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Updated: Jun 21, 2025

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

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 Liu^1,2, Zhi Xie³, Miaoxin Li^4,5,6,7,8

¹Program in Bioinformatics, Zhongshan School of Medicine, The Fifth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.

|July 15, 2024

Summary

This summary is machine-generated.

This study evaluated 53 structural variation (SV) detection pipelines for third-generation sequencing. Top-performing pipelines like Minimap2-cuteSV2 and NGMLR-SVIM were identified, with pipeline combinations enhancing accuracy.

Keywords:

Long-reads Pipeline evaluation SV caller Sequence aligner Structural variation Third-generation sequencing

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Last Updated: Jun 21, 2025

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Published on: June 23, 2012

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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

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Published on: April 4, 2018

Area of Science:

Genomics
Bioinformatics

Background:

Accurate structural variation (SV) detection is crucial but challenging with third-generation sequencing (TGS) data.
Existing SV detection methods show inconsistencies and biases, complicating tool selection.

Purpose of the Study:

To comprehensively evaluate and compare the performance of 53 SV detection pipelines using TGS data.
To identify optimal pipelines and strategies for improving SV detection accuracy and reliability.

Main Methods:

Evaluation of 53 SV detection pipelines on simulated and real PacBio (CLR, CCS) and Nanopore (ONT) data.
Assessment of performance metrics including recall, precision, breakpoint accuracy, and genotyping accuracy across various SV types and sizes.
Analysis of sequencing depth impact on SV detection.

Main Results:

Pipelines such as Minimap2-cuteSV2, NGMLR-SVIM, PBMM2-pbsv, Winnowmap-Sniffles2, and Winnowmap-SVision demonstrated superior recall and precision.
Combining multiple pipelines, particularly those sharing an aligner (e.g., pbmm2, winnowmap), significantly improved detection performance.
Detailed performance rankings and metrics are available at http://pmglab.top/SVPipelinesRanking.

Conclusions:

This study provides a thorough characterization of SV detection pipeline strengths and weaknesses for TGS data.
The findings offer valuable insights for selecting appropriate tools and improving SV detection, annotation, and function prediction.