Evidence map›Paper›PMID 30753478›Full record

ArticleDatabase : the journal of biological databases and curation2019

Integrated curation and data mining for disease and phenotype models at the Rat Genome Database.

Shur-Jen Wang, Stanley J F Laulederkind, Yiqing Zhao, G Thomas Hayman, Jennifer R Smith, Monika Tutaj, Jyothi Thota, Marek A Tutaj, Matthew J Hoffman, Elizabeth R Bolton and 3 more

Abstract read
In one paragraph

Article in Database : the journal of biological databases and curation, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

13 authors.

Shur-Jen WangDepartment of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI, USA.
Stanley J F LaulederkindDepartment of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI, USA.
Yiqing ZhaoDepartment of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI, USA.
G Thomas HaymanDepartment of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI, USA.
Jennifer R SmithDepartment of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI, USA.
Monika TutajDepartment of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI, USA.
Jyothi ThotaDepartment of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI, USA.
Marek A TutajDepartment of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI, USA.
Matthew J HoffmanDepartment of Physiology, Medical College of Wisconsin, Milwaukee, WI, USA.
Elizabeth R BoltonDepartment of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI, USA.
Jeffrey De PonsDepartment of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI, USA.
Melinda R DwinellDepartment of Physiology, Medical College of Wisconsin, Milwaukee, WI, USA.
Mary ShimoyamaDepartment of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI, USA.

Funding

RAT GENOME DATABASER01HL064541 · NHLBI · MEDICAL COLLEGE OF WISCONSIN · PI ANNE E. KWITEK · 1999 to 2026
$50.0M
NHLBI NIH HHS R01 HL064541
6 · The paper itself

Abstract

Rats have been used as research models in biomedical research for over 150 years. These disease models arise from naturally occurring mutations, selective breeding and, more recently, genome manipulation. Through the innovation of genome-editing technologies, genome-modified rats provide precision models of disease by disrupting or complementing targeted genes. To facilitate the use of these data produced from rat disease models, the Rat Genome Database (RGD) organizes rat strains and annotates these strains with disease and qualitative phenotype terms as well as quantitative phenotype measurements. From the curated quantitative data, the expected phenotype profile ranges were established through a meta-analysis pipeline using inbred rat strains in control conditions. The disease and qualitative phenotype annotations are propagated to their associated genes and alleles if applicable. Currently, RGD has curated nearly 1300 rat strains with disease/phenotype annotations and about 11% of them have known allele associations. All of the annotations (disease and phenotype) are integrated and displayed on the strain, gene and allele report pages. Finding disease and phenotype models at RGD can be done by searching for terms in the ontology browser, browsing the disease or phenotype ontology branches or entering keywords in the general search. Use cases are provided to show different targeted searches of rat strains at RGD.

Indexed as

Databases, GeneticData CurationData MiningGenomeAnimalsCytochrome P-450 Enzyme SystemDiseaseDisease Models, AnimalMolecular Sequence AnnotationPhenotypeRatsCytochrome P-450 Enzyme System

Identifiers

PMID30753478
PMCPMC6369425

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.