Evidence map›Paper›PMID 38902799›Full record

ArticleGenome biology2024

Evaluation of somatic copy number variation detection by NGS technologies and bioinformatics tools on a hyper-diploid cancer genome.

Daniall Masood, Luyao Ren, Cu Nguyen, Francesco G Brundu, Lily Zheng, Yongmei Zhao, Erich Jaeger, Yong Li, Seong Won Cha, Aaron Halpern and 16 more

Abstract readEvaluation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Genome biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing 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

26 citing papers in PubMed.

  1. Trial
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  6. A complete human pancreatic cancer genome.bioRxiv : the preprint server for biology · 2026
    Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Benchmarking Ploidy Estimation Methods for Bulk and Single-Cell Whole Genome Sequencing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Seq2Karyotype (S2K): A Method forbioRxiv : the preprint server for biology · 2025
    Article
  19. Review
  20. 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

26 authors.

Daniall Masood *Office of Oncologic Diseases, Office of New Drug, Center for Drug Evaluation and Research, Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, 20993, USA.
Luyao Ren *State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, China.
Cu Nguyen *Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology (CBIIT), National Cancer Institute, Rockville, MD, USA.
Francesco G Brundu *Illumina Inc., San Diego, CA, USA.
Lily Zheng *Office of Oncologic Diseases, Office of New Drug, Center for Drug Evaluation and Research, Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, 20993, USA.
Yongmei ZhaoSequencing Facility Bioinformatics Group, Biomedical Informatics and Data Science Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA.
Erich JaegerIllumina Inc., San Diego, CA, USA.
Yong LiIllumina Inc., San Diego, CA, USA.
Seong Won ChaIllumina Inc., San Diego, CA, USA.
Aaron HalpernIllumina Inc., San Diego, CA, USA.
Sean TruongIllumina Inc., San Diego, CA, USA.
Michael VirataIllumina Inc., San Diego, CA, USA.
Chunhua YanComputational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology (CBIIT), National Cancer Institute, Rockville, MD, USA.
Qingrong ChenComputational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology (CBIIT), National Cancer Institute, Rockville, MD, USA.
Andy PangBionano Genomics, San Diego, CA, 20892, USA.
Reyes AlbertoBionano Genomics, San Diego, CA, 20892, USA.
Chunlin XiaoNational Center for Biotechnology Information, National Librarssy of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA.
Zhaowei YangCenter for Genomics, Loma Linda University School of Medicine, Loma Linda, CA, USA.
Wanqiu ChenCenter for Genomics, Loma Linda University School of Medicine, Loma Linda, CA, USA.
Charles WangCenter for Genomics, Loma Linda University School of Medicine, Loma Linda, CA, USA.
Frank CrossOffice of Oncologic Diseases, Office of New Drug, Center for Drug Evaluation and Research, Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, 20993, USA.
Severine CatreuxIllumina Inc., San Diego, CA, USA.
Leming ShiState Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, China.
Julia A BeaverOffice of Oncologic Diseases, Office of New Drug, Center for Drug Evaluation and Research, Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, 20993, USA.
Wenming XiaoOffice of Oncologic Diseases, Office of New Drug, Center for Drug Evaluation and Research, Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, 20993, USA. wenming.xiao@fda.hhs.gov.ORCID 0000-0003-4096-9724
Daoud M MeerzamanComputational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology (CBIIT), National Cancer Institute, Rockville, MD, USA. meerzamd@mail.nih.gov.

Funding

National Biomedical Information ServicesZIHLM200888 · NLM · NATIONAL LIBRARY OF MEDICINE · PI PALTOO, DINA · 2009 to 2025
$5200.7M
High Throughput DNA SequencerS10OD019960 · OD · LOMA LINDA UNIVERSITY · PI WANG, CHARLES · 2015 to 2015
$272k
National Natural Science Foundation of China 32300536NIH HHS S10 OD019960NIH HHS S10OD019960
6 · The paper itself

Abstract

backgroundCopy number variation (CNV) is a key genetic characteristic for cancer diagnostics and can be used as a biomarker for the selection of therapeutic treatments. Using data sets established in our previous study, we benchmark the performance of cancer CNV calling by six most recent and commonly used software tools on their detection accuracy, sensitivity, and reproducibility. In comparison to other orthogonal methods, such as microarray and Bionano, we also explore the consistency of CNV calling across different technologies on a challenging genome.

resultsWhile consistent results are observed for copy gain, loss, and loss of heterozygosity (LOH) calls across sequencing centers, CNV callers, and different technologies, variation of CNV calls are mostly affected by the determination of genome ploidy. Using consensus results from six CNV callers and confirmation from three orthogonal methods, we establish a high confident CNV call set for the reference cancer cell line (HCC1395).

conclusionsNGS technologies and current bioinformatics tools can offer reliable results for detection of copy gain, loss, and LOH. However, when working with a hyper-diploid genome, some software tools can call excessive copy gain or loss due to inaccurate assessment of genome ploidy. With performance matrices on various experimental conditions, this study raises awareness within the cancer research community for the selection of sequencing platforms, sample preparation, sequencing coverage, and the choice of CNV detection tools.

Indexed as

Computational BiologyDNA Copy Number VariationsHigh-Throughput Nucleotide SequencingLoss of HeterozygosityNeoplasmsSoftwareCell Line, TumorDiploidyGenome, HumanHumansReproducibility of ResultsSequence Analysis, DNAAccuracyBioinformatics toolsCancer genomeConsistencyCopy number variationDetection sensitivityGenome ploidyNext-generation sequencingReproducibility

Identifiers

PMID38902799
PMCPMC11188507

What OpenQuestion holds

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LicenceCC BY
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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.