Regenotyping structural variants through an accurate force-calling method

Abstract

Long-read sequencing technologies have great potential for the comprehensive discovery of structural variation (SV). However, the accurate genotype assignment for SV is still a challenge since the unavoidable factors like specific sequencing errors or limited coverages. Herein, we propose cuteSV2, a fast and accurate long-read-based re-genotyping approach that is able to force-calling genotypes for the given records. cuteSV2 is an upgraded version of cuteSV and applies an improved strategy of the refinement and purification of the heuristic extracted signatures through spatial and allele similarity estimation. The benchmarking results on several baseline evaluations demonstrate that cuteSV2 outperforms the-state-of-art methods and has stable and robust capability in practice.

SVDF: enhancing structural variation detect from long-read sequencing via automatic filtering strategies

“…To comprehensively evaluate the SVDF performance, we first conducted tests on samples simulated using SURVIVOR [ 26 ] and PBSIM2 [ 27 ] (the implementation of simulate see Methods section). The alignment files of the simulated samples were inputted into the three latest SV calling tools, SVIM, Sniffles2 [ 28 ], and cuteSV2 [ 29 ], for SV detection. The evaluation results from SUVIVOR demonstrate that amongst the five SV types in PacBio and Nanopore simulated data, SVDF achieved the highest F1 scores compared to the other three calling tools ( Fig.…”

Kled: an ultra-fast and sensitive structural variant detection tool for long-read sequencing data

“…For example, Sniffles [ 28 , 29 ] identifies potential SV-supporting alignments based on statistics and clusters them based on various factors. CuteSV [ 30 , 31 ] collects signatures from the aligned sequencing data and clusters them based on a defined distance metric. SVIM [ 32 ] collects signatures within and among alignments and performs graph clustering to identify maximum cliques as clusters.…”