Evaluating nanopore sequencing data processing pipelines for structural variation identification.

Area of Science:

• Genomics
• Bioinformatics

Background:

• Structural variations (SVs) constitute ~1% of human genomic differences, influencing phenotypic variation and disease susceptibility.
• Nanopore sequencing offers long reads for potential accurate SV identification, but alignment and detection tools require thorough evaluation.

Purpose of the Study:

• To evaluate the performance of alignment and structural variation (SV) detection tools using nanopore sequencing data.
• To assess the impact of sequencing depth on SV detection.
• To develop a machine learning approach for integrating SV call sets from multiple pipelines.

Main Methods:

• Evaluation of four alignment tools and three SV detection tools on four nanopore datasets (empirical and simulated).
• Assessment of sequencing depth's influence on SV detection accuracy.
• Development of a machine learning model to integrate SV call sets.

Main Results:

• Performance of SV callers varies by SV type.
• Minimap2 aligner with Sniffles SV caller recommended for initial assessment due to speed and balanced performance.
• Integrating multiple call sets enhances SV call performance.

Conclusions:

• A workflow for evaluating nanopore SV detection tools and integrating call sets was presented.
• Further optimizations are needed to improve SV detection accuracy and sensitivity.
• Nanopore technology advancements and growing community will yield better benchmark datasets for tool development.