中央研究院 資訊科學研究所

活動訊息

友善列印

列印可使用瀏覽器提供的(Ctrl+P)功能

學術演講

:::

Revealing the epitranscriptomic landscape of m6A using deep neural networks

  • 講者余柏毅 先生 (日本東京大學先端科學技術研究中心)
    邀請人:林仲彥
  • 時間2023-01-06 (Fri.) 10:30 ~ 12:00
  • 地點資訊所新館106會議室
摘要
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 have a better understanding of the role of m6A, transcriptome-wide m6A profiling data is indispensable. In these years, the third-generation Oxford Nanopore Technology (ONT) direct RNA sequencing (dRNA-seq) platform has shown promise in RNA modification detection based on current disruptions measured in transcripts. However, decoding the current intensity data to m6A modification profiles remains a challenge.

Here, we developed a novel deep-learning based approach for m6A detection at single-nucleotide resolution by using dRNA-seq. We demonstrated that our model outperformed other Nanopore-based m6A detection tools by using different validation datasets. In addition, we showed that our tool can be applied to transcriptome-wide m6A detection in several human cell lines. The long-term goal is to depict the m6A profiles of patients and pave the way for precision medicine in epitranscriptomics.