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M6ATM: a deep learning framework for demystifying m6A epitranscriptome via Nanopore long read RNA-seq data

ABSTRACT: N6-methyladenosine (m6A) has been one of the most abundant and well-known modifications in mRNA since its discovery in 1970s. Recent studies have demonstrated that m6A gets involved in various biological processes such as alternative splicing and RNA degradation, playing an important role in all kinds of diseases. To better understand the role of m6A, transcriptome-wide m6A profiling data is indispensable. In these years, the Oxford Nanopore Technology Direct RNA Sequencing (DRS) platform has shown promise in RNA modification detection based on current disruptions measured in transcripts. However, decoding current intensity data into modification profiles remains a challenging task. Here, we introduce m6A Transcriptome-wide Mapper (m6ATM), a novel Python-based computational pipeline that applies deep neural networks to predict m6A sites at single-base resolution using DRS data. The m6ATM model architecture incorporates a WaveNet encoder and a dual-stream multiple instance learning model to extract features from specific target sites and characterize the m6A epitranscriptome. For validation, m6ATM achieved an accuracy of 80 to 98% across in-vitro transcription datasets containing varying m6A modification ratios and outperformed other tools in benchmarking with human cell-line data. Moreover, we demonstrated the versatility of m6ATM in providing reliable stoichiometric information and used it to pinpoint PEG10 as a potential m6A target transcript in liver cancer cells. In conclusion, we showed that m6ATM is a high-performance m6A detection tool and our results paved the way for epitranscriptomic precision medicine.

ORGANISM(S): synthetic construct

PROVIDER: GSE265754 | GEO | 2024/10/28

REPOSITORIES: GEO

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M6ATM: a deep learning framework for demystifying m6A epitranscriptome via Nanopore long read RNA-seq data

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