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Updated: Dec 30, 2025
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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
Published on: June 23, 2012
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Comparison of multiple algorithms to reliably detect structural variants in pears.
Yueyuan Liu1, Mingyue Zhang1, Jieying Sun1
1Center of Pear Engineering Technology Research, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, China.
BMC Genomics
|January 22, 2020
Summary
Combining multiple structural variation (SV) detection tools improves accuracy. Long-read sequencing is superior to next-generation sequencing (NGS) for SV detection in pear genomes.
Area of Science:
- Genomics
- Bioinformatics
- Plant Science
Background:
- Structural variations (SVs) are crucial for genetic diversity and trait regulation.
- Numerous computational algorithms for SV detection exist, but their combined use for high-confidence detection remains understudied.
- Optimal sequencing depth for SV detection in pear is unknown.
Purpose of the Study:
- To construct a pipeline for SV detection using both next-generation sequencing (NGS) and long-read sequencing data.
- To compare the performance of various SV detection software packages.
- To determine the optimal sequencing depth for SV detection in the pear genome.
Main Methods:
- Evaluated nine SV detection software packages: seven using NGS data and two (SVIM, Sniffles) using long-read data.
- Compared software performance based on SV identification counts, accuracy, computational resource usage, and speed.
- Assessed the impact of combining results from multiple SV detection tools.
- Determined appropriate sequencing depth for SV detection using assembly-based algorithms with NGS data.
Main Results:
- SVIM identified the most SVs, while Sniffles demonstrated the highest accuracy (>90%) among individual long-read tools.
- Combining NGS-based tools (MetaSV, IMR/DENOM) yielded higher accuracy (98.7%) than individual long-read tools.
- Long-read sequencing software required fewer computational resources and ran faster than NGS-based software.
- A sequencing depth of 50× was found to be appropriate for SV detection in the pear genome using NGS data.
Conclusions:
- Using multiple SV detection software packages with different algorithms enhances detection confidence.
- Long-read sequencing data is preferable to NGS data for SV detection.
- The developed SV detection pipeline can aid in studying crop diversity.
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