Evidence map›Paper›PMID 41415608›Full record

ArticleArXiv2025

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

Jonas A Gustafson, Jiadong Lin, Evan E Eichler, Danny E Miller

Abstract readPreprint
PubMed Full text at PMC
In one paragraph

Article in ArXiv, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Jonas A GustafsonDepartment of Molecular and Cellular Biology, University of Washington, Seattle, WA 98195, USA.
Jiadong LinDepartment of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA.
Evan E EichlerDepartment of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA.
Danny E MillerDepartment of Pediatrics, University of Washington, Seattle, WA 98195, USA.

Funding

Sequence-resolved structural variation of human genomesR01HG010169 · NHGRI · UNIVERSITY OF WASHINGTON · PI Evan Eichler · 2018 to 2026
$4.5M
Long-read DNA and RNA sequencing to identify disease-causing genetic variation and streamline testingDP5OD033357 · OD · UNIVERSITY OF WASHINGTON · PI MILLER, DANNY ERWIN · 2022 to 2025
$1.9M
NHGRI NIH HHS R01 HG010169NIH HHS DP5 OD033357
6 · The paper itself

Abstract

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.

Identifiers

PMID41415608
PMCPMC12709490

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.