Evidence map›Paper›PMID 41639627›Full record

ArticleBMC bioinformatics2026

Virus variant quantification with Orthanq.

Hamdiye Uzuner, Felix Wiegand, Sven Schrinner, David Lähnemann, Dirk Schadendorf, Johannes Köster

Abstract read
In one paragraph

Article in BMC bioinformatics, 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
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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

6 authors.

Hamdiye UzunerBioinformatics and Computational Oncology, Institute for Artifical Intelligence in Medicine (IKIM), Faculty of Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany. hamdiye.uzuner@uni-due.de.
Felix WiegandBioinformatics and Computational Oncology, Institute for Artifical Intelligence in Medicine (IKIM), Faculty of Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany.
Sven SchrinnerBioinformatics and Computational Oncology, Institute for Artifical Intelligence in Medicine (IKIM), Faculty of Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany.
David Lähnemann *Bioinformatics and Computational Oncology, Institute for Artifical Intelligence in Medicine (IKIM), Faculty of Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany.
Dirk SchadendorfDepartment of Dermatology, University Hospital Essen, West German Cancer Center, University Duisburg-Essen, Essen, Germany.
Johannes Köster *Bioinformatics and Computational Oncology, Institute for Artifical Intelligence in Medicine (IKIM), Faculty of Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundExisting tools for virus variant identification can pinpoint the most abundant virus variant in a sequencing sample. However, patients can be infected by more than one variant of the same virus species or strain, for example by multiple variants of SARS-CoV-2. This leads to the more complicated problem of virus variant quantification from samples containing virus mixtures.

resultsWe report on improvements of Orthanq, our generic tool for haplotype quantification, and show how it can be applied to perform uncertainty aware quantification of virus variants. We evaluate this ability on simulated and real SARS-CoV-2 and HIV-1 virus mixture datasets and show that Orthanq outperforms other state of the art approaches.

conclusionsOrthanq performs identification and uncertainty-aware quantification of known virus variants of any virus species, in particular also in samples with mixed infections. With extensive built-in visualizations and reporting of alternative solutions with posterior densities, users can easily evaluate the uncertainty of the results.

Indexed as

HIV-1SoftwareCOVID-19Genetic VariationHaplotypesHumansSARS-CoV-2Co-infectionHaplotype quantificationSARS-CoV-2Uncertainty-computationVirus lineage quantification

Identifiers

PMID41639627
PMCPMC12930645

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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.