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Since 2015, deep learning (DL) methods have been widely applied to identify TF binding sites and predict motif patterns, with the strengths of offering a scalable, flexible and unified computational approach for highly accurate predictions. As far as we know, 20 DL methods have been developed. However, without a clear and systematic assessment, users will struggle to choose the most appropriate tool for their specific studies. In this manuscript, we evaluated 20 DL methods for cis-regulatory motif prediction using 690 ENCODE ChIP-seq, 126 cancer ChIP-seq and 55 RNA CLIP-seq data. Four metrics were investigated, including the accuracy of motif finding, the performance of DNA\/RNA sequence classification, algorithm scalability and tool usability. The assessment results demonstrated the high complementarity of the existing DL methods. It was determined that the most suitable model should primarily depend on the data size and type and the method\u2019s outputs.<\/jats:p>","DOI":"10.1093\/bib\/bbab374","type":"journal-article","created":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T07:20:51Z","timestamp":1631085651000},"source":"Crossref","is-referenced-by-count":20,"title":["Assessing deep learning methods in\n                    <i>cis<\/i>\n                    -regulatory motif finding based on genomic sequencing data"],"prefix":"10.1093","volume":"23","author":[{"given":"Shuangquan","family":"Zhang","sequence":"first","affiliation":[{"name":"Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anjun","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dong","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, and Christopher S. 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