Evidence map›Paper›PMID 29310597›Full record

ArticleBMC genomics2018

De novo transcriptome assembly, annotation and comparison of four ecological and evolutionary model salmonid fish species.

Madeleine Carruthers, Andrey A Yurchenko, Julian J Augley, Colin E Adams, Pawel Herzyk, Kathryn R Elmer

Erratum issuedAbstract read
In one paragraph

Article in BMC genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 49 papers.

0numbers the graph read from it
0cells of the map it votes in
49citing 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

49 citing papers in PubMed.

  1. Article
  2. Polyploidization-Driven Functional Innovation of AGPase Small Subunit GeneInternational journal of molecular sciences · 2026
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  8. Characterization of transcriptome changes in saline stress adaptation on Leuciscus merzbacheri using PacBio Iso-Seq and RNA-Seq.DNA research : an international journal for rapid publication of reports on genes and genomes · 2024
    Article
  9. Article
  10. Article
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  16. Review
  17. Article
  18. Genes · 2021
    Article
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  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Madeleine CarruthersInstitute of Biodiversity, Animal Health & Comparative Medicine, College of Medical, Veterinary & Life Sciences, University of Glasgow, G12 8QQ, Glasgow, UK.
Andrey A YurchenkoInstitute of Biodiversity, Animal Health & Comparative Medicine, College of Medical, Veterinary & Life Sciences, University of Glasgow, G12 8QQ, Glasgow, UK.
Julian J AugleyGlasgow Polyomics, Wolfson Wohl Cancer Research Centre, University of Glasgow, G61 1QH, Glasgow, UK.
Colin E AdamsInstitute of Biodiversity, Animal Health & Comparative Medicine, College of Medical, Veterinary & Life Sciences, University of Glasgow, G12 8QQ, Glasgow, UK.
Pawel HerzykGlasgow Polyomics, Wolfson Wohl Cancer Research Centre, University of Glasgow, G61 1QH, Glasgow, UK.
Kathryn R ElmerInstitute of Biodiversity, Animal Health & Comparative Medicine, College of Medical, Veterinary & Life Sciences, University of Glasgow, G12 8QQ, Glasgow, UK. kathryn.elmer@glasgow.ac.uk.

Funding

Biotechnology and Biological Sciences Research Council BB/J013854/1FP7 People: Marie-Curie Actions CIG-321999Wellcome TrustWellcome Trust 097821/Z/11/Z
6 · The paper itself

Abstract

backgroundSalmonid fishes exhibit high levels of phenotypic and ecological variation and are thus ideal model systems for studying evolutionary processes of adaptive divergence and speciation. Furthermore, salmonids are of major interest in fisheries, aquaculture, and conservation research. Improving understanding of the genetic mechanisms underlying traits in these species would significantly progress research in these fields. Here we generate high quality de novo transcriptomes for four salmonid species: Atlantic salmon (Salmo salar), brown trout (Salmo trutta), Arctic charr (Salvelinus alpinus), and European whitefish (Coregonus lavaretus). All species except Atlantic salmon have no reference genome publicly available and few if any genomic studies to date.

resultsWe used paired-end RNA-seq on Illumina to generate high coverage sequencing of multiple individuals, yielding between 180 and 210 M reads per species. After initial assembly, strict filtering was used to remove duplicated, redundant, and low confidence transcripts. The final assemblies consisted of 36,505 protein-coding transcripts for Atlantic salmon, 35,736 for brown trout, 33,126 for Arctic charr, and 33,697 for European whitefish and are made publicly available. Assembly completeness was assessed using three approaches, all of which supported high quality of the assemblies: 1) ~78% of Actinopterygian single-copy orthologs were successfully captured in our assemblies, 2) orthogroup inference identified high overlap in the protein sequences present across all four species (40% shared across all four and 84% shared by at least two), and 3) comparison with the published Atlantic salmon genome suggests that our assemblies represent well covered (~98%) protein-coding transcriptomes. Thorough comparison of the generated assemblies found that 84-90% of transcripts in each assembly were orthologous with at least one of the other three species. We also identified 34-37% of transcripts in each assembly as paralogs. We further compare completeness and annotation statistics of our new assemblies to available related species.

conclusionNew, high-confidence protein-coding transcriptomes were generated for four ecologically and economically important species of salmonids. This offers a high quality pipeline for such complex genomes, represents a valuable contribution to the existing genomic resources for these species and provides robust tools for future investigation of gene expression and sequence evolution in these and other salmonid species.

Indexed as

AnnotationBLASTBUSCOGene Ontology (GO) analysisOrthoFinderRNA-seqSalmonidsTranscriptome

Identifiers

PMID29310597
PMCPMC5759245

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LicenceCC BY
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Registered trials

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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.