DNA 突变和测序/作图错误阻碍了从转录组数据集中精确识别混杂的腺苷到肌苷 RNA 编辑位点。在这里，我们提出了一个名为 DEMINING 的逐步计算框架，可以直接从 RNA 测序数据集中区分 RNA 编辑和 DNA 突变，并具有名为 DeepDDR 的嵌入式深度学习模型。经过迁移学习，DEMINING 还可以对非灵长类测序样本中的 RNA 编辑位点和 DNA 突变进行分类。当应用于急性髓系白血病患者的样本时，DEMINING 发现了以前未被充分认识的 DNA 突变和 RNA 编辑位点；有些与宿主基因的表达上调或新抗原的产生有关。© 2024。作者。

Precise calling of promiscuous adenosine-to-inosine RNA editing sites from transcriptomic datasets is hindered by DNA mutations and sequencing/mapping errors. Here, we present a stepwise computational framework, called DEMINING, to distinguish RNA editing and DNA mutations directly from RNA sequencing datasets, with an embedded deep learning model named DeepDDR. After transfer learning, DEMINING can also classify RNA editing sites and DNA mutations from non-primate sequencing samples. When applied in samples from acute myeloid leukemia patients, DEMINING uncovers previously underappreciated DNA mutation and RNA editing sites; some associated with the upregulated expression of host genes or the production of neoantigens.© 2024. The Author(s).