Machine learning of reverse transcription signatures of variegated polymerases allows mapping and discrimination of methylated purines in limited transcriptomes

Reverse transcription (RT) of RNA templates containing RNA modifications leads to synthesis of cDNA containing information on the modification in the form of misincorporation, arrest, or nucleotide skipping events. A compilation of such events from multiple cDNAs represents an RT-signature that is typical for a given modification, but, as we show here, depends also on the reverse transcriptase enzyme. A comparison of 13 different enzymes revealed a range of RT-signatures, with individual enzymes exhibiting average arrest rates between 20 and 75%, as well as average misincorporation rates between 30 and 75% in the read-through cDNA. Using RT-signatures from individual enzymes to train a random forest model as a machine learning regimen for prediction of modifications, we found strongly variegated success rates for the prediction of methylated purines, as exemplified with N(1)-methyladenosine (m(1)A). Among the 13 enzymes, a correlation was found between read length, misincorporation, and prediction success. Inversely, low average read length was correlated to high arrest rate and lower prediction success. The three most successful polymerases were then applied to the characterization of RT-signatures of other methylated purines. Guanosines featuring methyl groups on the Watson-Crick face were identified with high confidence, but discrimination between m(1)G and m(2)(2)G was only partially successful. In summary, the results suggest that, given sufficient coverage and a set of specifically optimized reaction conditions for reverse transcription, all RNA modifications that impede Watson-Crick bonds can be distinguished by their RT-signature.