Nm-Nano: a machine learning framework for transcriptome-wide single-molecule mapping of 2´-O-methylation (Nm) sites in nanopore direct RNA sequencing datasets

Doaa Hassana Department of Biohealth Informatics, Luddy School of Informatics, Computing, and Engineering, Indiana University Indianapolis (IUI), Indianapolis, Indiana, USA;b Computers and Systems Department, National Telecommunication Institute, Cairo, EgyptView further author information

Aditya Ariyura Department of Biohealth Informatics, Luddy School of Informatics, Computing, and Engineering, Indiana University Indianapolis (IUI), Indianapolis, Indiana, USAView further author information

Swapna Vidhur Daulatabada Department of Biohealth Informatics, Luddy School of Informatics, Computing, and Engineering, Indiana University Indianapolis (IUI), Indianapolis, Indiana, USAView further author information

Quoseena Mira Department of Biohealth Informatics, Luddy School of Informatics, Computing, and Engineering, Indiana University Indianapolis (IUI), Indianapolis, Indiana, USAView further author information

Sarath Chandra Jangaa Department of Biohealth Informatics, Luddy School of Informatics, Computing, and Engineering, Indiana University Indianapolis (IUI), Indianapolis, Indiana, USA;c Centre for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IndianaCorrespondence[email protected]
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Nm-Nano: a machine learning framework for transcriptome-wide single-molecule mapping of 2´-O-methylation (Nm) sites in nanopore direct RNA sequencing datasets

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Nm-Nano: a machine learning framework for transcriptome-wide single-molecule mapping of 2´-O-methylation (Nm) sites in nanopore direct RNA sequencing datasets

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