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

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EagleC is a new deep-learning framework for detecting a full range of structural variations (SVs) in human genomes using Hi-C data. It uniquely identifies fusion genes missed by other methods and works across various chromatin interaction platforms.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Hi-C technology is a promising tool for detecting structural variations (SVs) in human genomes.
  • Existing algorithms have limitations in detecting a full spectrum of SVs at high resolution using Hi-C data.
  • Current methods primarily identify large-scale SVs (>1 Mb) and interchromosomal translocations.

Purpose of the Study:

  • To develop a novel framework, EagleC, for comprehensive SV detection using Hi-C data.
  • To enhance the resolution and scope of SV detection beyond current limitations.
  • To identify fusion genes and other SVs missed by conventional sequencing methods.

Main Methods:

  • Development of EagleC, a framework integrating deep-learning and ensemble-learning strategies.
  • Application of EagleC to Hi-C data for SV prediction.
  • Validation of EagleC's performance across diverse chromatin interaction datasets (HiChIP, ChIA-PET, capture Hi-C).

Main Results:

  • EagleC successfully predicts a full range of SVs at high resolution.
  • The framework uniquely identifies fusion genes not detected by whole-genome sequencing or nanopore sequencing.
  • EagleC demonstrates effectiveness across multiple chromatin interaction platforms.
  • Analysis of over 100 cancer cell lines and tumors identified a valuable set of high-quality SVs.

Conclusions:

  • EagleC represents a significant advancement in SV detection using Hi-C data.
  • The framework's ability to capture missed fusion genes and work across platforms offers new insights into genomic alterations.
  • EagleC's applicability to single-cell Hi-C data enables the study of SV heterogeneity in complex biological systems like tumors.