Evidence map›Paper›PMID 40248358›Full record

ArticleBioinformatics advances2025

Benchmarking of germline copy number variant callers from whole genome sequencing data for clinical applications.

Francisco M De La Vega, Sean A Irvine, Pavana Anur, Kelly Potts, Lewis Kraft, Raul Torres, Peter Kang, Sean Truong, Yeonghun Lee, Shunhua Han and 2 more

Abstract read
In one paragraph

Article in Bioinformatics advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

11 citing papers in PubMed.

  1. Article
  2. Review
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  7. Expanding the Genetic Landscape ofNeurology. Genetics · 2026
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Francisco M De La VegaTempus AI, Inc., Chicago, IL 60654, United States.ORCID https://orcid.org/0000-0002-9228-2097
Sean A IrvineReal Time Genomics, Ltd., Hamilton 3204, New Zealand.
Pavana AnurTempus AI, Inc., Chicago, IL 60654, United States.
Kelly PottsTempus AI, Inc., Chicago, IL 60654, United States.
Lewis KraftTempus AI, Inc., Chicago, IL 60654, United States.
Raul TorresTempus AI, Inc., Chicago, IL 60654, United States.
Peter KangTempus AI, Inc., Chicago, IL 60654, United States.
Sean Truongllumina, Inc., San Diego, CA 92122, United States.
Yeonghun Leellumina, Inc., San Diego, CA 92122, United States.
Shunhua Hanllumina, Inc., San Diego, CA 92122, United States.ORCID https://orcid.org/0000-0002-8605-7547
Vitor Onuchicllumina, Inc., San Diego, CA 92122, United States.
James Hanllumina, Inc., San Diego, CA 92122, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Motivation: Whole-genome sequencing (WGS) is increasingly preferred for clinical applications due to its comprehensive coverage, effectiveness in detecting copy number variants (CNVs), and declining costs. However, systematic evaluations of WGS CNV callers tailored to germline clinical testing-where high sensitivity and confirmation of reported CNVs are essential-remain necessary. Clinical reporting typically emphasizes CNVs affecting coding regions over precise breakpoint detection. This study benchmarks several short-read WGS CNV detection tools using reference cell lines to inform their clinical use. Results: While tools vary in sensitivity (7%-83%) and precision (1%-76%), few meet the sensitivity needed for clinical testing. Callers generally perform better for deletions (up to 88% sensitivity) than duplications (up to 47% sensitivity), with poor detection of duplications under 5 kb. Notably, for CNVs in genes commonly included in clinical panels, significantly improved sensitivity and precision were observed when benchmarking against 25 cell lines with known CNVs. DRAGEN v4.2 high-sensitivity CNV calls, post-processed with custom filters, achieved 100% sensitivity and 77% precision on the optimized gene panel after excluding recurring artifacts. This level of performance may support clinical use with orthogonal confirmation of reportable CNVs, pending validation on laboratory-specific samples. Availability and implementation: The data underlying this article are available in the European Nucleo-tide Archive under project accession PRJEB87628.

Identifiers

PMID40248358
PMCPMC12005901

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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.