Yuxin Zhang

TL;DR: A support vector machine-based computational framework was established to predict the epitranscriptome-wide targets of six m6A reader proteins based on 58 genomic features as well as the conventional sequence-derived features, marking a substantial improvement in accuracy compared to the sequence encoding schemes tested.

TL;DR: DirectRMDB as discussed by the authors is a database of quantitative RNA modification profiles, which includes 16 types of modification and a total of 904,712 modification sites in 25 species identified from 39 independent studies.

TL;DR: A comprehensive online platform for unveiling the context-specific m6A methylation and genetic mutations that potentially regulate m 6A epigenetic mark is presented, and should make a useful resource for studying the m6a methylome and genetic factor of epitranscriptome disturbance in a specific tissue (or cancer type).

TL;DR: Wang et al. as discussed by the authors developed the WHISTLE server, the first machine learning platform based on genomic coordinates, which features convenient covariate extraction and model web deployment with 46 distinct genomic features integrated along with the conventional sequence features.

TL;DR: In this paper , the authors showed that the accuracy of ONT-based m6A site prediction can be further increased by integrating additional information from the primary sequences of RNA (AUROC of 0.918) compared with using ONT signals only (aUROC 0.878 using Base call error features, and 0.804 using Tombo features).