Systematic comparison of germline variant calling pipelines cross multiple next-generation sequencers

Area of Science:

• Genomics and Bioinformatics
• Next-Generation Sequencing (NGS) Technologies
• Clinical Diagnostics

Background:

• Next-generation sequencing (NGS) and its analysis tools are crucial for scientific research and clinical diagnostics.
• A systematic comparison of NGS platforms and variant calling pipelines is needed to guide genomic applications.
• Evaluating performance, concordance, and efficiency is essential for reliable variant identification.

Purpose of the Study:

• To compare the performance, concordance, and operating efficiency of 27 combinations of sequencing platforms and variant calling pipelines.
• To assess variant calling accuracy for Single Nucleotide Polymorphisms (SNPs) and Insertions/Deletions (INDELs) across different platforms and pipelines.
• To evaluate the impact of sequencing coverage on variant calling efficiency and identify optimal pipelines.

Main Methods:

• Compared three variant calling pipelines (Genome Analysis Tool Kit HaplotypeCaller, Strelka2, Samtools-Varscan2) across nine datasets for the NA12878 genome.
• Utilized BGISEQ500, MGISEQ2000, HiSeq4000, NovaSeq, and HiSeq Xten sequencing platforms.
• Down-sampled Whole Exome Sequencing (WES) and Whole Genome Sequencing (WGS) datasets to assess performance at varying coverage levels.

Main Results:

• All combinations showed good SNP calling performance (F-scores > 0.96 for WES, > 0.975 for WGS).
• INDEL calling performance varied (0.75-0.91 for WES, 0.71-0.93 for WGS), with high overall concordance.
• Strelka2 exhibited superior detection accuracy and processing efficiency on both BGI and Illumina platforms, especially at lower coverages.

Conclusions:

• The study provides comprehensive guidelines for reliable and consistent variant identification using NGS technologies.
• Strelka2 is recommended for promotion and application due to its high accuracy and efficiency.
• Understanding platform and pipeline performance is critical for optimizing NGS-based genomic research and clinical diagnostics.