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.
@article{arxiv.2512.08175,
title = {needLR: Long-read structural variant annotation with population-scale frequency estimation},
author = {Jonas A. Gustafson and Jiadong Lin and Evan E. Eichler and Danny E. Miller},
journal= {arXiv preprint arXiv:2512.08175},
year = {2025}
}