Format

Send to

Choose Destination
Nat Commun. 2019 Sep 9;10(1):4079. doi: 10.1038/s41467-019-11713-9.

Accurate detection of m6A RNA modifications in native RNA sequences.

Liu H1,2, Begik O1,2,3, Lucas MC1,4, Ramirez JM1, Mason CE5,6,7, Wiener D8, Schwartz S8, Mattick JS2,3,9, Smith MA3,10, Novoa EM11,12,13,14.

Author information

1
Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, 08003, Barcelona, Spain.
2
Department of Neuroscience, Garvan Institute of Medical Research, Darlinghurst, New South Wales, 2010, Australia.
3
St-Vincent's Clinical School, UNSW Sydney, Darlinghurst, New South Wales, 2010, Australia.
4
Universitat Pompeu Fabra (UPF), Barcelona, Spain.
5
Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10021, USA.
6
The Feil Family Brain and Mind Institute, Weill Cornell Medicine, New York, NY, 10021, USA.
7
The WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY, 10021, USA.
8
Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel.
9
Green templeton College, Oxford, OX2 6HG, UK.
10
Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Darlinghurst, New South Wales, 2010, Australia.
11
Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, 08003, Barcelona, Spain. eva.novoa@crg.eu.
12
Department of Neuroscience, Garvan Institute of Medical Research, Darlinghurst, New South Wales, 2010, Australia. eva.novoa@crg.eu.
13
St-Vincent's Clinical School, UNSW Sydney, Darlinghurst, New South Wales, 2010, Australia. eva.novoa@crg.eu.
14
Universitat Pompeu Fabra (UPF), Barcelona, Spain. eva.novoa@crg.eu.

Abstract

The epitranscriptomics field has undergone an enormous expansion in the last few years; however, a major limitation is the lack of generic methods to map RNA modifications transcriptome-wide. Here, we show that using direct RNA sequencing, N6-methyladenosine (m6A) RNA modifications can be detected with high accuracy, in the form of systematic errors and decreased base-calling qualities. Specifically, we find that our algorithm, trained with m6A-modified and unmodified synthetic sequences, can predict m6A RNA modifications with ~90% accuracy. We then extend our findings to yeast data sets, finding that our method can identify m6A RNA modifications in vivo with an accuracy of 87%. Moreover, we further validate our method by showing that these 'errors' are typically not observed in yeast ime4-knockout strains, which lack m6A modifications. Our results open avenues to investigate the biological roles of RNA modifications in their native RNA context.

PMID:
31501426
PMCID:
PMC6734003
DOI:
10.1038/s41467-019-11713-9
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Nature Publishing Group Icon for PubMed Central
Loading ...
Support Center