbs: a database of regulatory snps affecting predicted transcription factor binding site affinity","volume":"45","author":"Kumar","year":"2017","journal-title":"Nucleic Acids Res"},{"issue":"6","key":"2021110815074101800_ref4","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"},{"issue":"2","key":"2021110815074101800_ref5","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1093\/bfgp\/elx043","article-title":"Insights from resolving protein-DNA interactions at near base-pair resolution","volume":"17","author":"Venters","year":"2018","journal-title":"Brief Funct Genomics"},{"issue":"1","key":"2021110815074101800_ref6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-017-1769-7","article-title":"Assessing the model transferability for prediction of transcription factor binding sites based on chromatin accessibility","volume":"18","author":"Liu","year":"2017","journal-title":"BMC Bioinf"},{"issue":"5","key":"2021110815074101800_ref7","doi-asserted-by":"crossref","first-page":"2757","DOI":"10.1093\/nar\/gkv151","article-title":"Base-resolution methylation patterns accurately predict transcription factor bindings in vivo","volume":"43","author":"Xu","year":"2015","journal-title":"Nucleic Acids Res"},{"issue":"17","key":"2021110815074101800_ref8","doi-asserted-by":"crossref","first-page":"2852","DOI":"10.1093\/bioinformatics\/btv294","article-title":"BinDNase: a discriminatory approach for transcription factor binding prediction using DNase I hypersensitivity data","volume":"31","author":"Khr","year":"2015","journal-title":"Bioinformatics"},{"issue":"19","key":"2021110815074101800_ref9","doi-asserted-by":"crossref","first-page":"3003","DOI":"10.1093\/bioinformatics\/btx336","article-title":"DNA sequence+ shape kernel enables alignment-free modeling of transcription factor binding","volume":"33","author":"Ma","year":"2017","journal-title":"Bioinformatics"},{"issue":"MAR.","key":"2021110815074101800_ref10","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.engappai.2019.01.003","article-title":"Identification of DNA-protein binding sites by bootstrap multiple convolutional neural networks on sequence information","volume":"79","author":"Zhang","year":"2019","journal-title":"Eng Appl Artif Intel"},{"issue":"4","key":"2021110815074101800_ref11","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1007\/s13042-019-00990-x","article-title":"DeepSite: bidirectional LSTM and CNN models for predicting DNA-protein binding","volume":"11","author":"Zhang","year":"2020","journal-title":"Int J Mach Learn Cybernet"},{"issue":"8","key":"2021110815074101800_ref12","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"},{"issue":"10","key":"2021110815074101800_ref13","doi-asserted-by":"crossref","first-page":"931","DOI":"10.1038\/nmeth.3547","article-title":"Predicting effects of noncoding variants with deep learning-based sequence model","volume":"12","author":"Zhou","year":"2015","journal-title":"Nat Methods"},{"key":"2021110815074101800_ref14","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1109\/BIBM.2016.7822515","volume-title":"IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","author":"Hassanzadeh","year":"2016"},{"issue":"11","key":"2021110815074101800_ref15","doi-asserted-by":"crossref","first-page":"e107","DOI":"10.1093\/nar\/gkw226","article-title":"Danq: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences","volume":"44","author":"Quang","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2021110815074101800_ref16","first-page":"126","volume-title":"International Conference on Intelligent Science and Big Data Engineering (ICISBDE)","author":"Bao","year":"2019"},{"issue":"22","key":"2021110815074101800_ref17","doi-asserted-by":"crossref","first-page":"3575","DOI":"10.1093\/bioinformatics\/btx480","article-title":"Sequence2vec: a novel embedding approach for modeling transcription factor binding affinity landscape","volume":"33","author":"Dai","year":"2017","journal-title":"Bioinformatics"},{"issue":"1","key":"2021110815074101800_ref18","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1109\/TCBB.2019.2901789","article-title":"An integrative framework for combining sequence and epigenomic data to predict transcription factor binding sites using deep learning","volume":"18","author":"Jing","year":"2019","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"issue":"2","key":"2021110815074101800_ref19","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1109\/TCBB.2018.2864203","article-title":"Weakly-supervised convolutional neural network architecture for predicting protein-DNA binding","volume":"17","author":"Zhang","year":"2018","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"issue":"1","key":"2021110815074101800_ref20","first-page":"1","article-title":"Enhancing the interpretability of transcription factor binding site prediction using attention mechanism","volume":"10","author":"Park","year":"2020","journal-title":"Sci Rep"},{"issue":"20","key":"2021110815074101800_ref21","doi-asserted-by":"crossref","first-page":"3446","DOI":"10.1093\/bioinformatics\/bty383","article-title":"Base-pair resolution detection of transcription factor binding site by deep deconvolutional network","volume":"34","author":"Salekin","year":"2018","journal-title":"Bioinformatics"},{"key":"2021110815074101800_ref22","first-page":"448","volume-title":"Proceedings of the International Conference on Machine Learning (ICML)","author":"Ioffe","year":"2015"},{"key":"2021110815074101800_ref23","first-page":"315","volume-title":"International Conference on Artificial Intelligence and Statistics (AISTATS)","author":"Glorot","year":"2011"},{"key":"2021110815074101800_ref24","article-title":"Yolov3: an incremental improvement","author":"Redmon","year":"2018"},{"key":"2021110815074101800_ref25","volume-title":"Neural Information Processing Systems (NIPS)"},{"key":"2021110815074101800_ref26","first-page":"3431","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Long","year":"2015"},{"key":"2021110815074101800_ref27","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1109\/BHI.2017.7897204","volume-title":"IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)","author":"Salekin","year":"2017"},{"key":"2021110815074101800_ref28","article-title":"Adam: a method for stochastic optimization","author":"Kingma","year":"2014"},{"issue":"DEC","key":"2021110815074101800_ref29","doi-asserted-by":"crossref","first-page":"219256","DOI":"10.1109\/ACCESS.2020.3042903","article-title":"A review about transcription factor binding sites prediction based on deep learning","volume":"8","author":"Zeng","year":"2020","journal-title":"IEEE Access"},{"issue":"2","key":"2021110815074101800_ref30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/gb-2007-8-2-r24","article-title":"Quantifying similarity between motifs","volume":"8","author":"Gupta","year":"2007","journal-title":"Genome Biol"},{"issue":"1","key":"2021110815074101800_ref31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12870-019-1760-8","article-title":"Genome-wide analyses and expression patterns under abiotic stress of NAC transcription factors in white pear (Pyrus bretschneideri)","volume":"19","author":"Gong","year":"2019","journal-title":"BMC Plant Biol"},{"issue":"4","key":"2021110815074101800_ref32","doi-asserted-by":"crossref","first-page":"1628","DOI":"10.1093\/nar\/gky1297","article-title":"Heterodimeric DNA motif synthesis and validations","volume":"47","author":"Wong","year":"2019","journal-title":"Nucleic Acids Res"},{"issue":"24","key":"2021110815074101800_ref33","doi-asserted-by":"crossref","first-page":"5067","DOI":"10.1093\/bioinformatics\/btz451","article-title":"Mttfsite: cross-cell type TF binding site prediction by using multi-task learning","volume":"35","author":"Zhou","year":"2019","journal-title":"Bioinformatics"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/22\/6\/bbab273\/41088886\/bbab273.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/22\/6\/bbab273\/41088886\/bbab273.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T18:57:19Z","timestamp":1725303439000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbab273\/6322761"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,16]]},"references-count":33,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2021,11,5]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbab273","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2021,11]]},"published":{"date-parts":[[2021,7,16]]},"article-number":"bbab273"}}