Dieseldorff Jones Karissa, Putnam Daniel, Williams Justin, Chen Xiang
Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA.
Department of Tumor Cell Biology, St. Jude Children's Research Hospital, Memphis, TN, USA.
Methods Mol Biol. 2023;2624:73-85. doi: 10.1007/978-1-0716-2962-8_6.
Genome-wide DNA methylomes have contributed greatly to tumor detection and subclassification. However, interpreting the biological impact of the DNA methylome at the individual gene level remains a challenge. MethylationToActivity (M2A) is a pipeline that uses convolutional neural networks to infer H3K4me3 and H3K27ac enrichment from DNA methylomes and thus infer promoter activity. It was shown to be highly accurate and robust in revealing promoter activity landscapes in various pediatric and adult cancers. The following will present a user-friendly guide through the model pipeline.
全基因组DNA甲基化组对肿瘤检测和亚分类有很大贡献。然而，在单个基因水平上解释DNA甲基化组的生物学影响仍然是一项挑战。甲基化到活性（M2A）是一种利用卷积神经网络从DNA甲基化组推断H3K4me3和H3K27ac富集从而推断启动子活性的流程。在揭示各种儿科和成人癌症的启动子活性图谱方面，它被证明具有高度准确性和稳健性。以下将提供一个通过该模型流程的用户友好指南。
