A computational platform for high-throughput analysis of RNA sequences and modifications by mass spectrometry

• Published in: Nature communications Vol. 11; no. 1; p. 926
• Main Authors: Wein, Samuel, Andrews, Byron, Sachsenberg, Timo, Santos-Rosa, Helena, Kohlbacher, Oliver, Kouzarides, Tony, Garcia, Benjamin A., Weisser, Hendrik
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 17-02-2020; Nature Publishing Group; Nature Portfolio
• Subjects: 38/90; 631/114/794; 631/1647/296; 631/45/500; 82/58; Base Sequence - genetics; Computer applications; Computer programs; Data bases; Databases, Factual - statistics & numerical data; Datasets as Topic; Epigenomics - methods; Freeware; Gene sequencing; High-Throughput Screening Assays - methods; Humanities and Social Sciences; Humans; Mass spectrometry; Mass spectroscopy; multidisciplinary; Nucleotide sequence; Oligonucleotides - chemistry; Oligonucleotides - genetics; Oligonucleotides - metabolism; Post-transcription; Proteomics; Reproducibility of Results; Residues; Ribonucleic acid; RNA; RNA modification; RNA processing; RNA Processing, Post-Transcriptional - genetics; RNA, Transfer - chemistry; RNA, Transfer - genetics; RNA, Transfer - metabolism; Science; Science (multidisciplinary); Scientific imaging; Search Engine; Search engines; Shotguns; Software; Source code; Spectroscopy; Tandem Mass Spectrometry - methods; tRNA
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-020-14665-7

The field of epitranscriptomics continues to reveal how post-transcriptional modification of RNA affects a wide variety of biological phenomena. A pivotal challenge in this area is the identification of modified RNA residues within their sequence contexts. Mass spectrometry (MS) offers a comprehensive solution by using analogous approaches to shotgun proteomics. However, software support for the analysis of RNA MS data is inadequate at present and does not allow high-throughput processing. Existing software solutions lack the raw performance and statistical grounding to efficiently handle the numerous modifications found on RNA. We present a free and open-source database search engine for RNA MS data, called NucleicAcidSearchEngine (NASE), that addresses these shortcomings. We demonstrate the capability of NASE to reliably identify a wide range of modified RNA sequences in four original datasets of varying complexity. In human tRNA, we characterize over 20 different modification types simultaneously and find many cases of incomplete modification.
Mass spectrometry (MS) enables identification of modified RNA residues, but high-throughput processing is currently a bottleneck. Here, the authors present a free and open-source database search engine for RNA MS data to facilitate reliable identification of modified RNA sequences.