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

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    "abstract": "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.",
    "audit": {},
    "authors": "Wang X, Luan Y, Yue F.",
    "award": {
        "@id": "/awards/HG012070/",
        "component": "data coordination"
    },
    "creation_timestamp": "2023-03-09T23:49:16.195207+00:00",
    "donors": [],
    "file_sets": [],
    "lab": {
        "@id": "/labs/feng-yue/",
        "title": "Feng Yue, WashU"
    },
    "publication_identifiers": [
        "doi:10.1126/sciadv.abn9215"
    ],
    "published_by": [
        "IGVF"
    ],
    "release_timestamp": "2023-03-22T23:37:03.163044+00:00",
    "samples": [],
    "schema_version": "6",
    "software": [],
    "software_versions": [],
    "status": "released",
    "submitted_by": {
        "@id": "/users/6667a92a-d202-493a-8c7d-7a56d1380356/",
        "title": "Khine Lin"
    },
    "summary": "EagleC: A deep-learning framework for detecting a full range of structural variations from bulk and single-cell contact maps",
    "title": "EagleC: A deep-learning framework for detecting a full range of structural variations from bulk and single-cell contact maps",
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