Abstract：
Routine exome sequencing (ES) in individuals with neurodevelopmental disorders (NDD) remains inconclusive in >50% of the cases. Research analysis of unsolved cases can identify novel candidate genes but is time-consuming, subjective, and hard to compare between labs. The field, therefore, requires automated and standardized assessment methods to prioritize candidates for matchmaking. We developed AutoCaSc (https://autocasc.uni-leipzig.de) based on our candidate scoring scheme. We validated our approach using synthetic trios and real in-house trio ES data. AutoCaSc consistently (94.5% of all cases) scored the relevant variants in valid novel NDD genes in the top three ranks. In 93 real trio exomes, AutoCaSc identified most (97.5%) previously manually scored variants while evaluating additional high-scoring variants missed in manual evaluation. It identified candidate variants in previously undescribed NDD candidate genes (CNTN2, DLGAP1, SMURF1, NRXN3, and PRICKLE1). AutoCaSc enables anybody to quickly screen a variant for its plausibility in NDD. After contributing >40 descriptions of NDD-associated genes, we provide usage recommendations based on our extensive experience. Our implementation is capable of pipeline integration and therefore allows the screening of large cohorts for candidate genes. AutoCaSc empowers even small labs to a standardized matchmaking collaboration and to contribute to the ongoing identification of novel NDD entities.
摘要：
神经发育障碍（NDD）患者的常规外显子组测序（ES）在>50％的病例中仍然没有定论。对未解决病例的研究分析可以识别新的候选基因，但耗时，主观,很难在实验室之间进行比较。场,因此,需要自动化和标准化的评估方法来优先考虑匹配的候选人。我们开发了AutoCaSc（https://autocasc。uni-leipzig.de)基于我们的候选人评分方案。我们使用合成三重奏和真实的内部三重奏ES数据验证了我们的方法。AutoCaSc始终（占所有病例的94.5％）在前三名的有效新NDD基因中对相关变体进行评分。在93个真正的三重奏外显子中，AutoCaSc鉴定了大多数(97.5%)先前手动评分的变体,同时评估在手动评估中遗漏的其他高评分变体。它鉴定了先前未描述的NDD候选基因（CNTN2，DLGAP1，SMURF1，NRXN3和PRICKLE1）中的候选变体。AutoCaSc使任何人都可以在NDD中快速筛选变体的合理性。在贡献了>40个NDD相关基因描述后，我们根据我们丰富的经验提供使用建议。我们的实施能够进行管道整合，因此可以筛选大型队列的候选基因。AutoCaSc甚至使小型实验室能够进行标准化的配对协作，并为正在进行的新型NDD实体的识别做出贡献。
