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

released
1{
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8    "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.",
9    "audit": {},
10    "authors": "Wang X, Luan Y, Yue F.",
11    "award": {
12        "@id": "/awards/HG012070/"
13    },
14    "creation_timestamp": "2023-03-09T23:49:16.195207+00:00",
15    "donors": [],
16    "file_sets": [],
17    "lab": {
18        "@id": "/labs/feng-yue/",
19        "title": "Feng Yue, WashU"
20    },
21    "publication_identifiers": [
22        "doi:10.1126/sciadv.abn9215"
23    ],
24    "published_by": [
25        "IGVF"
26    ],
27    "release_timestamp": "2023-03-22T23:37:03.163044+00:00",
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29    "schema_version": "6",
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32    "status": "released",
33    "submitted_by": {
34        "@id": "/users/6667a92a-d202-493a-8c7d-7a56d1380356/",
35        "title": "Khine Lin"
36    },
37    "summary": "EagleC: A deep-learning framework for detecting a full range of structural variations from bulk and single-cell contact maps",
38    "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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