Unknown

Dataset Information

0

Accurate detection of m6A RNA modifications in native RNA sequences.


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.

SUBMITTER: Liu H 

PROVIDER: S-EPMC6734003 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

altmetric image

Publications

Accurate detection of m<sup>6</sup>A RNA modifications in native RNA sequences.

Liu Huanle H   Begik Oguzhan O   Lucas Morghan C MC   Ramirez Jose Miguel JM   Mason Christopher E CE   Wiener David D   Schwartz Schraga S   Mattick John S JS   Smith Martin A MA   Novoa Eva Maria EM  

Nature communications 20190909 1


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, N<sup>6</sup>-methyladenosine (m<sup>6</sup>A) 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 m<sup>6</sup>A-modified and unmod  ...[more]

Similar Datasets

2019-09-09 | GSE126213 | GEO
2019-07-10 | GSE124309 | GEO
| PRJNA521324 | ENA
| PRJNA511582 | ENA
| S-EPMC9069010 | biostudies-literature
| S-EPMC5769742 | biostudies-literature
| S-EPMC8450095 | biostudies-literature
| S-EPMC3664801 | biostudies-other
| S-EPMC7826254 | biostudies-literature
| S-EPMC8664944 | biostudies-literature