Kinnear Evan, Derbel Houssemeddine, Zhao Zhongming, Liu Qian
Nevada Institute of Personalized Medicine, University of Nevada, Las Vegas, 4505 S Maryland Pkwy, Las Vegas, NV 89154, USA.
Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
Comput Struct Biotechnol J. 2025 Feb 14;27:631-638. doi: 10.1016/j.csbj.2025.02.007. eCollection 2025.
DNA methylations, such as 5-methylcytosine (5mC), are crucial in biological processes, and aberrant methylations are strongly linked to various human diseases. Genomic 5mC is not randomly distributed but exhibits a strong association with genomic sequences. Thus, various computational methods were developed to predict 5mC status based on DNA sequences. These methods generated promising achievements and overcome the limitations of experimental approaches. However, few studies have comprehensively investigated the dependency of 5mC on genomic sequences, and most existing methods focus on specific genomic regions. In this work, we introduce Deep5mC, a deep learning transformer-based method designed to predict 5mC methylations. Deep5mC leverages long-range dependencies within genomic sequences to estimate the probability of cytosine methylations. Through cross-chromosome evaluation, Deep5mC achieves Matthew's correlation coefficient over 0.86 and F1-score over 0.93, substantially outperforming state-of-the-art methods. Deep5mC not only confirms the influence of long-range sequence context on 5mC prediction but also paves the way for further studying 5mC-sequence dependency across species and in human diseases.
DNA甲基化，如5-甲基胞嘧啶（5mC），在生物过程中至关重要，而异常甲基化与多种人类疾病密切相关。基因组中的5mC并非随机分布，而是与基因组序列表现出强烈关联。因此，人们开发了各种计算方法来基于DNA序列预测5mC状态。这些方法取得了有前景的成果，克服了实验方法的局限性。然而，很少有研究全面调查5mC对基因组序列的依赖性，并且大多数现有方法聚焦于特定基因组区域。在这项工作中，我们介绍了Deep5mC，一种基于深度学习Transformer的方法，旨在预测5mC甲基化。Deep5mC利用基因组序列中的长程依赖性来估计胞嘧啶甲基化的概率。通过跨染色体评估，Deep5mC的马修斯相关系数超过0.86，F1分数超过0.93，显著优于现有最先进的方法。Deep5mC不仅证实了长程序列背景对5mC预测的影响，还为进一步研究跨物种和人类疾病中的5mC-序列依赖性铺平了道路。
