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HiSVision: A Method for Detecting Large-Scale Structural Variations Based on Hi-C Data and Detection Transformer.

Haixia Zhai¹, Chengyao Dong¹, Tao Wang¹

¹School of Software, Henan Polytechnic University, Jiaozuo, 454003, China.

Interdisciplinary Sciences, Computational Life Sciences

|December 23, 2024

Summary

This summary is machine-generated.

HiSVision accurately identifies large structural variations (SVs) in the human genome using Hi-C data. This new method improves precision and F1 scores for detecting cancer-related genomic alterations.

Keywords:

Detection transformer Hi-C Object detection Structural variation

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Structural variations (SVs) significantly impact human health and disease, particularly cancer.
Hi-C sequencing is valuable for detecting large-scale SVs, but accurate identification from contact matrices remains challenging due to complex 3D genome structures.

Purpose of the Study:

To develop a novel computational method, HiSVision, for accurate identification of large-scale SVs from Hi-C data.
To leverage a detection transformer framework for enhanced SV detection in genomic imaging.

Main Methods:

Transformed Hi-C contact matrices into image representations.
Employed a detection transformer network to identify candidate SV regions within these images.
Implemented a breakpoint feature-based filtering system to refine SV calls.

Main Results:

HiSVision demonstrated superior performance compared to existing methods.
Achieved higher precision and F1 scores in identifying SVs on both cancer cell line and simulated datasets.

Conclusions:

HiSVision offers a robust and accurate approach for large-scale SV detection from Hi-C data.
The method shows promise for advancing cancer genomics research and understanding the role of SVs in disease.