RNA sequencing (RNA-seq) has emerged as a powerful approach to discover disease-causing gene regulatory defects in individuals affected by genetically undiagnosed rare disorders. Pioneering studies have shown that RNA-seq could increase the diagnosis rates over DNA sequencing alone by 8–36%, depending on the disease entity and tissue probed. To accelerate adoption of RNA-seq by human genetics centers, detailed analysis protocols are now needed. We present a step-by-step protocol that details how to robustly detect aberrant expression levels, aberrant splicing and mono-allelic expression in RNA-seq data using dedicated statistical methods. We describe how to generate and assess quality control plots and interpret the analysis results. The protocol is based on the detection of RNA outliers pipeline (DROP), a modular computational workflow that integrates all the analysis steps, can leverage parallel computing infrastructures and generates browsable web page reports.

RNA 测序 (RNA-seq) 已成为一种强有力的方法，可以发现受遗传未诊断的罕见疾病影响的个体的致病基因调控缺陷。开创性的研究表明，RNA-seq 可以将单独的 DNA 测序的诊断率提高 8-36%，具体取决于疾病实体和所探测的组织。为了加速人类遗传学中心对 RNA-seq 的采用，现在需要详细的分析协议。我们提出了一个分步协议，详细说明了如何使用专用统计方法稳健地检测 RNA-seq 数据中的异常表达水平、异常剪接和单等位基因表达。我们描述了如何生成和评估质量控制图并解释分析结果。该协议基于检测 RNA 异常值管道 (DROP)，