Abstract：
Interpreting the wealth of rare genetic variants discovered in population-scale sequencing efforts and deciphering their associations with human health and disease present a critical challenge due to the lack of sufficient clinical case reports. One promising avenue to overcome this problem is deep mutational scanning (DMS), a method of introducing and evaluating large-scale genetic variants in model cell lines. DMS allows unbiased investigation of variants, including those that are not found in clinical reports, thus improving rare disease diagnostics. Currently, the main obstacle limiting the full potential of DMS is the availability of functional assays that are specific to disease mechanisms. Thus, we explore high-throughput functional methodologies suitable to examine broad disease mechanisms. We specifically focus on methods that do not require robotics or automation but instead use well-designed molecular tools to transform biological mechanisms into easily detectable signals, such as cell survival rate, fluorescence or drug resistance. Here, we aim to bridge the gap between disease-relevant assays and their integration into the DMS framework.
摘要：
由于缺乏足够的临床病例报告，解释在人口规模测序工作中发现的大量罕见遗传变异并破译它们与人类健康和疾病的关联是一个严峻的挑战。克服这个问题的一个有希望的途径是深度突变扫描(DMS)，在模型细胞系中引入和评估大规模遗传变异的方法。DMS允许对变体进行公正的调查，包括那些在临床报告中没有发现的，从而改善罕见疾病诊断。目前,限制DMS全部潜力的主要障碍是疾病机制特异性功能测定的可用性。因此，我们探索了适合检查广泛疾病机制的高通量功能方法.我们特别关注不需要机器人或自动化的方法，而是使用精心设计的分子工具将生物机制转化为易于检测的信号。如细胞存活率，荧光或耐药性。这里,我们的目标是弥合疾病相关检测方法与纳入DMS框架之间的差距.
