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Long-read sequencing technologies have great potential for the comprehensive discovery of structural variations (SVs). However, accurate genotype assignment for SVs remains challenging due to unavoidable sequencing errors, limited coverage, and the complexity of SVs. Herein, we propose cuteFC, which employs self-adaptive clustering along with a multiallele-aware clustering to achieve accurate SV regenotyping through a force-calling approach. cuteFC also applies a Genome Position Scanner algorithm to improve its application efficiency. Benchmarking evaluations demonstrate that cuteFC outperforms state-of-the-art methods with 2-5% higher F1 scores and constructs a higher-quality genomic atlas with minimal computational resources. cuteFC is available at https://github.com/Meltpinkg/cuteFC and https://zenodo.org/records/14671406 .
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12164092 | PMC |
http://dx.doi.org/10.1186/s13059-025-03642-2 | DOI Listing |
Genome Biol
June 2025
Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China.
Long-read sequencing technologies have great potential for the comprehensive discovery of structural variations (SVs). However, accurate genotype assignment for SVs remains challenging due to unavoidable sequencing errors, limited coverage, and the complexity of SVs. Herein, we propose cuteFC, which employs self-adaptive clustering along with a multiallele-aware clustering to achieve accurate SV regenotyping through a force-calling approach.
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