Evidence map›Paper›PMID 30194329›Full record

ArticleScientific reports2018

Characterisation and functional predictions of canine long non-coding RNAs.

Céline Le Béguec, Valentin Wucher, Lætitia Lagoutte, Edouard Cadieu, Nadine Botherel, Benoît Hédan, Clotilde De Brito, Anne-Sophie Guillory, Catherine André, Thomas Derrien and 1 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed
1.4field-weighted citation impact, top 20% 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

32 citing papers in PubMed, 37 citations in OpenAlex.

  1. The role of epigenetic alterations in canine mammary cancer.The Journal of reproduction and development · 2026
    Review
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Review
  11. Article
  12. LncRNAs in domesticated animals: from dog to livestock species.Mammalian genome : official journal of the International Mammalian Genome Society · 2022
    Review
  13. Article
  14. Tumor immune microenvironment lncRNAs.Briefings in bioinformatics · 2022
    Review
  15. Article
  16. Fourth Report on Chicken Genes and Chromosomes 2022.Cytogenetic and genome research · 2022
    Review
  17. SARS-CoV-2, Cardiovascular Diseases, and Noncoding RNAs: A Connected Triad.International journal of molecular sciences · 2021
    Review
  18. Article
  19. Article
  20. Review
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

11 authors at 1 institution in 2 countries.

Céline Le BéguecUniv Rennes, CNRS, IGDR (Institut de génétique et développement de Rennes) - UMR 6290, F-35000, Rennes, France.
Valentin WucherUniv Rennes, CNRS, IGDR (Institut de génétique et développement de Rennes) - UMR 6290, F-35000, Rennes, France.
Lætitia LagoutteUniv Rennes, CNRS, IGDR (Institut de génétique et développement de Rennes) - UMR 6290, F-35000, Rennes, France.
Edouard CadieuUniv Rennes, CNRS, IGDR (Institut de génétique et développement de Rennes) - UMR 6290, F-35000, Rennes, France.
Nadine BotherelUniv Rennes, CNRS, IGDR (Institut de génétique et développement de Rennes) - UMR 6290, F-35000, Rennes, France.
Benoît HédanUniv Rennes, CNRS, IGDR (Institut de génétique et développement de Rennes) - UMR 6290, F-35000, Rennes, France.
Clotilde De BritoUniv Rennes, CNRS, IGDR (Institut de génétique et développement de Rennes) - UMR 6290, F-35000, Rennes, France.
Anne-Sophie GuilloryUniv Rennes, CNRS, IGDR (Institut de génétique et développement de Rennes) - UMR 6290, F-35000, Rennes, France.
Catherine AndréUniv Rennes, CNRS, IGDR (Institut de génétique et développement de Rennes) - UMR 6290, F-35000, Rennes, France.
Thomas DerrienUniv Rennes, CNRS, IGDR (Institut de génétique et développement de Rennes) - UMR 6290, F-35000, Rennes, France. tderrien@univ-rennes1.fr.
Christophe HitteUniv Rennes, CNRS, IGDR (Institut de génétique et développement de Rennes) - UMR 6290, F-35000, Rennes, France. hitte@univ-rennes1.fr.ORCID 0000-0003-1714-437X
Centre National de la Recherche Scientifique · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Long non-coding RNAs (lncRNAs) are a family of heterogeneous RNAs that play major roles in multiple biological processes. We recently identified an extended repertoire of more than 10,000 lncRNAs of the domestic dog however, predicting their biological functionality remains challenging. In this study, we have characterised the expression profiles of 10,444 canine lncRNAs in 26 distinct tissue types, representing various anatomical systems. We showed that lncRNA expressions are mainly clustered by tissue type and we highlighted that 44% of canine lncRNAs are expressed in a tissue-specific manner. We further demonstrated that tissue-specificity correlates with specific families of canine transposable elements. In addition, we identified more than 900 conserved dog-human lncRNAs for which we show their overall reproducible expression patterns between dog and human through comparative transcriptomics. Finally, co-expression analyses of lncRNA and neighbouring protein-coding genes identified more than 3,400 canine lncRNAs, suggesting that functional roles of these lncRNAs act as regulatory elements. Altogether, this genomic and transcriptomic integrative study of lncRNAs constitutes a major resource to investigate genotype to phenotype relationships and biomedical research in the dog species.

Indexed as

Databases, Nucleic AcidTranscriptomeAnimalsDogsGene Expression RegulationHumansOrgan SpecificityRNA, Long NoncodingRNA, Long Noncoding

Identifiers

PMID30194329
PMCPMC6128939
OpenAlexW2952416697

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.