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.
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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.
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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).
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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
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DOI: https://doi.org/10.1038/s41592-022-01668-z
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