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N6-methyladenosine (m 6 A) is the most abundant internal RNA modification in eukaryotic mRNAs and influences many aspects of RNA processing. miCLIP (m 6 A individual-nucleotide resolution UV crosslinking and immunoprecipitation) is an antibody-based approach to map m 6 A sites with single-nucleotide resolution. However, due to broad antibody reactivity, reliable identification of m 6 A sites from miCLIP data remains challenging. Here, we present miCLIP2 in combination with machine learning to significantly improve m 6 A detection. The optimized miCLIP2 results in high-complexity libraries from less input material. Importantly, we established a robust computational pipeline to tackle the inherent issue of false positives in antibody-based m 6 A detection. The analyses were calibrated with  Mettl3  knockout cells to learn the characteristics of m 6 A deposition, including m 6 A sites outside of DRACH motifs. To make our results universally applicable, we trained a machine learning model, m6Aboost, based on the experimental and RNA sequence features. Importantly, m6Aboost allows prediction of genuine m 6 A sites in miCLIP2 data without filtering for DRACH motifs or the need for Mettl3 depletion. Using m6Aboost, we identify thousands of high-confidence m 6 A sites in different murine and human cell lines, which provide a rich resource for future analysis. Collectively, our combined experimental and computational methodology greatly improves m 6 A identification.

Deep and accurate detection of m6A RNA modifications using miCLIP2 and m6Aboost machine learning