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Updated: Jun 29, 2025
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Detection of Rare Mutations in CtDNA Using Next Generation Sequencing
Published on: August 24, 2017
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Comparison of structural variant callers for massive whole-genome sequence data.
Soobok Joe1, Jong-Lyul Park2,3, Jun Kim4
1Korea Bioinformation Center (KOBIC), Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, 34141, Republic of Korea.
BMC Genomics
|March 29, 2024
Summary
This study compared 11 structural variation (SV) callers for population genomics. Manta showed strong performance for deletions and insertions, aiding large-scale genomic data analysis.
Area of Science:
- Genomics
- Bioinformatics
- Population Genetics
Background:
- Detecting structural variations (SVs) in population-level next-generation sequencing (NGS) data is computationally intensive.
- Eleven recently published and widely used SV callers were evaluated for their performance.
Purpose of the Study:
- To compare the accuracy, computational resource usage, and efficiency of 11 different SV callers.
- To guide the selection of appropriate SV callers for large-scale genomic studies.
Main Methods:
- Comparative analysis of 11 SV callers: Delly, Manta, GridSS, Wham, Sniffles, Lumpy, SvABA, Canvas, CNVnator, MELT, and INSurVeyor.
- Evaluation metrics included accuracy, sequence depth, running time, and memory usage.
- Genotype concordance was verified using a phased long-read assembly dataset.
Main Results:
- Several callers performed better for deletions than other SV types.
- Manta demonstrated superior performance and efficiency for deletions and good precision for insertions.
- Canvas and CNVnator excelled at identifying long duplications using read-depth.
- Manta showed the highest genotype concordance for deletions and insertions.
Conclusions:
- The study provides a comprehensive assessment of SV caller accuracy and computational efficiency.
- Findings facilitate the integrative analysis of SV profiles across diverse large-scale genomic datasets.
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