By modeling the probability of N6-methyladenosine (m6A) RNA modifications for individual reads from direct RNA sequencing, m6Anet achieves high classification accuracy and takes a step towards transcriptome-wide maps of m6A modifications at single-base, single-molecule resolution.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$259.00 per year
only $21.58 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
References
Wan, Y. K., Hendra, C., Pratanwanich, P. N. & Göke, J. Beyond sequencing: machine learning algorithms extract biology hidden in Nanopore signal data. Trends Genet. https://doi.org/10.1016/j.tig.2021.09.001 (2021). A review on machine learning methods that use direct RNA-seq data.
Koh, C. W. Q., Goh, Y. T. & Goh, W. S. S. Atlas of quantitative single-base-resolution N6-methyl-adenine methylomes. Nat. Commun. 10, 5636 (2019). This article describes single-base-resolution m6A profiling with m6ACE-seq, the main technology used for training m6Anet.
Chen, Y. et al. A systematic benchmark of Nanopore long read RNA sequencing for transcript level analysis in human cell lines. Preprint at bioRxiv https://doi.org/10.1101/2021.04.21.440736 (2021). This preprint describes the Singapore Nanopore Expression project, a comprehensive resource for direct RNA-seq data from human cell lines.
Parker, M. T. et al. Nanopore direct RNA sequencing maps the complexity of Arabidopsis mRNA processing and m6A modification. Elife https://doi.org/10.7554/eLife.49658 (2020). This article describes the Arabidopsis data we used to demonstrate the generalizability of m6Anet to other species.
Liu, H. et al. Accurate detection of m6A RNA modifications in native RNA sequences. Nat. Commun. 10, 4079 (2019). This article describes a set of synthetic sequences that can be used to train and evaluate supervised methods for detection of RNA modifications from direct RNA-seq data.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This is a summary of: Hendra, C. et al. Detection of m6A from direct RNA sequencing using a multiple instance learning framework. Nat. Methods https://doi.org/10.1038/s41592-022-01666-1 (2022).
Rights and permissions
About this article
Cite this article
m6Anet identifies N6-methyladenosine from individual direct RNA sequencing reads. Nat Methods 19, 1530–1531 (2022). https://doi.org/10.1038/s41592-022-01668-z
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41592-022-01668-z