4.8 Article

EagleC: A deep-learning framework for detecting a full range of structural variations from bulk and single-cell contact maps

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SCIENCE ADVANCES
卷 8, 期 24, 页码 -

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AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/sciadv.abn9215

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  1. NIH [5R01HG011207, 5R35GM124820, 5R01HG009906, 1U24HG012070]

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The Hi-C technique is promising for detecting structural variations in human genomes, but lacks algorithms for full-range SV detection. EagleC combines deep-learning and ensemble-learning strategies to predict a full range of SVs at high resolution, uniquely capturing a set of fusion genes. EagleC also effectively captures SVs in other chromatin interaction platforms.
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.

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