ArticleBMC bioinformatics2023
SVcnn: an accurate deep learning-based method for detecting structural variation based on long-read data.
Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Challenges in Bringing Pangenome Research Into Breeding: A Case Study in Rice.Plant biotechnology journal · 2026Review
- Article
- Integrative taxonomic revision of three Camellia species from section Tuberculata (Camellia, Theaceae) by morphological, anatomical, palynological, and molecular evidence.Botanical studies · 2026Article
- Review
- SVEA: an accurate model for structural variation detection using multi-channel image encoding and enhanced AlexNet architecture.Journal of translational medicine · 2025Article
- Comparative population pangenomes reveal unexpected complexity and fitness effects of structural variants.bioRxiv : the preprint server for biology · 2025Article
- Unlocking precision medicine: clinical applications of integrating health records, genetics, and immunology through artificial intelligence.Journal of biomedical science · 2025Review
- FindCSV: a long-read based method for detecting complex structural variations.BMC bioinformatics · 2024Article
- CSV-Filter: a deep learning-based comprehensive structural variant filtering method for both short and long reads.Bioinformatics (Oxford, England) · 2024Article
- LcDel: deletion variation detection based on clustering and long reads.Frontiers in genetics · 2024Article
- invMap: a sensitive mapping tool for long noisy reads with inversion structural variants.Bioinformatics (Oxford, England) · 2023Article
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2 authors.
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Abstract
backgroundStructural variations (SVs) refer to variations in an organism's chromosome structure that exceed a length of 50 base pairs. They play a significant role in genetic diseases and evolutionary mechanisms. While long-read sequencing technology has led to the development of numerous SV caller methods, their performance results have been suboptimal. Researchers have observed that current SV callers often miss true SVs and generate many false SVs, especially in repetitive regions and areas with multi-allelic SVs. These errors are due to the messy alignments of long-read data, which are affected by their high error rate. Therefore, there is a need for a more accurate SV caller method.
resultWe propose a new method-SVcnn, a more accurate deep learning-based method for detecting SVs by using long-read sequencing data. We run SVcnn and other SV callers in three real datasets and find that SVcnn improves the F1-score by 2-8% compared with the second-best method when the read depth is greater than 5×. More importantly, SVcnn has better performance for detecting multi-allelic SVs.
conclusionsSVcnn is an accurate deep learning-based method to detect SVs. The program is available at https://github.com/nwpuzhengyan/SVcnn .
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