Over the last decade next generation sequencing (NGS) has been extensively used to identify new pathogenic mutations and genes causing rare genetic diseases. The efficient analyses of NGS data is not trivial and requires a technically and biologically rigorous pipeline that addresses data quality control, accurate variant filtration to minimize false positives and false negatives, and prioritization of the remaining genes based on disease genomics and physiological knowledge. This review provides a pipeline including all these steps, describes popular software for each step of the analysis, and proposes a general framework for the identification of causal mutations and genes in individual patients of rare genetic diseases.

中文翻译：

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通过计算方法鉴定单例患者基因组中的致病突变。

在过去的十年中，下一代测序（NGS）已被广泛用于鉴定新的致病突变和引起罕见遗传病的基因。NGS数据的有效分析并非易事，需要技术和生物学上严格的流程来处理数据质量控制，准确的变异过滤以最大程度地减少假阳性和假阴性，并根据疾病基因组学和生理知识对其余基因进行优先级排序。这篇综述提供了包括所有这些步骤的流程，描述了分析的每个步骤的流行软件，并提出了一种用于识别罕见遗传病个体患者中因果突变和基因的通用框架。