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Previous studies revealed the significance of Nm in multiple biological processes. With Nm getting more and more attention, a revolutionary technique termed Nm-seq, was developed to profile Nm sites mainly in mRNA with single nucleotide resolution and high sensitivity. In a recent work, supported by the Nm-seq data, we have reported a method in silico for predicting Nm sites, which relies on nucleotide sequence information, and established an online server named NmSEER. More recently, a more confident dataset produced by refined Nm-seq was available. Therefore, in this work, we redesigned the prediction model to achieve a more robust performance on the new data.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Results<\/jats:title>\n<jats:p>We redesigned the prediction model from two perspectives, including machine learning algorithm and multi-encoding scheme combination. With optimization by 5-fold cross-validation tests and evaluation by independent test respectively, random forest was selected as the most robust algorithm. Meanwhile, one-hot encoding, together with position-specific dinucleotide sequence profile and K-nucleotide frequency encoding were collectively applied to build the final predictor.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Conclusions<\/jats:title>\n<jats:p>The predictor of updated version, named NmSEER V2.0, achieves an accurate prediction performance (AUROC\u2009=\u20090.862) and has been settled into a brand-new server, which is available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"http:\/\/www.rnanut.net\/nmseer-v2\/\">http:\/\/www.rnanut.net\/nmseer-v2\/<\/jats:ext-link> for free.<\/jats:p>\n<\/jats:sec>","DOI":"10.1186\/s12859-019-3265-8","type":"journal-article","created":{"date-parts":[[2019,12,24]],"date-time":"2019-12-24T09:02:35Z","timestamp":1577178155000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["NmSEER V2.0: a prediction tool for 2\u2032-O-methylation sites based on random forest and multi-encoding combination"],"prefix":"10.1186","volume":"20","author":[{"given":"Yiran","family":"Zhou","sequence":"first","affiliation":[]},{"given":"Qinghua","family":"Cui","sequence":"additional","affiliation":[]},{"given":"Yuan","family":"Zhou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,12,24]]},"reference":[{"key":"3265_CR1","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1146\/annurev-genom-090413-025405","volume":"15","author":"S Li","year":"2014","unstructured":"Li S, Mason CE. 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