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

Jonas A Gustafson1,2, Jiadong Lin3, Evan E Eichler3,4,5

  • 1Department of Molecular and Cellular Biology, University of Washington, Seattle, WA 98195, USA.

Arxiv
|December 19, 2025
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Summary
This summary is machine-generated.

We developed needLR, a tool to filter and prioritize structural variants (SVs) from long-read sequencing. It effectively uses population data to reduce candidate SVs while retaining pathogenic ones.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Long-read sequencing technologies enable comprehensive structural variant (SV) detection.
  • Accurate annotation and prioritization of pathogenic SVs are crucial for diagnosing genetic disorders.
  • Existing tools often struggle with the scale and complexity of SV data from long reads.

Purpose of the Study:

  • To introduce needLR, a novel computational tool for annotating and prioritizing structural variants (SVs) identified through long-read sequencing.
  • To leverage population allele frequencies, genomic context, and gene-phenotype associations for SV filtering.
  • To enhance the efficiency of identifying candidate pathogenic SVs in clinical and research settings.

Main Methods:

  • Developed needLR, an SV annotation tool integrating population allele frequencies, genomic annotations, and gene-phenotype data.
  • Utilized population data from 500 healthy individuals for variant frequency analysis.
  • Evaluated needLR performance on nine test cases containing known pathogenic SVs.

Main Results:

  • needLR successfully assigned allele frequencies to over 97.5% of all detected SVs across test cases.
  • The tool significantly reduced the average number of novel genic SVs to 121 per case.
  • All known pathogenic SVs were successfully retained within the filtered set, demonstrating high sensitivity.

Conclusions:

  • needLR provides an effective method for filtering and prioritizing structural variants from long-read sequencing data.
  • The tool's ability to integrate diverse data sources improves the accuracy and efficiency of identifying disease-causing SVs.
  • needLR represents a valuable advancement for genomic analysis in both research and clinical diagnostics.