Ancient DNA (aDNA) studies often rely on standard methods of mutation calling, optimized for high-quality contemporary DNA but not for excessive contamination, time- or environment-related damage of aDNA. In the absence of validated datasets and despite showing extreme sensitivity to aDNA quality, these methods have been used in many published studies, sometimes with additions of arbitrary filters or modifications, designed to overcome aDNA degradation and contamination problems. The general lack of best practices for aDNA mutation calling may lead to inaccurate results. To address these problems, we present ARIADNA (ARtificial Intelligence for Ancient DNA), a novel approach based on machine learning techniques, using specific aDNA characteristics as features to yield improved mutation calls. In our comparisons of variant callers across several ancient genomes, ARIADNA consistently detected higher-quality genome variants with fast runtimes, while reducing the false positive rate compared with other approaches.

中文翻译：

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ARIADNA：用于古代DNA变异发现的机器学习方法。

古代DNA（aDNA）研究通常依赖于标准的突变调用方法，这些方法针对高质量的当代DNA进行了优化，但并未针对aDNA的过度污染，与时间或环境相关的损害进行优化。在没有经过验证的数据集的情况下，尽管显示出对aDNA质量的极端敏感性，但这些方法已用于许多已发表的研究中，有时会添加任意过滤器或修改，以克服aDNA降解和污染问题。通常缺少有关aDNA突变调用的最佳实践，可能会导致结果不准确。为了解决这些问题，我们提出了ARIADNA（古代DNA的人工智能），这是一种基于机器学习技术的新颖方法，使用特定的aDNA特征作为特征来产生改进的突变调用。