There is a lack of approaches for identifying pathogenic genomic structural variants (SVs) although they play a crucial role in many diseases. We present a mechanism-agnostic machine learning-based workflow, called SVFX, to assign pathogenicity scores to somatic and germline SVs. In particular, we generate somatic and germline training models, which include genomic, epigenomic, and conservation-based features, for SV call sets in diseased and healthy individuals. We then apply SVFX to SVs in cancer and other diseases; SVFX achieves high accuracy in identifying pathogenic SVs. Predicted pathogenic SVs in cancer cohorts are enriched among known cancer genes and many cancer-related pathways.

中文翻译：

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SVFX：量化结构变异致病性的机器学习框架


尽管致病性基因组结构变异 (SV) 在许多疾病中发挥着至关重要的作用，但目前仍缺乏识别它们的方法。我们提出了一种与机制无关的基于机器学习的工作流程，称为 SVFX，用于为体细胞和种系 SV 分配致病性分数。特别是，我们为患病和健康个体的 SV 调用集生成体细胞和种系训练模型，其中包括基因组、表观基因组和基于保护的特征。然后我们将 SVFX 应用于癌症和其他疾病中的 SV； SVFX 在识别致病性 SV 方面实现了高精度。癌症队列中预测的致病性 SV 在已知的癌症基因和许多癌症相关途径中丰富。