Evidence map›Paper›PMID 32623783›Full record

ReviewImmunology and cell biology2021

Detecting pathogenic variants in autoimmune diseases using high-throughput sequencing.

Matt A Field

Open access · hybridAbstract readReview
In one paragraph

Review in Immunology and cell biology, 2021. 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
1.0field-weighted citation impact, top 24% 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, 23 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

1 author at 1 institution in 1 country.

Matt A FieldCentre for Tropical Bioinformatics and Molecular Biology, Australian Institute of Tropical Health and Medicine, James Cook University, Cairns, QLD, Australia.ORCID 0000-0003-0788-6513
Australian National University · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sequencing the first human genome in 2003 took 15 years and cost $2.7 billion. Advances in sequencing technologies have since decreased costs to the point where it is now feasible to resequence a whole human genome for $1000 in a single day. These advances have allowed the generation of huge volumes of high-quality human sequence data used to construct increasingly large catalogs of both population-level and disease-causing variation. The existence of such databases, coupled with a high-quality human reference genome, means we are able to interrogate and annotate all types of genetic variation and identify pathogenic variants for many diseases. Increasingly, sequencing-based approaches are being used to elucidate the underlying genetic cause of autoimmune diseases, a group of roughly 80 polygenic diseases characterized by abnormal immune responses where healthy tissue is attacked. Although sequence data generation has become routine and affordable, significant challenges remain with no gold-standard methodology to identify pathogenic variants currently available. This review examines the latest methodologies used to identify pathogenic variants in autoimmune diseases and considers available sequencing options and subsequent bioinformatic methodologies and strategies. The development of reliable and robust sequencing and analytic workflows to detect pathogenic variants is critical to realize the potential of precision medicine programs where patient variant information is used to inform clinical practice.

Indexed as

Autoimmune DiseasesHigh-Throughput Nucleotide SequencingComputational BiologyDNA Mutational AnalysisGenetic VariationHumansAutoimmune diseaseshigh-throughput sequencingpathogenic variantSNVvariant annotationvariant detection

Identifiers

PMID32623783
PMCPMC7891608
OpenAlexW3039720477

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.