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m6Anet identifies N6-methyladenosine from individual direct RNA sequencing reads

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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Fig. 1: From individual reads to site-level m6A predictions with m6Anet.


  1. Wan, Y. K., Hendra, C., Pratanwanich, P. N. & Göke, J. Beyond sequencing: machine learning algorithms extract biology hidden in Nanopore signal data. Trends Genet. (2021). A review on machine learning methods that use direct RNA-seq data.

  2. 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.

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  4. Parker, M. T. et al. Nanopore direct RNA sequencing maps the complexity of Arabidopsis mRNA processing and m6A modification. Elife (2020). This article describes the Arabidopsis data we used to demonstrate the generalizability of m6Anet to other species.

  5. 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.

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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 (2022).

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m6Anet identifies N6-methyladenosine from individual direct RNA sequencing reads. Nat Methods (2022).

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