A method for structural variant detection using Hi-C contact matrix and neural networks.

Structural variations (SVs) play a key role in many human diseases and are major causative factors of malignant tumors. High-throughput chromatin conformation capture (Hi-C) technology captures spatial interactions between genomic fragments, thereby enhancing SV identification and localization and compensating for the limitations of sequencing-based approaches in detecting complex variants. However, existing methods based on Hi-C data still suffer from low accuracy, limited applicability, and difficulties in handling multiple types of SVs simultaneously. In this study, we propose VarHiCNet, a novel method for detecting structural variations from Hi-C data. Contact matrices are preprocessed and converted into image-like representations. These representations are then input into an improved RT-DETR network to identify candidate SV regions. Subsequently, a filtering and classification network is applied for precise breakpoint detection. Evaluated on six cancer cell lines, VarHiCNet demonstrates high accuracy and stability in SV identification, with overall performance surpassing that of existing methods. The source code is available at https://github.com/000425/VarHiCNet. Experimental results indicate that VarHiCNet achieves superior performance in detecting structural variations compared to other methods, offering a robust and accurate tool for genomic studies.