ArticleBriefings in bioinformatics2024
Kled: an ultra-fast and sensitive structural variant detection tool for long-read sequencing data.
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 11 papers.
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Who cites it
11 citing papers in PubMed.
- Article
- cuteHap: Haplotype-Aware Structural Variant Detection in Phased Long-Read Sequencing Data.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Identification of transgene insertion sites via short- and long-read whole genome sequencing.bioRxiv : the preprint server for biology · 2026Article
- MRDadaptis: self-adaptive parameter configuration enhances minimal residual disease detection in heterogeneous ctDNA samples.Briefings in bioinformatics · 2025Article
- cuteFC: regenotyping structural variants through an accurate and efficient force-calling method.Genome biology · 2025Article
- SVHunter: long-read-based structural variation detection through the transformer model.Briefings in bioinformatics · 2025Article
- cfMethylPre: deep transfer learning enhances cancer detection based on circulating cell-free DNA methylation profiling.Briefings in bioinformatics · 2025Article
- Comparisons of performances of structural variants detection algorithms in solitary or combination strategy.PloS one · 2025Article
- Comprehensive and deep evaluation of structural variation detection pipelines with third-generation sequencing data.Genome biology · 2024Article
- SVDF: enhancing structural variation detect from long-read sequencing via automatic filtering strategies.Briefings in bioinformatics · 2024Article
- HapKled: a haplotype-aware structural variant calling approach for Oxford nanopore sequencing data.Frontiers in genetics · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Structural Variants (SVs) are a crucial type of genetic variant that can significantly impact phenotypes. Therefore, the identification of SVs is an essential part of modern genomic analysis. In this article, we present kled, an ultra-fast and sensitive SV caller for long-read sequencing data given the specially designed approach with a novel signature-merging algorithm, custom refinement strategies and a high-performance program structure. The evaluation results demonstrate that kled can achieve optimal SV calling compared to several state-of-the-art methods on simulated and real long-read data for different platforms and sequencing depths. Furthermore, kled excels at rapid SV calling and can efficiently utilize multiple Central Processing Unit (CPU) cores while maintaining low memory usage. The source code for kled can be obtained from https://github.com/CoREse/kled.
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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 OpenQuestion graph.