ArticleFrontiers in genetics2021
PanSVR: Pan-Genome Augmented Short Read Realignment for Sensitive Detection of Structural Variations.
Article in Frontiers in genetics, 2021. 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.
- SVPG: a pangenome-based structural variant detection approach and rapid augmentation of pangenome graphs with new samples.Nature methods · 2026Article
- Comparisons of performances of structural variants detection algorithms in solitary or combination strategy.PloS one · 2025Article
- Kled: an ultra-fast and sensitive structural variant detection tool for long-read sequencing data.Briefings in bioinformatics · 2024Article
- HapKled: a haplotype-aware structural variant calling approach for Oxford nanopore sequencing data.Frontiers in genetics · 2024Article
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Authors and funding
4 authors.
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Abstract
The comprehensive discovery of structure variations (SVs) is fundamental to many genomics studies and high-throughput sequencing has become a common approach to this task. However, due the limited length, it is still non-trivial to state-of-the-art tools to accurately align short reads and produce high-quality SV callsets. Pan-genome provides a novel and promising framework to short read-based SV calling since it enables to comprehensively integrate known variants to reduce the incompleteness and bias of single reference to breakthrough the bottlenecks of short read alignments and provide new evidences to the detection of SVs. However, it is still an open problem to develop effective computational approaches to fully take the advantage of pan-genomes. Herein, we propose Pan-genome augmented Structure Variation calling tool with read Re-alignment (PanSVR), a novel pan-genome-based SV calling approach. PanSVR uses several tailored methods to implement precise re-alignment for SV-spanning reads against well-organized pan-genome reference with plenty of known SVs. PanSVR enables to greatly improve the quality of short read alignments and produce clear and homogenous SV signatures which facilitate SV calling. Benchmark results on real sequencing data suggest that PanSVR is able to largely improve the sensitivity of SV calling than that of state-of-the-art SV callers, especially for the SVs from repeat-rich regions and/or novel insertions which are difficult to existing tools.
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