ArticlePLoS computational biology2023
HiSV: A control-free method for structural variation detection from Hi-C data.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It carries an expression of concern. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A method for structural variant detection using Hi-C contact matrix and neural networks.Scientific reports · 2026Article
- HiSVision: A Method for Detecting Large-Scale Structural Variations Based on Hi-C Data and Detection Transformer.Interdisciplinary sciences, computational life sciences · 2025Article
- Review
- Identification of multiple genomic alterations and prediction of neoantigens from circulating tumor cells at the single-cell level.Nature communications · 2025Article
- Charm is a flexible pipeline to simulate chromosomal rearrangements on Hi-C-like data.NAR genomics and bioinformatics · 2025Article
- H3K27me3 and the PRC1-H2AK119ub pathway cooperatively maintain heterochromatin and transcriptional silencing after the loss of H3K9 methylation.Epigenetics & chromatin · 2025Article
- Expression of Concern: HiSV: A control-free method for structural variation detection from Hi-C data.PLoS computational biology · 2025Article
- DeCGR: an interactive toolkit for deciphering complex genomic rearrangements from Hi-C data.BMC genomics · 2024Article
- LcDel: deletion variation detection based on clustering and long reads.Frontiers in genetics · 2024Article
- Chromosome conformation capture technologies as tools to detect structural variations and their repercussion in chromatin 3D configuration.Frontiers in cell and developmental biology · 2023Review
Corrections and comments
- Expression of concern
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Structural variations (SVs) play an essential role in the evolution of human genomes and are associated with cancer genetics and rare disease. High-throughput chromosome capture (Hi-C) technology probed all genome-wide crosslinked chromatin to study the spatial architecture of chromosomes. Hi-C read pairs can span megabases, making the technology useful for detecting large-scale SVs. So far, the identification of SVs from Hi-C data is still in the early stages with only a few methods available. Therefore, we developed HiSV (Hi-C for Structural Variation), a control-free method for identifying large-scale SVs from a Hi-C sample. Inspired by the single image saliency detection model, HiSV constructed a saliency map of interaction frequencies and extracted saliency segments as large-scale SVs. By evaluating both simulated and real data, HiSV not only detected all variant types, but also achieved a higher level of accuracy and sensitivity than most existing methods. Moreover, our results on cancer cell lines showed that HiSV effectively detected eight complex SV events and identified two novel SVs of key factors associated with cancer development. Finally, we found that integrating the result of HiSV helped the WGS method to identify a total number of 94 novel SVs in two cancer cell lines.
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Registered trials
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