Evidence map›Paper›PMID 41327433›Full record

ArticleGenome biology2025

Evaluation of false positive and false negative errors in targeted next generation sequencing.

Youngbeen Moon, Young-Ho Kim, Jong-Kwang Kim, Chung Hwan Hong, Eun-Kyung Kang, Hye Won Choi, Dong-Eun Lee, Tae-Min Kim, Seong Gu Heo, Namshik Han and 1 more

Abstract read
In one paragraph

Article in Genome biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Review
  3. The Pyrosequencing-Based Method forDiagnostics (Basel, Switzerland) · 2026
    Article
  4. Review
  5. Review
  6. Article
  7. 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

11 authors.

Youngbeen Moon *Bioinformatics Analysis Team, Research Core Center, Research Institute, National Cancer Center, Goyang, Gyeonggi‑do, Republic of Korea.
Young-Ho Kim *Division of Technology Convergence, Diagnostic and Therapeutics Technology Branch, Research Institute, National Cancer Center, Goyang, Gyeonggi‑do, Republic of Korea.
Jong-Kwang KimBioinformatics Analysis Team, Research Core Center, Research Institute, National Cancer Center, Goyang, Gyeonggi‑do, Republic of Korea.
Chung Hwan HongDivision of Cancer Biology, Cancer Molecular Biology Branch, Research Institute, National Cancer Center, Goyang, Gyeonggi‑do, Republic of Korea.
Eun-Kyung KangDivision of Cancer Biology, Cancer Molecular Biology Branch, Research Institute, National Cancer Center, Goyang, Gyeonggi‑do, Republic of Korea.
Hye Won ChoiDivision of Cancer Biology, Cancer Molecular Biology Branch, Research Institute, National Cancer Center, Goyang, Gyeonggi‑do, Republic of Korea.
Dong-Eun LeeBiostatistics Collaboration Team, Research Core Center, Research Institute, National Cancer Center, Goyang, Gyeonggi‑do, Republic of Korea.
Tae-Min KimDepartment of Medical Informatics and Cancer Research Institute, College of Medicine, The Catholic University of Korea, Seoul, Korea.
Seong Gu HeoDana Farber Cancer Institute, Boston, MA, USA.
Namshik HanMilner Therapeutics Institute, University of Cambridge, Cambridge, UK.
Kyeong-Man HongBioinformatics Analysis Team, Research Core Center, Research Institute, National Cancer Center, Goyang, Gyeonggi‑do, Republic of Korea. kmhong@ncc.re.kr.

Funding

the intramural program of the National Cancer Center, Korea 2311430
6 · The paper itself

Abstract

backgroundNext-generation sequencing (NGS) has become an indispensable diagnostic tool across various diseases. However, sequencing and analysis errors remain major barriers to clinical implementation. In cancer diagnostics, detecting low-level somatic variants is particularly challenging due to tumor heterogeneity and contamination from normal cells.

resultsWe assess targeted next-generation sequencing (T-NGS) performance using reference-standard DNA mixtures of homozygote hydatidiform mole and heterozygote blood DNA at varying ratios, analyzed by certified NGS providers. Analytical sensitivity differs by up to 13.9-fold, and false positive (FP) error rates vary up to 615-fold, depending on provider and pipeline. For identical raw data, DRAGEN and the in-house pipeline differ by up to 36.3-fold in FP error rates. Moderately recurrent FP-prone alleles, although representing only 5.37% of all FP sites, contribute to 36.7% of total FP errors in the Geninus in-house result. Among 22 discordant variant calls between DRAGEN and in-house analyses, more than half of them are not confirmed by single base extension assays, indicating likely false positives. Compared to DRAGEN, a conventional BWA + GATK Mutect2 pipeline maintains equivalent sensitivity but produces a 4-fold increase in FP errors, along with a notable enrichment of recurrent FP-prone alleles.

conclusionsT-NGS results from certified providers exhibit substantial variability in both sensitivity and FP error rates. Conventional pipelines not only increase FP errors but also accumulate recurrent FP-prone alleles. These findings underscore the urgent need for standardized pipelines and rigorous quality control measures to ensure the reliability of T-NGS in clinical diagnostics.

Indexed as

High-Throughput Nucleotide SequencingSequence Analysis, DNAFalse Negative ReactionsFalse Positive ReactionsFemaleHumansCancerFalse negative errorFalse positive errorMutationNGSQuality controlReference standard

Identifiers

PMID41327433
PMCPMC12670792

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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.