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Nat Commun. 2020 Feb 17;11(1):926. doi: 10.1038/s41467-020-14665-7.

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

Author information

1
Epigenetics Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
2
Center for Bioinformatics Tübingen, University of Tübingen, Tübingen, Germany.
3
STORM Therapeutics Limited, Moneta Building, Babraham Research Campus, Cambridge, UK.
4
Applied Bioinformatics, Department for Computer Science, University of Tübingen, Tübingen, Germany.
5
Gurdon Institute, University of Cambridge, Cambridge, UK.
6
Quantitative Biology Center, University of Tübingen, Tübingen, Germany.
7
Biomolecular Interactions, Max Planck Institute for Developmental Biology, Tübingen, Germany.
8
Translational Bioinformatics, University Hospital Tübingen, Tübingen, Germany.
9
Epigenetics Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. bgarci@pennmedicine.upenn.edu.
10
STORM Therapeutics Limited, Moneta Building, Babraham Research Campus, Cambridge, UK. hendrik.weisser@stormtherapeutics.com.

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.

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