Machine learning‐based classification of
sams RNA reads in Nanopore direct RNA sequencing data. The statistics in (D) as well as duration time of the Nanopore current at nucleotide positions −50 through +50 of up to 80% of mapped reads from the unmodified and methylated
in␣vitro transcribed
sams‐3/
sams‐4 and
sams‐5 RNAs were pooled and used for training with a machine learning algorithm Gradient Boosting and accuracy of the classifier was tested with 20% of the mapped reads. The classifier was also applied to reads identified as unproductive
sams‐3,
sams‐4, or
sams‐5 mRNA isoforms in the analysis of endogenous mRNAs shown in Fig
1. Numbers of reads used in the tests are indicated at the top; percentages of reads classified as “m
6A‐modified” are shown with color code.