Evidence map›Paper›PMID 41570118›Full record

ArticleBioinformatics (Oxford, England)2026

Application of qualifying variants for genomic analysis.

Dylan Lawless, Ali Saadat, Mariam Ait Oumelloul, Simon Boutry, Veronika Stadler, Sabine Österle, Jan Armida, David Haerry, D Sean Froese, Luregn J Schlapbach and 1 more

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2026. 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

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Dylan LawlessDepartment of Intensive Care and Neonatology, University Children's Hospital Zürich, University of Zürich, Zurich, 8008, Switzerland.ORCID 0000-0001-8496-3725
Ali SaadatGlobal Health Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, 1015, Switzerland.
Mariam Ait OumelloulGlobal Health Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, 1015, Switzerland.
Simon BoutryGlobal Health Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, 1015, Switzerland.ORCID 0000-0002-3214-2981
Veronika StadlerDepartment of Intensive Care and Neonatology, University Children's Hospital Zürich, University of Zürich, Zurich, 8008, Switzerland.
Sabine ÖsterleSwiss Personalized Health Network, SIB Swiss Institute of Bioinformatics, Basel, 4051, Switzerland.
Jan ArmidaSwiss Personalized Health Network, SIB Swiss Institute of Bioinformatics, Basel, 4051, Switzerland.
David HaerryPositive Council, Zurich, 8031, Switzerland.
D Sean FroeseDivision of Metabolism and Children's Research Center, University Children's Hospital Zürich, University of Zurich, Zurich, 8008, Switzerland.ORCID 0000-0003-1557-3517
Luregn J SchlapbachDepartment of Intensive Care and Neonatology, University Children's Hospital Zürich, University of Zürich, Zurich, 8008, Switzerland.
Jacques FellayGlobal Health Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, 1015, Switzerland.ORCID 0000-0002-8240-939X

Funding

Related Technologies" of the ETH Domain (Swiss Federal Institutes of Technology)Strategic Focal Area "Personalized HealthSwiss National Science Foundation 320030_201060/1Swiss National Science Foundation NDS-2021-911Swiss Personalized Health Network
6 · The paper itself

Abstract

motivationQualifying variants (QVs) are genomic alterations selected by defined criteria within analysis pipelines. Although crucial for both research and clinical diagnostics, QVs are often seen as simple filters rather than dynamic elements that influence the entire workflow. In practice these rules are embedded within pipelines, which hinders transparency, audit, and reuse across tools. A unified, portable specification for QV criteria is needed.

resultsOur aim is to embed the concept of a "QV" into the genomic analysis vernacular, moving beyond its treatment as a single filtering step. By decoupling QV criteria from pipeline variables and code, the framework enables clearer discussion, application, and reuse. It provides a flexible reference model for integrating QVs into analysis pipelines, improving reproducibility, interpretability, and interdisciplinary communication. Validation across diverse applications confirmed that QV based workflows match conventional methods while offering greater clarity and scalability. AVAILABILITY AND IMPLEMENTATION: The source code and data are accessible at the Zenodo repository https://doi.org/10.5281/zenodo.17414191. Manuscript files are available at https://github.com/DylanLawless/qvApp2025lawless. The QV framework is available under the MIT licence, and the dataset will be maintained for at least two years following publication.

Indexed as

Genetic VariationGenomicsSoftwareHumans

Identifiers

PMID41570118
PMCPMC12926777

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.