{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T16:19:41Z","timestamp":1773159581920,"version":"3.50.1"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2024,6,18]],"date-time":"2024-06-18T00:00:00Z","timestamp":1718668800000},"content-version":"vor","delay-in-days":17,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFF1201200"],"award-info":[{"award-number":["2021YFF1201200"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62350004"],"award-info":[{"award-number":["62350004"]}],"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":["62332020"],"award-info":[{"award-number":["62332020"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,6,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Nanopore direct RNA sequencing (DRS) enables the detection of RNA N6-methyladenosine (m6A) without extra laboratory techniques. A number of supervised or comparative approaches have been developed to identify m6A from Nanopore DRS reads. However, existing methods typically utilize either statistical features of the current signals or basecalling-error features, ignoring the richer information of the raw signals of DRS reads.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Here, we propose RedNano, a deep-learning method designed to detect m6A from Nanopore DRS reads by utilizing both raw signals and basecalling errors. RedNano processes the raw-signal feature and basecalling-error feature through residual networks. We validated the effectiveness of RedNano using synthesized, Arabidopsis, and human DRS data. The results demonstrate that RedNano surpasses existing methods by achieving higher area under the ROC curve (AUC) and area under the precision-recall curve (AUPRs) in all three datasets. Furthermore, RedNano performs better in cross-species validation, demonstrating its robustness. Additionally, when detecting m6A from an independent dataset of Populus trichocarpa, RedNano achieves the highest AUC and AUPR, which are 3.8%\u20139.9% and 5.5%\u201313.8% higher than other methods, respectively.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code of RedNano is freely available at https:\/\/github.com\/Derryxu\/RedNano.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae375","type":"journal-article","created":{"date-parts":[[2024,6,18]],"date-time":"2024-06-18T18:20:10Z","timestamp":1718734810000},"source":"Crossref","is-referenced-by-count":12,"title":["RNA m6A detection using raw current signals and basecalling errors from Nanopore direct RNA sequencing reads"],"prefix":"10.1093","volume":"40","author":[{"given":"Peng","family":"Ni","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Central South University , Changsha 410083, China"},{"name":"Xiangjiang Laboratory , Changsha 410205, China"},{"name":"Hunan Provincial Key Lab on Bioinformatics, Central South University , Changsha 410083, China"}]},{"given":"Jinrui","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University , Changsha 410083, China"},{"name":"Xiangjiang Laboratory , Changsha 410205, China"},{"name":"Hunan Provincial Key Lab on Bioinformatics, Central South University , Changsha 410083, China"}]},{"given":"Zeyu","family":"Zhong","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University , Changsha 410083, China"},{"name":"Xiangjiang Laboratory , Changsha 410205, China"},{"name":"Hunan Provincial Key Lab on Bioinformatics, Central South University , Changsha 410083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4813-2403","authenticated-orcid":false,"given":"Feng","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Computing, Clemson University , Clemson, SC 29634-0974, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1516-0480","authenticated-orcid":false,"given":"Jianxin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University , Changsha 410083, China"},{"name":"Xiangjiang Laboratory , Changsha 410205, China"},{"name":"Hunan Provincial Key Lab on Bioinformatics, Central South University , Changsha 410083, China"}]}],"member":"286","published-online":{"date-parts":[[2024,6,18]]},"reference":[{"key":"2024062805053073300_btae375-B1","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1038\/nature14281","article-title":"N6-methyladenosine marks primary microRNAs for 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