新加坡国立大学Jonathan Göke和Alexandre Thiery共同合作近期取得重要工作进展，他们研究使用多实例学习框架从直接RNA测序中检测m6A修饰。这一研究成果2022年11月10日在线发表于《自然—方法学》杂志上。

研究人员开发了一种基于神经网络的方法m6Anet，利用多实例学习框架专门处理站点级训练数据中缺失的读取级修饰标签。m6Anet优于现有的计算方法，显示出与实验方法相似的精度，并且在不重新训练模型参数的情况下以高精度推广到不同的细胞系和物种。

此外，研究人员证明m6Anet可以捕获到基本的读级化学计量。m6Anet提供了一种工具，可以从一次直接的RNA测序中捕获m6A转录组范围的识别和量化。

据介绍，RNA修饰如m6A甲基化在转录组中形成了额外的复杂层面。纳米孔直接RNA测序法可以在每个RNA分子的原始信号中捕获这些信息，从而能够使监督机器学习检测RNA修饰成为可能。然而，实验方法只提供了位点水平的训练数据，而每个RNA分子的修饰状态都是缺失的。

附：英文原文

Title: Detection of m6A from direct RNA sequencing using a multiple instance learning framework

Author: Hendra, Christopher, Pratanwanich, Ploy N., Wan, Yuk Kei, Goh, W. S. Sho, Thiery, Alexandre, Gke, Jonathan

Issue&Volume: 2022-11-10

Abstract: RNA modifications such as m6A methylation form an additional layer of complexity in the transcriptome. Nanopore direct RNA sequencing can capture this information in the raw current signal for each RNA molecule, enabling the detection of RNA modifications using supervised machine learning. However, experimental approaches provide only site-level training data, whereas the modification status for each single RNA molecule is missing. Here we present m6Anet, a neural-network-based method that leverages the multiple instance learning framework to specifically handle missing read-level modification labels in site-level training data. m6Anet outperforms existing computational methods, shows similar accuracy as experimental approaches, and generalizes with high accuracy to different cell lines and species without retraining model parameters. In addition, we demonstrate that m6Anet captures the underlying read-level stoichiometry, which can be used to approximate differences in modification rates. Overall, m6Anet offers a tool to capture the transcriptome-wide identification and quantification of m6A from a single run of direct RNA sequencing.

DOI: 10.1038/s41592-022-01666-1

Source: https://www.nature.com/articles/s41592-022-01666-1