ArticleBriefings in bioinformatics2024
SVDF: enhancing structural variation detect from long-read sequencing via automatic filtering strategies.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Comprehensive evaluation of structural variation detection for germline and somatic analysis with long-read sequencing data.Briefings in bioinformatics · 2026Article
- MRDsteer: quality-aware AI-driven closed-loop optimization enhances ctDNA-based minimal residual disease detection.Briefings in bioinformatics · 2026Article
- MRDadaptis: self-adaptive parameter configuration enhances minimal residual disease detection in heterogeneous ctDNA samples.Briefings in bioinformatics · 2025Article
- Indel calling from ONT sequencing data of family trios via sparse attention and 3D convolution.Briefings in bioinformatics · 2025Article
- GKNnet: an relational graph convolutional network-based method with knowledge-augmented activation layer for microbial structural variation detection.Briefings in bioinformatics · 2025Article
- Comparisons of performances of structural variants detection algorithms in solitary or combination strategy.PloS one · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Structural variation (SV) is an important form of genomic variation that influences gene function and expression by altering the structure of the genome. Although long-read data have been proven to better characterize SVs, SVs detected from noisy long-read data still include a considerable portion of false-positive calls. To accurately detect SVs in long-read data, we present SVDF, a method that employs a learning-based noise filtering strategy and an SV signature-adaptive clustering algorithm, for effectively reducing the likelihood of false-positive events. Benchmarking results from multiple orthogonal experiments demonstrate that, across different sequencing platforms and depths, SVDF achieves higher calling accuracy for each sample compared to several existing general SV calling tools. We believe that, with its meticulous and sensitive SV detection capability, SVDF can bring new opportunities and advancements to cutting-edge genomic research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.