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Updated: Jun 21, 2025
Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
Published on: June 23, 2012
Comprehensive and deep evaluation of structural variation detection pipelines with third-generation sequencing data
Zhi Liu1,2, Zhi Xie3, Miaoxin Li4,5,6,7,8
1Program in Bioinformatics, Zhongshan School of Medicine, The Fifth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
Background:
Structural variation (SV) detection methods using third-generation sequencing data are widely employed, yet accurately detecting SVs remains challenging. Different methods often yield inconsistent results for certain SV types, complicating tool selection and revealing biases in detection.
Results:
This study comprehensively evaluates 53 SV detection pipelines using simulated and real data from PacBio (CLR: Continuous Long Read, CCS: Circular Consensus Sequencing) and Nanopore (ONT) platforms. We assess their performance in detecting various sizes and types of SVs, breakpoint biases, and genotyping accuracy with various sequencing depths. Notably, pipelines such as Minimap2-cuteSV2, NGMLR-SVIM, PBMM2-pbsv, Winnowmap-Sniffles2, and Winnowmap-SVision exhibit comparatively higher recall and precision. Our findings also show that combining multiple pipelines with the same aligner, like pbmm2 or winnowmap, can significantly enhance performance. The individual pipelines' detailed ranking and performance metrics can be viewed in a dynamic table: http://pmglab.top/SVPipelinesRanking .
Conclusions:
This study comprehensively characterizes the strengths and weaknesses of numerous pipelines, providing valuable insights that can improve SV detection in third-generation sequencing data and inform SV annotation and function prediction.
