The Hi-C technique has been shown to be a promising method to detect structural variations (SVs) in human genomes. However, algorithms that can use Hi-C data for a full-range SV detection have been severely lacking. Current methods can only identify interchromosomal translocations and long-range intrachromosomal SVs (>1 Mb) at less-than-optimal resolution. Therefore, we develop EagleC, a framework that combines deep-learning and ensemble-learning strategies to predict a full range of SVs at high resolution. We show that EagleC can uniquely capture a set of fusion genes that are missed by whole-genome sequencing or nanopore. Furthermore, EagleC also effectively captures SVs in other chromatin interaction platforms, such as HiChIP, Chromatin interaction analysis with paired-end tag sequencing (ChIA-PET), and capture Hi-C. We apply EagleC in more than 100 cancer cell lines and primary tumors and identify a valuable set of high-quality SVs. Last, we demonstrate that EagleC can be applied to single-cell Hi-C and used to study the SV heterogeneity in primary tumors.

中文翻译：

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EagleC：一种深度学习框架，用于检测批量和单细胞接触图的全方位结构变化


Hi-C 技术已被证明是一种有前途的检测人类基因组结构变异 (SV) 的方法。然而，能够利用 Hi-C 数据进行全方位 SV 检测的算法却严重缺乏。目前的方法只能以低于最佳的分辨率识别染色体间易位和长距离染色体内 SV (>1 Mb)。因此，我们开发了 EagleC，这是一个结合深度学习和集成学习策略的框架，可以以高分辨率预测全范围的 SV。我们证明 EagleC 可以独特地捕获一组全基因组测序或纳米孔遗漏的融合基因。此外，EagleC 还可以有效捕获其他染色质相互作用平台中的 SV，例如 HiChIP、双端标签测序染色质相互作用分析 (ChIA-PET) 以及捕获 Hi-C。我们将 EagleC 应用到 100 多种癌细胞系和原发性肿瘤中，并鉴定出一组有价值的高质量 SV。最后，我们证明 EagleC 可以应用于单细胞 Hi-C 并用于研究原发性肿瘤的 SV 异质性。