Evidence map›Paper›PMID 30329086›Full record

ArticleNucleic acids research2019

CAGm: a repository of germline microsatellite variations in the 1000 genomes project.

Nicholas Kinney, Kyle Titus-Glover, Jonathan D Wren, Robin T Varghese, Pawel Michalak, Han Liao, Ramu Anandakrishnan, Arichanah Pulenthiran, Lin Kang, Harold R Garner

Open access · goldAbstract read
In one paragraph

Article in Nucleic acids research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.9field-weighted citation impact, top 21% of its field
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

6 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Assessment of Microsatellite Instability from Next-Generation Sequencing Data.Advances in experimental medicine and biology · 2022
    Article
  5. Article
  6. 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 at 4 institutions in 2 countries.

Nicholas KinneyEdward Via College of Osteopathic Medicine, 2265 Kraft Drive, Blacksburg, VA 24060, USA.
Kyle Titus-GloverDepartment of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA.
Jonathan D WrenArthritis and Clinical Immunology Research Program, Division of Genomics and Data Sciences Oklahoma Medical Research Foundation, Oklahoma City, OK 73104, USA.
Robin T VargheseEdward Via College of Osteopathic Medicine, 2265 Kraft Drive, Blacksburg, VA 24060, USA.
Pawel MichalakEdward Via College of Osteopathic Medicine, 2265 Kraft Drive, Blacksburg, VA 24060, USA.
Han LiaoDepartment of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA.
Ramu AnandakrishnanEdward Via College of Osteopathic Medicine, 2265 Kraft Drive, Blacksburg, VA 24060, USA.
Arichanah PulenthiranEdward Via College of Osteopathic Medicine, 2265 Kraft Drive, Blacksburg, VA 24060, USA.
Lin KangEdward Via College of Osteopathic Medicine, 2265 Kraft Drive, Blacksburg, VA 24060, USA.
Harold R GarnerEdward Via College of Osteopathic Medicine, 2265 Kraft Drive, Blacksburg, VA 24060, USA.
Edward Via College of Osteopathic Medicine · USVirginia Tech · USOklahoma Medical Research Foundation · USVirginia–Maryland College of Veterinary Medicine · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The human genome harbors an abundance of repetitive DNA; however, its function continues to be debated. Microsatellites-a class of short tandem repeat-are established as an important source of genetic variation. Array length variants are common among microsatellites and affect gene expression; but, efforts to understand the role and diversity of microsatellite variation has been hampered by several challenges. Without adequate depth, both long-read and short-read sequencing may not detect the variants present in a sample; additionally, large sample sizes are needed to reveal the degree of population-level polymorphism. To address these challenges we present the Comparative Analysis of Germline Microsatellites (CAGm): a database of germline microsatellites from 2529 individuals in the 1000 genomes project. A key novelty of CAGm is the ability to aggregate microsatellite variation by population, ethnicity (super population) and gender. The database provides advanced searching for microsatellites embedded in genes and functional elements. All data can be downloaded as Microsoft Excel spreadsheets. Two use-case scenarios are presented to demonstrate its utility: a mononucleotide (A) microsatellite at the BAT-26 locus and a dinucleotide (CA) microsatellite in the coding region of FGFRL1. CAGm is freely available at http://www.cagmdb.org/.

Indexed as

Databases, GeneticGenetic VariationGenome, HumanGenomicsMicrosatellite RepeatsFemaleGerm CellsHumansMaleWeb Browser

Identifiers

PMID30329086
PMCPMC6323891
OpenAlexW2896559313

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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.