Zhang Yin, Tang Lin, Zhi Shengyao, Hu Bosu, Zuo Zhixiang, Ren Jian, Xie Yubin, Luo Xiaotong
Innovation Center of the Sixth Affiliated hospital, School of Life Sciences, Sun Yat-sen University, Guangzhou 510060, China.
Guangdong Provincial Key Laboratory of Pharmaceutical Bioactive Substances, School of Biosciences and Biopharmaceutics, Guangdong Pharmaceutical University, Guangzhou 510006, China.
Gigascience. 2025 Jan 6;14. doi: 10.1093/gigascience/giaf040.
Allelic gene-specific regulatory events are crucial mechanisms in organisms, pivotal to many fundamental biological processes such as embryonic development and chromosome inactivation. Allelic gene imbalance manifests at both RNA expression and epigenetic levels. Recent research has unveiled allelic-specific regulation of RNA N6-methyladenosine (m6A), emphasizing the need for its precise identification. However, prevailing approaches primarily focus on screening allele-specific genetic variations associated with m6A, but not truly identify allelic m6A events. Therefore, the construction of a novel algorithm dedicated to identifying allele-specific m6A (ASm6A) signals is still necessary for comprehensively understanding the regulatory mechanism of ASm6A.
To address this limitation, we have developed a meta-analysis approach using hierarchical Bayesian models to accurately detect ASm6A events at the peak level from MeRIP-seq data. For user convenience, we introduce a unified analysis pipeline named M6Allele, streamlining the assessment of significant ASm6A across single and paired samples. Applying M6Allele to MeRIP-seq data analysis of pulmonary fibrosis and lung adenocarcinoma reveals enrichment of ASm6A events in key regulatory genes associated with these diseases, suggesting their potential involvement in disease regulation.
Our effort provides a method for precisely identifying ASm6A events at the peak level, elucidates the interplay of m6A with human health and disease genetics, and paves a new visual angle for disease research. The M6Allele software is freely available at https://github.com/RenLabBioinformatics/M6Allele under the MIT license.
等位基因特异性调控事件是生物体中的关键机制，对许多基本生物学过程（如胚胎发育和染色体失活）至关重要。等位基因失衡在RNA表达和表观遗传水平上均有体现。最近的研究揭示了RNA N6-甲基腺苷（m6A）的等位基因特异性调控，强调了对其进行精确鉴定的必要性。然而，目前流行的方法主要集中在筛选与m6A相关的等位基因特异性遗传变异，而不是真正鉴定等位基因m6A事件。因此，构建一种专门用于鉴定等位基因特异性m6A（ASm6A）信号的新算法对于全面理解ASm6A的调控机制仍然是必要的。
为了解决这一局限性，我们开发了一种荟萃分析方法，使用分层贝叶斯模型从MeRIP-seq数据中准确检测峰值水平的ASm6A事件。为方便用户，我们引入了一个名为M6Allele的统一分析流程，简化了对单个和配对样本中显著ASm6A的评估。将M6Allele应用于肺纤维化和肺腺癌的MeRIP-seq数据分析，发现这些疾病相关关键调控基因中ASm6A事件富集，表明它们可能参与疾病调控。
我们的工作提供了一种在峰值水平精确鉴定ASm6A事件的方法，阐明了m6A与人类健康和疾病遗传学的相互作用，为疾病研究开辟了新视角。M6Allele软件可在https://github.com/RenLabBioinformatics/M6Allele上根据MIT许可免费获取。
