Holistic Optimization of Bioinformatic Analysis Pipeline for Detection and Quantification of 2′-O-Methylations in RNA by RiboMethSeq

A major trend in the epitranscriptomics field over the last 5 years has been the high-throughput analysis of RNA modifications by a combination of specific chemical treatment(s), followed by library preparation and deep sequencing. Multiple protocols have been described for several important RNA modifications, such as 5-methylcytosine (m(5)C), pseudouridine (ψ), 1-methyladenosine (m(1)A), and 2′-O-methylation (Nm). One commonly used method is the alkaline cleavage-based RiboMethSeq protocol, where positions of reads' 5'-ends are used to distinguish nucleotides protected by ribose methylation. This method was successfully applied to detect and quantify Nm residues in various RNA species such as rRNA, tRNA, and snRNA. Such applications require adaptation of the initially published protocol(s), both at the wet bench and in the bioinformatics analysis. In this manuscript, we describe the optimization of RiboMethSeq bioinformatics at the level of initial read treatment, alignment to the reference sequence, counting the 5′- and 3′- ends, and calculation of the RiboMethSeq scores, allowing precise detection and quantification of the Nm-related signal. These improvements introduced in the original pipeline permit a more accurate detection of Nm candidates and a more precise quantification of Nm level variations. Applications of the improved RiboMethSeq treatment pipeline for different cellular RNA types are discussed.