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

Xiaotao Wang, Yu Luan, Feng Yue*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

51 Scopus citations

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.

Original languageEnglish (US)
Article numbereabn9215
JournalScience Advances
Volume8
Issue number24
DOIs
StatePublished - Jun 2022

Funding

F.Y. is supported by NIH grants 5R01HG011207, 5R35GM124820, 5R01HG009906, and 1U24HG012070.

ASJC Scopus subject areas

  • General

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