Long-read sequencing settings for efficient structural variation detection based on comprehensive evaluation.

Area of Science:

• Genomics
• Bioinformatics

Background:

• Long-read sequencing technologies enable comprehensive detection of genetic structural variations (SVs).
• High costs, limited read lengths, and elevated error rates hinder widespread adoption of SV calling.
• Establishing optimal sequencing parameters is critical for maximizing SV yields while minimizing expenses.

Purpose of the Study:

• To evaluate the impact of various sequencing settings on structural variation (SV) calling performance using long-read data.
• To provide evidence-based guidelines for selecting optimal long-read sequencing parameters for efficient and cost-effective SV detection.

Main Methods:

• Generation of simulated long-read datasets with diverse sequencing parameters (coverage, read length, error rate).
• Comprehensive performance evaluation of state-of-the-art SV callers on these simulated datasets.
• Benchmarking SV calling accuracy across different sequencing settings.

Main Results:

• Optimal SV calling performance is achieved with approximately 20× coverage, 20 kbp average read length, and error rates of 7.5% or lower.
• Sequencing coverage demonstrates the most significant positive impact on SV calling accuracy.
• Increased sequencing coverage directly correlates with higher overall costs.

Conclusions:

• Recommended long-read sequencing settings (20× coverage, 20 kbp reads, ≤1% error rate) enhance SV calling efficiency.
• These guidelines offer significant value for genomic studies and clinical applications.
• Balancing sequencing parameters is key to achieving high-yield, cost-effective structural variation detection.