Methylation of DNA N6-methyladenosine (6mA) is a type of epigenetic modification that plays pivotal roles in various biological processes. The accurate genome-wide identification of 6mA is a challenging task that leads to understanding the biological functions. For the last 5 years, a number of bioinformatics approaches and tools for 6mA site prediction have been established, and some of them are easily accessible as web application. Nevertheless, the accurate genome-wide identification of 6mA is still one of the challenging works that lead to understanding the biological functions. Especially in practical applications, these tools have implemented diverse encoding schemes, machine learning algorithms and feature selection methods, whereas few systematic performance comparisons of 6mA site predictors have been reported. In this review, 11 publicly available 6mA predictors evaluated with seven different species-specific datasets (Arabidopsis thaliana, Tolypocladium, Diospyros lotus, Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans and Escherichia coli). Of those, few species are close homologs, and the remaining datasets are distant sequences. Our independent, validation tests demonstrated that Meta-i6mA and MM-6mAPred models for A. thaliana, Tolypocladium, S. cerevisiae and D. melanogaster achieved excellent overall performance when compared with their counterparts. However, none of the existing methods were suitable for E. coli, C. elegans and D. lotus. A feasibility of the existing predictors is also discussed for the seven species. Our evaluation provides useful guidelines for the development of 6mA site predictors and helps biologists selecting suitable prediction tools.

中文翻译：

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基于网络的 DNA N6-甲基腺嘌呤位点预测工具的批判性评估

甲基化DNA N6-甲基腺苷 (6mA) 是一种表观遗传修饰，在各种生物过程中起着关键作用。6mA 的准确全基因组识别是一项具有挑战性的任务，可导致理解生物功能。在过去的 5 年中，已经建立了许多用于 6mA 站点预测的生物信息学方法和工具，其中一些可以作为 Web 应用程序轻松访问。尽管如此，6mA 的准确全基因组识别仍然是导致理解生物功能的具有挑战性的工作之一。特别是在实际应用中，这些工具已经实现了多种编码方案、机器学习算法和特征选择方法，而很少有关于 6mA 站点预测器的系统性能比较的报道。在这次审查中，拟南芥、Tolypocladium、Diospyros lotus、酿酒酵母、黑腹果蝇、秀丽隐杆线虫和大肠杆菌）。其中，很少有物种是近亲，其余的数据集是远距离序列。我们的独立验证测试表明，与对应物相比，拟南芥、Tolypocladium、酿酒酵母和黑腹果蝇的Meta-i6mA 和 MM-6mAPred 模型实现了出色的整体性能。然而，现有的方法都不适用于大肠杆菌、秀丽隐杆线虫和莲花。. 还讨论了七个物种的现有预测器的可行性。我们的评估为 6mA 位点预测器的开发提供了有用的指导，并帮助生物学家选择合适的预测工具。