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Future directions for high‐throughput splicing assays in precision medicine

机译：精密药物中的高通量剪接测定的未来方向

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Abstract Classification of variants of unknown significance is a challenging technical problem in clinical genetics. As up to one‐third of disease‐causing mutations are thought to affect pre‐mRNA splicing, it is important to accurately classify splicing mutations in patient sequencing data. Several consortia and healthcare systems have conducted large‐scale patient sequencing studies, which discover novel variants faster than they can be classified. Here, we compare the advantages and limitations of several high‐throughput splicing assays aimed at mitigating this bottleneck, and describe a data set of ~5,000 variants that we analyzed using our Massively Parallel Splicing Assay (MaPSy). The Critical Assessment of Genome Interpretation group (CAGI) organized a challenge, in which participants submitted machine learning models to predict the splicing effects of variants in this data set. We discuss the winning submission of the challenge (MMSplice) which outperformed existing software. Finally, we highlight methods to overcome the limitations of MaPSy and similar assays, such as tissue‐specific splicing, the effect of surrounding sequence context, classifying intronic variants, synthesizing large exons, and amplifying complex libraries of minigene species. Further development of these assays will greatly benefit the field of clinical genetics, which lack high‐throughput methods for variant interpretation.
机译：摘要临床遗传学中的未知意义变种的分类是一个具有挑战性的技术问题。对于最多三分之一的疾病突变被认为影响前mRNA剪接，重要的是准确地分类患者测序数据中的剪接突变。若干联盟和医疗保健系统已经进行了大规模的患者测序研究，该研究发现了比可以分类的更快的新型变体。在这里，我们比较旨在减轻这种瓶颈的几种高吞吐量剪接测定的优点和局限，并描述了使用我们大规模平行的剪接测定（Mapsy）分析的〜5,000变体的数据集。基因组解释组（CAGI）的批判性评估组织了一项挑战，其中参与者提交了机器学习模型，以预测该数据集中变体的拼接效果。我们讨论了挑战的胜利（MMSPLICE），这是现有软件表现优于现有软件的挑战（MMSPLICE）。最后，我们突出了克服Mapsy和类似测定的局限性的方法，例如组织特异性剪接，周围序列上下文的效果，分类内肾上腺素，合成大外显子，并扩增所述微型物种的复杂文库。这些分析的进一步发展将极大地使临床遗传学领域有益于缺乏用于变异解释的高通量方法。

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