Evidence map›Paper›PMID 31757929›Full record

ArticleG3 (Bethesda, Md.)2020

Challenges and Approaches to Genotyping Repetitive DNA.

Elizabeth A Morton, Ashley N Hall, Elizabeth Kwan, Calvin Mok, Konstantin Queitsch, Vivek Nandakumar, John Stamatoyannopoulos, Bonita J Brewer, Robert Waterston, Christine Queitsch

Abstract read
In one paragraph

Article in G3 (Bethesda, Md.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

20 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Assessing Human Ribosomal DNA Variation and Its Association With Phenotypic Outcomes.BioEssays : news and reviews in molecular, cellular and developmental biology · 2025
    Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Under the magnifying glass: The ups and downs of rDNA copy number.Seminars in cell & developmental biology · 2023
    Review
  17. Review
  18. Review
  19. Article
  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

10 authors.

Elizabeth A MortonDepartment of Genome Sciences.
Ashley N HallDepartment of Genome Sciences.
Elizabeth KwanDepartment of Genome Sciences.
Calvin MokDepartment of Molecular Genetics, University of Toronto, Ontario M5S 1A8, Canada, and.ORCID 0000-0001-7968-2857
Konstantin QueitschAltius Institute for Biomedical Sciences, Seattle, Washington 98121.
Vivek NandakumarAltius Institute for Biomedical Sciences, Seattle, Washington 98121.
John StamatoyannopoulosAltius Institute for Biomedical Sciences, Seattle, Washington 98121.
Bonita J BrewerDepartment of Genome Sciences.
Robert WaterstonDepartment of Genome Sciences.
Christine QueitschDepartment of Genome Sciences, queitsch@uw.edu.

Funding

TRAINING IN MOLECULAR AND CELLULAR BIOLOGYT32GM007270 · NIGMS · UNIVERSITY OF WASHINGTON · PI RAIBLE, DAVID W · 1985 to 2020
$21.0M
Technology to understand genetic variant effects in contextRM1HG010461 · NHGRI · UNIVERSITY OF WASHINGTON · PI Douglas M Fowler, Bruce Colston Trapnell · 2019 to 2026
$18.9M
Choreography of Eukaryotic DNA ReplicationR35GM122497 · NIGMS · UNIVERSITY OF WASHINGTON · PI BONITA J BREWER · 2017 to 2026
$5.7M
Understanding gene regulation in contextR35GM139532 · NIGMS · UNIVERSITY OF WASHINGTON · PI Christine Queitsch · 2021 to 2026
$3.3M
Ribosomal DNA copy number variation as a source of missing heritability in complex traitsR01GM122088 · NIGMS · UNIVERSITY OF WASHINGTON · PI QUEITSCH, CHRISTINE · 2017 to 2019
$1.4M
Ribosomal DNA copy number as a molecular determinant of wellness and life spanR21AG052020 · NIA · UNIVERSITY OF WASHINGTON · PI QUEITSCH, CHRISTINE · 2016 to 2017
$425k
Determining the role of ribosomal DNA in metazoan agingF31AG063450 · NIA · UNIVERSITY OF WASHINGTON · PI HALL, ASHLEY NICOLE · 2019 to 2020
$79k
NHGRI NIH HHS RM1 HG010461NIA NIH HHS F31 AG063450NIA NIH HHS R21 AG052020NIGMS NIH HHS R01 GM122088NIGMS NIH HHS R35 GM122497NIGMS NIH HHS R35 GM139532NIGMS NIH HHS T32 GM007270
6 · The paper itself

Abstract

Individuals within a species can exhibit vast variation in copy number of repetitive DNA elements. This variation may contribute to complex traits such as lifespan and disease, yet it is only infrequently considered in genotype-phenotype associations. Although the possible importance of copy number variation is widely recognized, accurate copy number quantification remains challenging. Here, we assess the technical reproducibility of several major methods for copy number estimation as they apply to the large repetitive ribosomal DNA array (rDNA). rDNA encodes the ribosomal RNAs and exists as a tandem gene array in all eukaryotes. Repeat units of rDNA are kilobases in size, often with several hundred units comprising the array, making rDNA particularly intractable to common quantification techniques. We evaluate pulsed-field gel electrophoresis, droplet digital PCR, and Nextera-based whole genome sequencing as approaches to copy number estimation, comparing techniques across model organisms and spanning wide ranges of copy numbers. Nextera-based whole genome sequencing, though commonly used in recent literature, produced high error. We explore possible causes for this error and provide recommendations for best practices in rDNA copy number estimation. We present a resource of high-confidence rDNA copy number estimates for a set of

Indexed as

DNA Copy Number VariationsAnimalsCaenorhabditis elegansGenotyping TechniquesPractice Guidelines as TopicRNA, RibosomalSaccharomyces cerevisiaeWhole Genome SequencingRNA, RibosomalCopy Number VariationRepetitive DNARibosomal DNAWhole Genome Sequencing

Identifiers

PMID31757929
PMCPMC6945026

What OpenQuestion holds

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LicenceCC BY
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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.