Abstract
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.
Highlights
The field of epitranscriptomics continues to reveal how post-transcriptional modification of RNA affects a wide variety of biological phenomena
Using nanoflow ion-pair liquid chromatography coupled to high-resolution tandem mass spectrometry, we generated four datasets from RNA samples of increasing complexity
The “NME1”, “human rRNA” and “human tRNA” samples were all digested with an RNA endonuclease (RNase T1) to generate oligonucleotide sequences of a length amenable to mass spectrometry
Summary
The field of epitranscriptomics continues to reveal how post-transcriptional modification of RNA affects a wide variety of biological phenomena. All of the approaches based on Solexa/Illumina sequencing use antibodies to immunoprecipitate modified RNA, and/or apply chemical or enzymatic treatments to alter it and read out modifications as mutations or truncations in the preparation of cDNA8,9. Steps have been made towards uncovering modifications directly using long-read sequencing platforms[11,12], but many technical challenges stand between these approaches and routine use, not least a significant error rate in base calling[13]. Mass spectrometry (MS) is currently the only technique that can directly and comprehensively characterize chemical modifications in RNA sequences. The majority of RNA MS has focused on reducing the RNA to mono-nucleosides and applying workflows analogous to metabolite analysis[16] While these techniques are effective in determining what modifications are present in a sample, all information about the location and co-occurrence of modifications is lost. Modification location and co-occurrence may be important for a phenotypic effect; for example, in microRNA 2′O-methylation of the 3′-most nucleic acid sterically inhibits 3′
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