Evidence map›Paper›PMID 36310247›Full record

ArticleGigaScience2022

Best genome sequencing strategies for annotation of complex immune gene families in wildlife.

Emma Peel, Luke Silver, Parice Brandies, Ying Zhu, Yuanyuan Cheng, Carolyn J Hogg, Katherine Belov

Open access · goldAbstract read
In one paragraph

Article in GigaScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
2.9field-weighted citation impact, top 8% 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

23 citing papers in PubMed, 35 citations in OpenAlex.

  1. Adaptive Immunity in Marsupials.Journal of experimental zoology. Part A, Ecological and integrative physiology · 2026
    Review
  2. Article
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  10. MHC Diversity Across Time and Space.Ecology and evolution · 2025
    Review
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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

7 authors at 2 institutions in 2 countries.

Emma PeelSchool of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia.ORCID 0000-0002-2335-8983
Luke SilverSchool of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia.ORCID 0000-0002-1718-5756
Parice BrandiesSchool of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia.ORCID 0000-0003-1702-2938
Ying ZhuSichuan Provincial Academy of Natural Resource Sciences, Chengdu, Sichuan 610000, China.
Yuanyuan ChengSchool of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia.ORCID 0000-0002-1747-9308
Carolyn J HoggSchool of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia.ORCID 0000-0002-6328-398X
Katherine BelovSchool of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia.ORCID 0000-0002-9762-5554
The University of Sydney · AUSichuan Provincial Academy of Natural Resource Sciences · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe biodiversity crisis and increasing impact of wildlife disease on animal and human health provides impetus for studying immune genes in wildlife. Despite the recent boom in genomes for wildlife species, immune genes are poorly annotated in nonmodel species owing to their high level of polymorphism and complex genomic organisation. Our research over the past decade and a half on Tasmanian devils and koalas highlights the importance of genomics and accurate immune annotations to investigate disease in wildlife. Given this, we have increasingly been asked the minimum levels of genome quality required to effectively annotate immune genes in order to study immunogenetic diversity. Here we set out to answer this question by manually annotating immune genes in 5 marsupial genomes and 1 monotreme genome to determine the impact of sequencing data type, assembly quality, and automated annotation on accurate immune annotation.

resultsGenome quality is directly linked to our ability to annotate complex immune gene families, with long reads and scaffolding technologies required to reassemble immune gene clusters and elucidate evolution, organisation, and true gene content of the immune repertoire. Draft-quality genomes generated from short reads with HiC or 10× Chromium linked reads were unable to achieve this. Despite mammalian BUSCOv5 scores of up to 94.1% amongst the 6 genomes, automated annotation pipelines incorrectly annotated up to 59% of manually annotated immune genes regardless of assembly quality or method of automated annotation.

conclusionsOur results demonstrate that long reads and scaffolding technologies, alongside manual annotation, are required to accurately study the immune gene repertoire of wildlife species.

Indexed as

Animals, WildGenomicsAnimalsBase SequenceGenomeHumansMammalsMolecular Sequence Annotationannotationdiseasegenomeimmune geneMHCqualitywildlife

Identifiers

PMID36310247
PMCPMC9618407
OpenAlexW4308180218

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.