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Related Experiment Video

Updated: Aug 15, 2025

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

HiSV: A control-free method for structural variation detection from Hi-C data.

Junping Li¹, Lin Gao¹, Yusen Ye¹

¹Department of Computer Science, School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi, China.

Plos Computational Biology

|January 6, 2023

Summary

This summary is machine-generated.

HiSV is a novel method for detecting large-scale structural variations (SVs) in genomes using Hi-C data. This approach enhances accuracy and sensitivity, aiding in cancer genetics research.

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Capturing Chromosome Conformation Across Length Scales

Capturing Chromosome Conformation Across Length Scales

Published on: January 20, 2023

Area of Science:

Genomics
Bioinformatics
Cancer Genetics

Background:

Structural variations (SVs) are crucial in human genome evolution, cancer genetics, and rare diseases.
High-throughput chromosome capture (Hi-C) technology offers insights into genome spatial architecture and large-scale SV detection.
Current methods for SV identification from Hi-C data are limited.

Purpose of the Study:

To develop a novel, control-free method (HiSV) for identifying large-scale structural variations from Hi-C data.
To improve the accuracy and sensitivity of SV detection compared to existing methods.
To apply HiSV to cancer cell lines for identifying novel SVs associated with cancer development.

Main Methods:

Developed HiSV, a control-free method inspired by image saliency detection models.
Constructed a saliency map of interaction frequencies from Hi-C data.
Extracted saliency segments to identify large-scale SVs.

Main Results:

HiSV detected all variant types with higher accuracy and sensitivity than existing methods on simulated and real data.
Effectively identified eight complex SV events and two novel SVs in cancer cell lines.
Integration with Whole Genome Sequencing (WGS) identified 94 novel SVs in two cancer cell lines.

Conclusions:

HiSV is an effective tool for identifying large-scale SVs from Hi-C data.
The method shows promise in advancing cancer genetics research by discovering novel SVs.
HiSV complements WGS for comprehensive SV detection.