Evidence map›Paper›PMID 30907867›Full record

ArticleJournal of visualized experiments : JoVE2019

qKAT: Quantitative Semi-automated Typing of Killer-cell Immunoglobulin-like Receptor Genes.

Jyothi Jayaraman, Vitalina Kirgizova, Da Di, Christopher Johnson, Wei Jiang, James A Traherne

Open access · greenAbstract readVideo-Audio Media
In one paragraph

Article in Journal of visualized experiments : JoVE, 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
0.5field-weighted citation impact, top 36% 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, 8 citations in OpenAlex.

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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

6 authors at 3 institutions in 3 countries.

Jyothi JayaramanDepartment of Pathology, University of Cambridge; Department of Physiology, Development and Neuroscience, University of Cambridge; Department of Obstetrics and Gynaecology, University of Cambridge School of Medicine, NIHR Cambridge Biomedical Research Centre; Centre for Trophoblast Research, University of Cambridge.
Vitalina KirgizovaDepartment of Pathology, University of Cambridge.
Da DiDepartment of Pathology, University of Cambridge; Department of Genetics & Evolution, University of Geneva.
Christopher JohnsonDepartment of Pathology, University of Cambridge; Royal Papworth Hospital.
Wei JiangDepartment of Pathology, University of Cambridge; Department of Plant Sciences, University of Cambridge.
James A TraherneDepartment of Pathology, University of Cambridge; jat51@cam.ac.uk.
University of Cambridge · GBCambridge School · PTPapworth Hospital · GB

Funding

European Research Council 695551Medical Research Council G0901682
6 · The paper itself

Abstract

Killer cell immunoglobulin-like receptors (KIRs) are a set of inhibitory and activating immune receptors, on natural killer (NK) and T cells, encoded by a polymorphic cluster of genes on chromosome 19. Their best-characterized ligands are the human leukocyte antigen (HLA) molecules that are encoded within the major histocompatibility complex (MHC) locus on chromosome 6. There is substantial evidence that they play a significant role in immunity, reproduction, and transplantation, making it crucial to have techniques that can accurately genotype them. However, high-sequence homology, as well as allelic and copy number variation, make it difficult to design methods that can accurately and efficiently genotype all KIR genes. Traditional methods are usually limited in the resolution of data obtained, throughput, cost-effectiveness, and the time taken for setting up and running the experiments. We describe a method called quantitative KIR semi-automated typing (qKAT), which is a high-throughput multiplex real-time polymerase chain reaction method that can determine the gene copy numbers for all genes in the KIR locus. qKAT is a simple high-throughput method that can provide high-resolution KIR copy number data, which can be further used to infer the variations in the structurally polymorphic haplotypes that encompass them. This copy number and haplotype data can be beneficial for studies on large-scale disease associations, population genetics, as well as investigations on expression and functional interactions between KIR and HLA.

Indexed as

SoftwareAutomationDNA Copy Number VariationsHaplotypesHumansLinkage DisequilibriumReceptors, KIRReceptors, KIR

Identifiers

PMID30907867
PMCPMC6794157
OpenAlexW2919647056

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.