Comprehensive analysis to improve the validation rate for single nucleotide variants detected by next-generation...

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Next-generation sequencing (NGS) facilitates high-throughput mutation discovery but faces challenges with variant detection accuracy.
• Inaccurate variant calls in NGS data can arise from various technical factors.
• Sanger sequencing remains a gold standard for variant validation.

Purpose of the Study:

• To systematically evaluate parameters influencing variant call accuracy in NGS data.
• To identify key metrics for improving the validation rate of single nucleotide variants (SNVs).
• To provide actionable recommendations for optimizing NGS variant detection and validation.

Main Methods:

• Categorization of NGS-detected variants based on total read depth (TD) and SNP quality (SNPQ).
• Validation of 348 non-synonymous SNVs using Sanger sequencing.
• Comparative analysis of variant calls from SAMtools and GATK algorithms.
• Assessment of parameters including strand bias (SB), mapping quality (MQ), and allele frequency (AF).

Main Results:

• Validation rate positively correlated with SNPQ, but not TD.
• Common variants identified by both SAMtools and GATK showed higher validation rates.
• Strand bias (SB) emerged as a critical parameter, differentiating validated from failed variants (92% validation rate with specific cutoffs).
• Combined filtering using MQ, SNPQ, and SB significantly increased validation rates to 97-99%.

Conclusions:

• SNP quality and strand bias are crucial for accurate NGS variant detection.
• Optimized filtering strategies using multiple parameters can substantially enhance variant validation rates.
• The study offers practical guidelines to improve the reliability and efficiency of NGS analyses, reducing costs and saving time.