Performance evaluation of structural variation detection using DNBSEQ whole-genome sequencing.

Area of Science:

• Genomics
• Bioinformatics
• Next-Generation Sequencing

Background:

• DNBSEQ platforms are utilized for variant detection, comparable to Illumina for single-nucleotide variants (SNVs) and short insertions/deletions (INDELs).
• The performance and characteristics of structural variation (SV) detection using DNBSEQ platforms remain underexplored.

Purpose of the Study:

• To systematically assess and characterize germline SVs detected on whole-genome sequencing (WGS) datasets generated by DNBSEQ platforms.
• To compare the SV detection performance between DNBSEQ and Illumina platforms.

Main Methods:

• Evaluation of 40 SV detection tools across eight DNBSEQ and two Illumina WGS datasets from NA12878.
• Comparative analysis of SV detection metrics (number, size, precision, sensitivity) between platforms.
• Construction and comparison of DNBSEQ and Illumina SV sets, analyzing genomic characteristics.

Main Results:

• High consistency (correlation > 0.80) in SV detection performance between DNBSEQ and Illumina platforms when using the same tools.
• Construction of distinct SV sets from each platform (4,785 for DNBSEQ, 6,797 for Illumina).
• Significant concordance between DNBSEQ and Illumina SV sets regarding genomic features like repetitive regions, GC content, and gene elements.

Conclusions:

• DNBSEQ platforms demonstrate robust and consistent performance for SV detection, comparable to Illumina.
• The study establishes a valuable benchmark resource for germline SV analysis using DNBSEQ WGS data.
• Findings support the utility of both DNBSEQ and Illumina platforms for comprehensive genomic variation studies.