N6-methyladenosine (m6A) is the methylation of the adenosine at the nitrogen-6 position, which is the most abundant RNA methylation modification and involves a series of important biological processes. Accurate identification of m6A sites in genome-wide is invaluable for better understanding their biological functions. In this work, an ensemble predictor named iRNA-m6A was established to identify m6A sites in multiple tissues of human, mouse and rat based on the data from high-throughput sequencing techniques. In the proposed predictor, RNA sequences were encoded by physical-chemical property matrix, mono-nucleotide binary encoding and nucleotide chemical property. Subsequently, these features were optimized by using minimum Redundancy Maximum Relevance (mRMR) feature selection method. Based on the optimal feature subset, the best m6A classification models were trained by Support Vector Machine (SVM) with 5-fold cross-validation test. Prediction results on independent dataset showed that our proposed method could produce the excellent generalization ability. We also established a user-friendly webserver called iRNA-m6A which can be freely accessible at http://lin-group.cn/server/iRNA-m6A. This tool will provide more convenience to users for studying m6A modification in different tissues.

N6-甲基腺苷（m6A）是腺苷在氮6位的甲基化，是最丰富的RNA甲基化修饰，涉及一系列重要的生物过程。准确识别全基因组中的 m6A 位点对于更好地了解其生物学功能非常有价值。在这项工作中，建立了一个名为 iRNA-m6A 的整体预测器，用于根据高通量测序技术的数据来识别人类、小鼠和大鼠多个组织中的 m6A 位点。在所提出的预测器中，RNA序列由物理化学特性矩阵、单核苷酸二进制编码和核苷酸化学特性编码。随后，使用最小冗余最大相关性（mRMR）特征选择方法对这些特征进行优化。基于最优特征子集，通过支持向量机（SVM）和5折交叉验证测试训练出最佳的m6A分类模型。对独立数据集的预测结果表明，我们提出的方法可以产生出色的泛化能力。我们还建立了一个名为 iRNA-m6A 的用户友好型网络服务器，可以在 http://lin-group.cn/server/iRNA-m6A 上免费访问。该工具将为用户研究不同组织中的m6A修饰提供更多便利。