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As more small RNA sequencing libraries are becoming available, it clearly emerges that microRNAs (miRNAs) are highly heterogeneous both in length and sequence. In comparison to canonical miRNAs, miRNA isoforms (termed as "isomiRs") might exhibit different biological properties, such as a different target repertoire, or enhanced/reduced stability. Nonetheless, this layer of information has remained largely unexplored due to the scarcity of small RNA NGS-datasets and the absence of proper analytical tools. Here, we present a workflow for the characterization and analysis of miRNAs and their variants in next-generation sequencing datasets. IsomiRs can originate from an alternative dicing event ("templated" forms) or from the addition of nucleotides through an enzymatic activity or target-dependent mechanisms ("non-templated" forms). Our pipeline allows distinguishing canonical miRNAs from templated and non-templated isomiRs by alignment to a custom database, which comprises all possible 3'-, 5'-, and trimmed variants. Functionally equivalent isomiRs can be grouped together according to the type of modification (e.g., uridylation, adenylation, trimming¡) to assess which miRNAs are more intensively modified in a given biological context. When applied to the analysis of primary epithelial breast cancer cells, our methodology provided a 40% increase in the number of detected miRNA species and allowed to easily identify and classify more than 1000 variants. Most modifications were compatible with templated IsomiRs, as a consequence of imprecise Drosha or Dicer cleavage. However, some non-templated variants were consistently found either in the normal or in the cancer cells, with the 3'-end adenylation and uridylation as the most frequent events, suggesting that miRNA post-transcriptional modification frequently occurs. In conclusion, our analytical tool permits the deconvolution of miRNA heterogeneity and could be used to explore the functional role of miRNA isoforms.[1]
