Poster abstracts
Poster number 43 submitted by Cameron Divoky
mmOligo: Direct Sequencing of RNAs via LC-MS/MS
Cameron W. Divoky (Ohio State Biochemistry Program), Olivia F. Miller (Ohio State Biochemistry Program), Herman Singh (MassMatrix, Columbus, Ohio, 43210), Mike Kollich, Andrii Albatov (MassMatrix, Columbus, Ohio, 43210), Nikolai But (MassMatrix, Columbus, Ohio, 43210)
Abstract:
Next-generation sequencing (NGS) of RNA can generate large amounts of sequencing data, however, current NGS methods often mask post-transcriptional modifications (PTMs) or limit the scope of modifications that can be identified on a given RNA. While LC-MS/MS has been used to identify a broader range of RNA modifications, this is typically achieved through enzymatic digestion of the RNA, limiting the analysis to only include the nucleosides and thereby removing the sequence context in which the modifications occur. mmOligo, a bioinformatic tool, was developed to bridge this gap and provide an intuitive, user-friendly platform to enable the direct sequencing of RNAs or peptides via LC-MS/MS while preserving the sequence context in which modifications occur. Preliminary LC-MS/MS data indicate that complex RNA pools can be reliably separated by LC, and a mass analyzer optimized for negative-ion mode can generate sufficient sequencing data for mmOligo analysis. For this symposium, our mmOligo software was benchmarked against two currently available direct-RNA sequencing algorithms for LC-MS/MS dataPytheas and the Nucleic Acid Search Engine (NASE)using publicly available MS data for a synthetic let-7-5p microRNA (miRNA) and SARS-CoV-2 mRNA vaccine. In both cases, mmOligo was able to outperform the other two software for spectral matching leading to comparable or increased sequencing coverage as well as identification of the modified residues.
Keywords: RNA, Direct-sequencing, Post-Transcriptional Modifications