Evidence map›Paper›PMID 40605045›Full record

ArticleBMC research notes2025

Genotyping from targeted NGS data based on a small set of SNPs correctly matches patient samples.

Deyan Yordanov Yosifov, Christof Schneider, Stephan Stilgenbauer, Daniel Mertens, Eugen Tausch

Abstract read
In one paragraph

Article in BMC research notes, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. IGLV3-21HemaSphere · 2026
    Article
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

5 authors.

Deyan Yordanov YosifovDivision of CLL, Department of Internal Medicine III, Ulm University Hospital, Ulm, Germany. deyan.yosifov@uniklinik-ulm.de.
Christof SchneiderDivision of CLL, Department of Internal Medicine III, Ulm University Hospital, Ulm, Germany.
Stephan StilgenbauerDivision of CLL, Department of Internal Medicine III, Ulm University Hospital, Ulm, Germany.
Daniel MertensDivision of CLL, Department of Internal Medicine III, Ulm University Hospital, Ulm, Germany.
Eugen TauschDivision of CLL, Department of Internal Medicine III, Ulm University Hospital, Ulm, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveMislabelling and swapping of laboratory samples are handling errors that can lead to erroneous interpretation of data and/or patient harm. Sequenced samples can be traced back to the respective donors by matching of single nucleotide polymorphisms (SNPs). Frameworks and software to do this have been developed for use with whole genome/exome sequencing data but not for targeted next-generation sequencing (tNGS), possibly due to the limited genomic coverage with tNGS and the need for individualization of the set of interrogated SNPs. We decided to adapt a popular tool for use with tNGS data, to demonstrate the possibility of selecting informative SNPs from a typical tNGS panel and to create an automated workflow for detection of sample handling errors.

resultsWe compiled a custom list of 28 SNPs and with its help we demonstrated the practicability of using only tNGS data to cost-effectively detect mislabelled samples. In two cohorts of totally 1441 patients with sequential samples, we could identify 3 sample swaps, 7 mislabelled samples (3 externally and 4 internally) and 1 mistake of unknown origin. We provide an R function for automated detection of sample swaps and mislabelling to the community as a free and open-source tool.

Indexed as

Genotyping TechniquesHigh-Throughput Nucleotide SequencingPolymorphism, Single NucleotideGenotypeHumansSequence Analysis, DNASoftwareGenotypingMaftoolsRSample misidentificationSample mislabellingSample swapsSingle nucleotide polymorphisms (SNPs)Targeted NGS

Identifiers

PMID40605045
PMCPMC12225085

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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.