cuteFC: regenotyping structural variants through an accurate and efficient force-calling method.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Long-read sequencing technologies offer comprehensive structural variation (SV) discovery.
• Accurate SV genotype assignment is hindered by sequencing errors, low coverage, and SV complexity.

Purpose of the Study:

• To introduce cuteFC, a novel computational tool for accurate SV regenotyping.
• To improve the efficiency and quality of genomic atlas construction using long-read sequencing data.

Main Methods:

• cuteFC utilizes self-adaptive and multiallele-aware clustering for SV regenotyping.
• A force-calling approach and Genome Position Scanner algorithm enhance accuracy and efficiency.

Main Results:

• Benchmarking shows cuteFC outperforms existing state-of-the-art methods.
• Achieved 2-5% higher F1 scores compared to other methods.
• Constructed a higher-quality genomic atlas with reduced computational resources.

Conclusions:

• cuteFC provides a robust solution for accurate SV regenotyping from long-read sequencing data.
• The tool enhances the construction of high-quality genomic atlases.
• cuteFC offers improved accuracy and efficiency for genomic variation analysis.