Evidence map›Paper›PMID 32095164›Full record

ArticleMobile DNA2020

Finding and extending ancient simple sequence repeat-derived regions in the human genome.

Jonathan A Shortt, Robert P Ruggiero, Corey Cox, Aaron C Wacholder, David D Pollock

Abstract read
In one paragraph

Article in Mobile DNA, 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. Emerging drivers of DNA repeat expansions.Biochemical Society transactions · 2025
    Review
  5. Article
  6. Recurrent DNA nicks drive massive expansions of (GAA)Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  7. Review
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. Review
  16. Finding and Characterizing Repeats in Plant Genomes.Methods in molecular biology (Clifton, N.J.) · 2022
    Article
  17. Review
  18. Review
  19. Review
  20. Review
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.

Jonathan A Shortt1Colorado Center for Personalized Medicine, University of Colorado School of Medicine, Aurora, CO 80045 USA.
Robert P Ruggiero2Department of Biology, Southeast Missouri State University, Cape Girardeau, MO 63701 USA.
Corey Cox1Colorado Center for Personalized Medicine, University of Colorado School of Medicine, Aurora, CO 80045 USA.
Aaron C Wacholder3Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213 USA.
David D Pollock4Department of Biochemistry & Molecular Genetics, University of Colorado School of Medicine, Aurora, CO 80045 USA.ORCID 0000-0002-7627-4214

Funding

Modeling evolution of functional context in proteinsR01GM083127 · NIGMS · UNIVERSITY OF COLORADO DENVER · PI POLLOCK, DAVID D · 2009 to 2019
$2.7M
NIGMS NIH HHS R01 GM083127
6 · The paper itself

Abstract

backgroundPreviously, 3% of the human genome has been annotated as simple sequence repeats (SSRs), similar to the proportion annotated as protein coding. The origin of much of the genome is not well annotated, however, and some of the unidentified regions are likely to be ancient SSR-derived regions not identified by current methods. The identification of these regions is complicated because SSRs appear to evolve through complex cycles of expansion and contraction, often interrupted by mutations that alter both the repeated motif and mutation rate. We applied an empirical, kmer-based, approach to identify genome regions that are likely derived from SSRs.

resultsThe sequences flanking annotated SSRs are enriched for similar sequences and for SSRs with similar motifs, suggesting that the evolutionary remains of SSR activity abound in regions near obvious SSRs. Using our previously described P-clouds approach, we identified 'SSR-clouds', groups of similar kmers (or 'oligos') that are enriched near a training set of unbroken SSR loci, and then used the SSR-clouds to detect likely SSR-derived regions throughout the genome.

conclusionsOur analysis indicates that the amount of likely SSR-derived sequence in the human genome is 6.77%, over twice as much as previous estimates, including millions of newly identified ancient SSR-derived loci. SSR-clouds identified poly-A sequences adjacent to transposable element termini in over 74% of the oldest class of

Indexed as

Genome evolutionGenome structureMicrosatellitesRepeatsSSRTandem repeats

Identifiers

PMID32095164
PMCPMC7027126

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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.