{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T09:47:27Z","timestamp":1781948847073,"version":"3.54.5"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1008767","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2021,3,2]],"date-time":"2021-03-02T00:00:00Z","timestamp":1614643200000}}],"reference-count":38,"publisher":"Public Library of Science (PLoS)","issue":"2","license":[{"start":{"date-parts":[[2021,2,18]],"date-time":"2021-02-18T00:00:00Z","timestamp":1613606400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004300","name":"Senior Talent Foundation of Jiangsu University","doi-asserted-by":"crossref","award":["050\/804009"],"award-info":[{"award-number":["050\/804009"]}],"id":[{"id":"10.13039\/501100004300","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"The National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["11901517"],"award-info":[{"award-number":["11901517"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    N6-methyladenine (6mA) is an important DNA modification form associated with a wide range of biological processes. Identifying accurately 6mA sites on a genomic scale is crucial for under-standing of 6mA\u2019s biological functions. However, the existing experimental techniques for detecting 6mA sites are cost-ineffective, which implies the great need of developing new computational methods for this problem. In this paper, we developed, without requiring any prior knowledge of 6mA and manually crafted sequence features, a deep learning framework named Deep6mA to identify DNA 6mA sites, and its performance is superior to other DNA 6mA prediction tools. Specifically, the 5-fold cross-validation on a benchmark dataset of rice gives the sensitivity and specificity of Deep6mA as 92.96% and 95.06%, respectively, and the overall prediction accuracy is 94%. Importantly, we find that the sequences with 6mA sites share similar patterns across different species. The model trained with rice data predicts well the 6mA sites of other three species:\n                    <jats:italic>Arabidopsis thaliana<\/jats:italic>\n                    ,\n                    <jats:italic>Fragaria vesca<\/jats:italic>\n                    and\n                    <jats:italic>Rosa chinensis<\/jats:italic>\n                    with a prediction accuracy over 90%. In addition, we find that (1) 6mA tends to occur at GAGG motifs, which means the sequence near the 6mA site may be conservative; (2) 6mA is enriched in the TATA box of the promoter, which may be the main source of its regulating downstream gene expression.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1008767","type":"journal-article","created":{"date-parts":[[2021,2,18]],"date-time":"2021-02-18T15:43:35Z","timestamp":1613663015000},"page":"e1008767","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":53,"title":["Deep6mA: A deep learning framework for exploring similar patterns in DNA N6-methyladenine sites across different 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