Evidence map›Paper›PMID 33162997›Full record

ArticleFrontiers in immunology2020

Efficient Sequencing, Assembly, and Annotation of Human KIR Haplotypes.

David Roe, Jonathan Williams, Keyton Ivery, Jenny Brouckaert, Nick Downey, Chad Locklear, Rui Kuang, Martin Maiers

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
0.8field-weighted citation impact, top 28% 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

10 citing papers in PubMed, 17 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

8 authors at 4 institutions in 1 country.

David RoeBioinformatics and Computational Biology, University of Minnesota, Rochester, MN, United States.
Jonathan WilliamsDNA Identification Testing Division, Laboratory Corporation of America Holdings, Burlington, NC, United States.
Keyton IveryDNA Identification Testing Division, Laboratory Corporation of America Holdings, Burlington, NC, United States.
Jenny BrouckaertDNA Identification Testing Division, Laboratory Corporation of America Holdings, Burlington, NC, United States.
Nick DowneyIntegrated DNA Technologies, Inc., Coralville, IA, United States.
Chad LocklearIntegrated DNA Technologies, Inc., Coralville, IA, United States.
Rui KuangBioinformatics and Computational Biology, University of Minnesota, Rochester, MN, United States.
Martin MaiersCenter for International Blood and Marrow Transplant Research, Minneapolis, MN, United States.
LabCorp (United States) · USIntegrated DNA Technologies (United States) · USUniversity of Minnesota Rochester · USNational Marrow Donor Program · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The homology, recombination, variation, and repetitive elements in the natural killer-cell immunoglobulin-like receptor (KIR) region has made full haplotype DNA interpretation impossible in a high-throughput workflow. Here, we present a new approach using long-read sequencing to efficiently capture, sequence, and assemble diploid human KIR haplotypes. Probes were designed to capture KIR fragments efficiently by leveraging the repeating homology of the region. IDT xGen

Indexed as

SoftwareAutoimmune DiseasesBlack or African AmericanComputational BiologyHaplotypesHigh-Throughput Nucleotide SequencingHumansImmunotherapyKiller Cells, NaturalMolecular Sequence AnnotationReceptors, KIRSequence Analysis, DNAVirus DiseasesWhite PeopleReceptors, KIRannotationassemblyDNAhaplotypekiller-cell immunoglobulin-like receptornatural killer

Identifiers

PMID33162997
PMCPMC7581912
OpenAlexW3092349148

What OpenQuestion 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 OpenQuestion graph.