计算方法在m6A研究中的应用及展望

doi:10.16288/j.yczz.24-373

遗传 ›› 2025, Vol. 47 ›› Issue (8): 903-927.doi: 10.16288/j.yczz.24-373

计算方法在m⁶A研究中的应用及展望

雷定伟¹(), 顾睿初²(), 谢晓雪², 丁时之³, 温翰³^,⁴^,⁵()

1.北京大学药学院，北京 100191
2.北京大学生命科学学院，北京 100871
3.北京科学智能研究院，北京 100084
4.北京大学核糖核酸研究中心，北京 100871
5.上海算法创新研究院，上海 200125

收稿日期:2025-02-26 修回日期:2025-06-19 出版日期:2025-06-24 发布日期:2025-06-24
通讯作者: 温翰，博士，副研究员，研究方向：分子动力学模拟，RNA科学，系统生物学。E-mail: wenh@aisi.ac.cn
作者简介:雷定伟，本科生，专业方向：药学。E-mail: dwlei@stu.pku.edu.cn
顾睿初，本科生，专业方向：生物信息。E-mail: pkugrc@outlook.com
第一联系人：
雷定伟和顾睿初并列第一作者。
基金资助:
北京市科技新星计划(2023111);上海市产业发展高质量专项(2023-GZL-RGZN-01005);上海市科技创新行动计划(24JS2820200);国家重点研发计划(2023YFC3403200)

Application and prospects of current computational methods in m⁶A research: a comprehensive review

Dingwei Lei¹(), Ruichu Gu²(), Xiaoxue Xie², Shizhi Ding³, Han Wen³^,⁴^,⁵()

1. School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
2. School of Life Sciences, Peking University, Beijing 100871, China
3. AI for Science Institute, Beijing 100084, China
4. Beijing Advanced Center of RNA Biology (BEACON), Peking University, Beijing 100871, China
5. Institute for Advanced Algorithms Research, Shanghai 200125, China

Received:2025-02-26 Revised:2025-06-19 Published:2025-06-24 Online:2025-06-24
Supported by:
Beijing Nova Program(2023111);Shanghai Specialized Program Promoting Industrial Development(2023-GZL-RGZN-01005);Shanghai Action Plan for Science, Technology and Innovation(24JS2820200);National Key R&D Program of China(2023YFC3403200)

摘要/Abstract

摘要：

N⁶-甲基腺嘌呤(m⁶A)修饰是真核生物mRNA中最丰富的修饰形式，对mRNA的剪接、加工、降解和翻译的调控具有关键作用。本文介绍了计算方法在m⁶A修饰研究中的应用，主要有数据驱动的方法预测m⁶A位点以及基于分子动力学方法探究m⁶A相关生物机制。文章首先回顾了m⁶A检测技术的发展历程，阐述了相应的数据处理方法，并整理了现有公开数据集，为计算模型的构建奠定数据基础。接着重点讨论机器学习与深度学习模型在m⁶A位点预测中的研究进展。最后描述了分子动力学模拟在解析m⁶A相关分子机制的贡献，展示了计算方法如何促进对这一复杂的表观遗传调控过程的理解。通过系统梳理相关内容，本文深入探讨了计算方法在m⁶A修饰领域的最新研究进展及其应用价值，为m⁶A相关的深入研究提供新的思路与启示。

关键词: N⁶-甲基腺嘌呤修饰, 修饰检测方法, 修饰位点预测, 人工智能算法, 分子动力学模拟

Abstract:

N⁶-methyladenosine (m⁶A) is the most prevalent modification in eukaryotic mRNA, playing a pivotal role in regulating various aspects of mRNA metabolism, including splicing, processing, degradation, and translation. This review provides a comprehensive overview of computational strategies employed in m⁶A research, with an emphasis on data-driven methodologies for the prediction of m⁶A sites and molecular dynamics simulations for deciphering m⁶A-associated biological mechanisms. The article first discusses the evolution of m⁶A detection technologies, outlines the corresponding data processing methods, and summarizes publicly available datasets that serve as essential resources for constructing computational models. Subsequently, we highlight research advancements in machine learning and deep learning models for m⁶A site prediction. Finally, we demonstrate the contributions of molecular dynamics simulations in unravelling m⁶A-related molecular mechanisms, illustrating how computational methods facilitate the understanding of this complex epigenetic regulation. By systematically synthesizing relevant content, this review further discusses the latest research progress and application values of computational methods in m⁶A modification, offering new perspectives and insights for in-depth investigations.

Key words: N⁶-methyladenosine modification, modification detection method, modification sites prediction, artificial intelligence algorithm, molecular dynamic simulation

雷定伟, 顾睿初, 谢晓雪, 丁时之, 温翰. 计算方法在m⁶A研究中的应用及展望[J]. 遗传, 2025, 47(8): 903-927.

Dingwei Lei, Ruichu Gu, Xiaoxue Xie, Shizhi Ding, Han Wen. Application and prospects of current computational methods in m⁶A research: a comprehensive review[J]. Hereditas(Beijing), 2025, 47(8): 903-927.

图/表 6

图1

表1

m6A相关的检测方法总结"

分类	方法	分辨率	RNA 输入量	优势	局限性	发布年份及参考文献
抗体依赖的免疫沉淀法	m⁶A-seq/ MeRIP-seq	100~200 nt	2~400 μg mRNA	简单的RNA库制备步骤	1. 分辨率低； 2. 准确度低； 3. 特异性差，不能区分m⁶A和m⁶Am	2012^[5]
	PA-m⁶A-seq	~23 nt	12 μg mRNA	准确度高；兼容核酸-蛋白相互作用的研究	对样本输入量的需求高；对培养环境要求高；交联率低	2015^[24]
	m⁶A-CLIP-seq	~100 nt	1 μg mRNA	1. 对样本输入量要求低； 2. 利用突变和截断特征来识别峰内的m⁶A位点确保了高水平的特异性； 3. 每个转录本可以检测多个m⁶A位点	1. 缺乏化学计量信息； 2. 无法区分紧密沉积的m⁶A簇； 3. 不是直接鉴定单m⁶A位点，而是从相邻嘧啶位点突变中进行推断	2018^[25]
	miCLIP-seq	单核苷酸	20 μg mRNA	1. 特异性和准确度高； 2. 无需对细胞进行修饰核苷酸预处理； 3. 是最被广泛使用的m⁶A测序方法	1. 缺乏化学计量信息； 2. 对样本输入量要求高； 3. RNA库制备流程复杂； 4. 交联率(crosslinking yield)低导致假阳性结果多； 5. 不是直接鉴定单m⁶A位点，而是从相邻嘧啶位点突变中进行推断	2017^[26]
	m⁶A-LAIC-seq	100~200 nt	2 μg 2× poly(A)选择的mRNA	1. 包含半化学计量信息； 2. 阐明组织或细胞水平下m⁶A水平的特异性； 3. 准确度和灵敏度高	1. 不能区分m⁶A和m⁶Am； 2. 实验过程复杂； 3. 需要抗体浓度的经验滴定； 4. 分辨率相对较低	2016^[27]
	m⁶AISH-PLA	单核苷酸	−	1. 可以在mRNA的特定位置识别m⁶A； 2. 实验条件温和，有助于保存细胞结构和形态； 3. 揭示特定m⁶A RNA的空间位置	1. 通量相对较低； 2. 无法区分短序列范围内的m⁶A位点	2021^[28]
特异性酶结合法	MAZTER-seq	单核苷酸	100 ng mRNA	1. 可以获取mRNA上ACA位点的m⁶A化学计量信息； 2. 假阳性概率低； 3. 与抗体依赖的免疫沉淀法相比，实验过程相对简单； 4. 该方法能够区分m⁶A和m⁶Am	1. 检测区域有限(仅检测ACA序列上下文中的m⁶A位点)； 2. 无法区分近距离的ACA位点； 3. 对于绝对量化，切割效率需要在甲基化缺陷背景下进行标准化； 4. MazF并非特异性识别ACA位点，在类似ACA的序列上也观察到少量的切割，如ACG或AAA，导致确度降低	2019^[29]
	DART-seq	单核苷酸	10 ng~1μg total RNA	1. 对样本输入量要求低； 2. 能够区分m⁶A和m⁶Am	1. 对低丰度m⁶A位点的灵敏度低； 2. 可能由于非特异性C-to-U编辑事件而产生假阳性信号	2019^[30]
	m⁶A-REF-seq	单核苷酸	100 ng mRNA	准确性和可靠性高	仅检测ACA序列上下文中的m⁶A位点	2019^[31]
	m⁶A-SEAL-seq	100-200 nt	5 μg mRNA	1. 对样本输入量要求低； 2. 特异性和可靠性较高； 3. 对于低丰度位点的检测很灵敏	1. 分辨率低； 2. 缺乏化学计量信息； 3. 操作步骤多、耗时长	2020^[32]
	scDART-seq	单核苷酸	900 ng total RNA	1. 第一种单细胞水平的m⁶A检测方法； 2. 准确度高	1. 需要在感兴趣的细胞或组织中表达外源基因APOBEC1-YTH； 2. 难以应用于组织样品	2022^[33]
	eTAM-seq	单核苷酸	250 ng total RNA	1. 实现了RNA每个位点的定量化学计量信息； 2. 对样本输入量要求低； 3. 灵敏度高	1. 对甲基化水平低的位点不敏感； 2. 需要一个阴性对照文库(由体外转录的转录组制备)和一个专用的统计模型； 3. 需要表达和纯化TadA8.20酶	2023^[34]
基于化学处理的方法	SCARLET	单核苷酸	1 μg mRNA	1. 在特定位点精确测量m⁶A的化学计量； 2. 对于生物样品中的低丰度转录本准确度高	1. 耗时长、步骤复杂； 2. 对样本输入量需求高； 3. 使用放射性试剂	2013^[35]
	SELECT	单核苷酸	>1 μg total RNA	1. 包含化学计量信息； 2. 准备步骤相对简单	1. 假阳性概率高； 2. 通量低； 3. 需要引入对照组并生成标准曲线以完成检测和定量分析	2018^[36]
	m⁶A-label-seq	单核苷酸	50ng total RNA	1. 准确度高； 2. 适用于细胞内RNA多种不同甲基化序列(m⁶A motif)的鉴定； 3. 对测定m⁶A簇修饰有优势	1. 缺乏化学计量学信息； 2. 在标记产率和标记时间窗口尺度方面需要优化提高	2020^[37]
	m⁶A-ORL-seq	单核苷酸	30 ng total RNA或5ng mRNA	1. 准确度和灵敏度高； 2. 测序成本相对较低； 3. 可用于DNA中m⁶A水平的估计和⁶mdA的检测	在生信分析中易发生数据突变	2022^[38]
	m⁶A-SAC-seq	单核苷酸	2~50 ng mRNA	1. 对样本输入量要求低； 2. 包含化学计量信息； 3. 准确度高； 4. 可以应用于各种类型的生物样品，包括新鲜和冷冻组织以及福尔马林固定或石蜡包埋的样品； 5. 可以识别大量m⁶A位点	MjDim1对GAC基序的明显偏好导致在检测AAC位点方面存在限制	2023^[39]
	GLORI-seq	单核苷酸	100 ng	1. 实现对m⁶A进行绝对定量评估； 2. 准确度、特异性和灵敏度高； 3. 结果高度可重复； 4. 揭示了 m⁶A 的定量图谱	1. 测序成本相对较高； 2. 无法区分m⁶A和其他A修饰，例如m1A或m⁶Am； 3. 灵敏度可能会受到化学标记效率的影响，并且会根据实验设置而变化	2023^[40]
基于纳米孔直接测序的方法	Nanopore DRS	单核苷酸	−	1. 不受试剂偏差与扩征偏差的影响； 2. 可以测复杂转录本的全修饰图景	1. 修饰引起的电信号偏弱，误差较大； 2.数据分析困难	2019^[41]

表1

图2

表2

m6A数据处理方法"

方法	输入文件	峰值检测	算法	优势	局限性	发布年份及参考文献
MACS	BAM	支持，主要用于ChIP-seq数据	利用泊松分布模型来评估测序深度的富集程度	可有效地处理ChIP-Seq数据中的噪声	需要依赖合适的参数设置和对照组数据以提高稳定性	2008^[49]
exomePeak	BAM	支持，基于exomePeak	对所有重复样本的平均标准化计数进行 Fisher 精确检验	灵敏度(真阳性率)较高，真发现率(TDR)表现突出	1. 使用合并的读取计数，忽略了重复样本间的异质性； 2. 假发现率(FDR)/I型错误控制较差； 3. 大样本时运行时间较长	2014^[50]
FET-HMM	Read count matrix	支持，基于exomePeak	结合Fisher精确检验和隐马尔可夫模型(HMM)以提高DMR(差异甲基化区)检测的空间分辨率	1. 兼具高真发现率(TDR)和灵敏度； 2. I 型错误控制达到理论最优水平	1.使用合并的读取计数，忽略了重复样本间的异质性 2. 假发现率(FDR)/假阳性控制较差	2015^[51]
MeTDiff	BAM	支持，基于HEPeak	针对原始IP计数构建 Beta-二项分布模型，并考虑IP和输入样本的总计数	内存消耗少	1. 未校正测序深度变异的影响 2. 在小样本量情况下检测效力较差 3. 大样本数据运行时间较长	2015^[52]
MeTPeaK	BAM	支持，主要用于MeRIP-seq数据	采用隐马尔可夫模型和Beta分布分层建模	1. 隐马尔可夫模型能够捕捉序列数据中的隐含状态； 2. 适用于处理有复杂噪声的数据	1. 计算资源需求较高； 2. 依赖高质量数据与精确参数配置	2016^[53]
MATK	BAM	支持，主要用于MeRIP-seq数据	用于m⁶A修饰检测的工具包，集成了包括统计模型、机器学习、深度学习等多种方法	1. 可根据数据特点选择最优方法； 2. 可扩展性好	不同算法的性能可能因数据集而异，需要进行比较和选择	2016^*
DRME	Read count matrix	支持，基于exomePeak	针对原始IP和输入计数构建负二项分布模型，仅使用输入计数估计基线表达	即使在小样本和低表达情况下，仍能保持最高灵敏度	1. 变异建模不合理； 2. P值要求“宽松”，导致最高的假发现率(FDR)和I型错误	2016^[54]
QNB	Read count matrix	支持，基于 exomePeak	针对原始IP和输入计数构建负二项分布模型，并同时使用IP和输入计数估计基线表达	较低的假发现率(FDR)	变异建模不合理	2017^[55]
exomePeak2	BAM	支持，基于 exomePeak2	应用DESeq2进行回归分析，并通过三次样条展开的泊松广义线性模型(GLM)调整GC含量偏差	1. 高真发现率(TDR)和较低的假发现率(FDR)控制较优； 2. p值分布合理	1.不能在模型中纳入额外的实验因素； 2. 内存消耗较大	2019^[56]
RADAR	BAM	否	针对预处理的IP计数数据构建泊松随机效应模型，并允许纳入混杂因素	首个考虑混杂因素的m⁶A分析方法	1. 对预处理数据的分布假设不合理； 2. 运行时间较长	2019^[57]
TRESS	BAM	支持，基于TRESS	针对原始IP和输入计数数据构建负二项分布模型，并允许纳入混杂因素	1. 真发现率(TDR)高； 2. 假发现率(FDR)/I 型错误控制较优； 3. P值分布合理； 4. 运行时间最短，内存消耗低	在小样本条件下灵敏度较低	2022^[58]
m⁶ACali	BAM	支持，主要用于MeRIP-seq数据	基于机器学习方法整合序列特征和基因注释信息	减少了假阳性位点	需要较多的训练数据和特征信息	2024^[59]

表2

表3

m6A相关的数据库总结"

数据库名称	主要技术	数据类型	覆盖物种		位点数量	数据来源	发布年份及参考文献
MeT-DB	MeRIP-seq	m⁶A峰	人类(Homo sapiens)，小鼠(Mus musculus)		约30万个m⁶A修饰位点	公共数据库	2014^[60]
RMBase	m⁶A-seq，位点预测	单碱基位点的修饰	人类，小鼠和酵母(Saccharomyces cerevisiae)		大约124,200个m⁶A修饰位点	来自GEO数据库及论文的补充材料	2015^[61]
MeT-DB V2.0	MeRIP-seq	m⁶A峰，单碱基位点的修饰	人类，小鼠等7个物种		大约370万个m⁶A修饰位点	26项独立研究	2017^[62]
m⁶AVar	miCLIP/PA-m⁶A-seq, MeRIP-seq, 全转录组预测	m⁶A相关遗传变异	人类		16,132个高置信度，71,321个中等置信度，326,915个低置信度m⁶A修饰位点	GWAS、ClinVar	2017^[63]
RMBase V2.0	m⁶A-seq，MeRIP- seq，位点预测	单碱基位点的修饰	人类，小鼠等13个物种		约137万个m⁶A修饰位点	47项研究中的约600个数据集	2017^[64]
CVm⁶A	MeRIP-seq，m⁶A-CLIP-seq	m⁶A峰	人类，小鼠		40,950个人类细胞系的m⁶A修饰位点，179,201个鼠类细胞系的m⁶A修饰位点	公共数据库	2019^[65]
M6A2Target, RM2Target	低通量和高通量研究	WERs靶标	人类和小鼠		−	NCBI、PubMed、GEO和SRA等数据库	2020^[66,67]
M6ADD	人工筛选和高通量测序	m⁶A-疾病关联	人类		409,229个m⁶A-疾病关联	实验数据、GWAS、ClinVar	2020^[68]
m⁶A-Atlas	7种单碱基分辨率技术	单碱基分辨率m⁶A位点	人类、小鼠和黑猩猩(Pan troglodytes)等7个物种		442,162个m⁶A修饰位点	高通量测序数据	2020^[56]
RMVar	−	m⁶A修饰相关遗传变异	人类		m⁶A修饰的1,461,691个遗传变异	实验数据、GWAS、ClinVar	2020^[69]
REPIC	MeRIP-seq，m⁶A-seq	m⁶A峰	11种生物		大约1,000万个m⁶A峰	49项研究的672个样本	2020^[70]
m⁶A-TSHub	−	m⁶A峰	人类	来自23个人类组织的184,554个修饰位点和499,369个来自25种肿瘤的修饰位点		GEO，NGDC	2022^[71]
DirectRMDB	直接RNA测序(DRS)	单碱基分辨率m⁶A位点	人类、小鼠等25个物种	586,167个修饰数据		来源于39项独立研究	2022^[72]
PRMD	MeRIP-seq	m⁶A峰	20种植物	6,816,278个m⁶A峰		主要来自EnsemblPlants	2023^[73]
RMBase V3.0	单碱基分辨率以及有限分辨率的测序技术	m⁶A峰或单碱基位点的修饰	与m⁶A修饰相关的主要有人类，小鼠等物种	67万个修饰数据		GEO	2023^[74]
RMVar 2.0	高通量测序，单碱基分辨率的修饰位点分析等	m⁶A峰或单碱基位点的修饰	人、小鼠	1,680,598个m⁶A修饰数据		文献报道，公共数据库	2024^[75]
m⁶A-Atlas V2.0	13种单碱基分辨率技术，2种单细胞m⁶A分析技术，MeRIP-seq	m⁶A峰，单碱基位点的修饰	多物种	797,091个m⁶A单碱基修饰位点		公共数据库，文献报道	2024^[76]

表3

表4

m6A修饰位点预测模型总结"

模型名称	物种	模型架构	特点	数据来源	发布年份及参考文献
DeepM6ASeq	人类、小鼠、斑马鱼(Danio rerio)	CNN+BiLSTM	提取序列特征，显著性图可视化，预测性能优于传统分类器，发现新m⁶A阅读器FMR1	SRAMP^[77]、Dominissini^[5]	2018^[78]
TDm⁶A	人类	CNN+LSTM+迁移学习	学习常见和细胞类型特异性motif，预测lncRNA m⁶A位点，适应不同细胞类型	SRAMP^[77]	2020^[79]
MASS	人类、小鼠、恒河猴(Macaca mulatta)、大鼠、猪和斑马鱼	BiLSTM+CNN+Multi- Head Attention	捕捉多物种共享特征，课程学习策略，减少物种偏差，增强泛化能力和解释性	RMBase v2.0^[64]、SRAMP17^[77]、nano20^[80]	2021^[81]
Deepm⁶A-MT	人类、大鼠(Rattus norvegicus)和小鼠	Bi-GRU+CNN	专注多组织的m⁶A预测，融合局部和全局信息，多种序列表示方法，提升预测精度，用户友好网络服务器	iRNA-m⁶A^[82]	2024^[83]
deepSRAMP	人类、小鼠、大鼠	Transformer+RNN	跨物种泛化能力强，识别isoform水平m⁶A修饰位点，引入注意力机制，处理复杂多转录本基因	m⁶A-Atlas v2.0^[76]、GSE63753、GSE1154、GSE86336和GSE98623	2024^[84]
m⁶ATM	人类	WaveNet+Dual-Stream Multiple Instance Learning (DSMIL)	结合1D卷积和MIL策略提取m⁶A特征，使用纳米孔测序数据进行单碱基分辨率m⁶A预测，支持m⁶A化学计量估计	GSE124309、 GSE132971、GSE265754、 GSE265867和PRJEB40872	2024^[85]
pum6a	人类、小鼠	Attention-based Positive and Unlabeled Multi-Instance Learning (MIL)	结合电信号特征和碱基比对数据，使用加权Noisy-OR概率机制，增强低覆盖位点的m⁶A检测灵敏度和准确性	HEK293T^[86]、Curlcake^[87]、GSE195618	2025^[88]
TandemMod	人类、水稻(Oryza sativa)	1D-CNN+BiLSTM+ Attention	采用迁移学习，提高新修饰类型的检测效率；可处理单细胞或批量数据，检测修饰比例	IVET^[89]、Curlcake^[87]、ELIGOS^[88]、HEK293T^[86]	2024^[89]

表4

参考文献 126

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计算方法在m⁶A研究中的应用及展望

Application and prospects of current computational methods in m⁶A research: a comprehensive review

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