ArticleBioinformatics (Oxford, England)2024
CSV-Filter: a deep learning-based comprehensive structural variant filtering method for both short and long reads.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Dual-SVF: a robust knowledge-guided multimodal method for structural variant filtering in long-read sequencing.Briefings in bioinformatics · 2026Article
- Molecular pharmacodynamics of amoxicillin-clavulanic acid for urinary tract infections caused by Escherichia coli.Nature communications · 2026Article
- A systematic assessment of machine learning for structural variant filtering.bioRxiv : the preprint server for biology · 2026Article
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9 authors.
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Abstract
motivationStructural variants (SVs) play an important role in genetic research and precision medicine. As existing SV detection methods usually contain a substantial number of false positive calls, approaches to filter the detection results are needed.
resultsWe developed a novel deep learning-based SV filtering tool, CSV-Filter, for both short and long reads. CSV-Filter uses a novel multi-level grayscale image encoding method based on CIGAR strings of the alignment results and employs image augmentation techniques to improve SV feature extraction. CSV-Filter also utilizes self-supervised learning networks for transfer as classification models, and employs mixed-precision operations to accelerate training. The experiments showed that the integration of CSV-Filter with popular SV detection tools could considerably reduce false positive SVs for short and long reads, while maintaining true positive SVs almost unchanged. Compared with DeepSVFilter, a SV filtering tool for short reads, CSV-Filter could recognize more false positive calls and support long reads as an additional feature. AVAILABILITY AND IMPLEMENTATION: https://github.com/xzyschumacher/CSV-Filter.
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