needLR: long-read structural variant annotation with population-scale frequency estimation.

Jonas A Gustafson; Jiadong Lin; Miranda P G Zalusky; Evan E Eichler; Danny E Miller

doi:10.1093/bioinformatics/btag407

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needLR: long-read structural variant annotation with population-scale frequency estimation.

Jonas A Gustafson·Jiadong Lin·Miranda P G Zalusky·Evan E Eichler·Danny E Miller

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Abstract

SUMMARY: We present needLR, a structural variant (SV) annotation tool that can be used for filtering and prioritization of candidate pathogenic SVs from long-read sequencing data using population allele frequencies, annotations for genomic context, and gene-phenotype associations. When using population data from 500 presumably healthy individuals to evaluate nine test cases with known pathogenic SVs, needLR assigned allele frequencies to over 97.5% of all detected SVs and reduced the average number of novel genic SVs to 121 per case while retaining all known pathogenic variants. AVAILABILITY AND IMPLEMENTATION: needLR is implemented in bash with dependencies including Truvari v4.2.2, BEDTools v2.31.1, and BCFtools v1.19. Source code, documentation, and pre-computed population allele frequency data are freely available at https://github.com/jgust1/needLR under an MIT license and archived on Zenodo at https://zenodo.org/records/19463479.

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Software Humans Molecular Sequence Annotation Gene Frequency Genomic Structural Variation Genomics High-Throughput Nucleotide Sequencing Sequence Analysis, DNA Journal Article Research Support, N.I.H., Extramural

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Jonas A Gustafson, Jiadong Lin, Miranda P G Zalusky, Evan E Eichler, Danny E Miller. 2026-07-02. needLR: long-read structural variant annotation with population-scale frequency estimation.. https://doi.org/10.1093/bioinformatics%2Fbtag407

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