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RNA is often altered post-transcriptionally by the covalent modification of particular nucleotides; these modifications are known to modulate the structure and activity of their host RNAs. The recent discovery that an RNA methyl-6 adenosine demethylase (FTO) is a risk gene in obesity has brought to light the significance of RNA modifications to human biology. These noncanonical nucleotides, when converted to cDNA in the course of RNA sequencing, can produce sequence patterns that are distinguishable from simple base-calling errors. To determine whether these modifications can be detected in RNA sequencing data, we developed a method that can not only locate these modifications transcriptome-wide with single nucleotide resolution, but can also differentiate between different classes of modifications. Using small RNA-seq data we were able to detect 92% of all known human tRNA modification sites that are predicted to affect RT activity. We also found that different modifications produce distinct patterns of cDNA sequence, allowing us to differentiate between two classes of adenosine and two classes of guanine modifications with 98% and 79% accuracy, respectively. To show the robustness of this method to sample preparation and sequencing methods, as well as to organismal diversity, we applied it to a publicly available yeast data set and achieved similar levels of accuracy. We also experimentally validated two novel and one known 3-methylcytosine (3mC) sites predicted by HAMR in human tRNAs. Researchers can now use our method to identify and characterize RNA modifications using only RNA-seq data, both retrospectively and when asking questions specifically about modified RNA.