Evidence map›Paper›PMID 26516210›Full record

ReviewThe EMBO journal2015

Long non-coding RNAs in corticogenesis: deciphering the non-coding code of the brain.

Julieta Aprea, Federico Calegari

Abstract readReview
In one paragraph

Review in The EMBO journal, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing 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

38 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. From Junk to Function: LncRNAs in CNS Health and Disease.Frontiers in molecular neuroscience · 2021
    Review
  14. Tight junctions and their regulation by non-coding RNAs.International journal of biological sciences · 2021
    Review
  15. Article
  16. Article
  17. Review
  18. Article
  19. Article
  20. Article
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

2 authors.

Julieta ApreaDFG-Research Center and Cluster of Excellence for Regenerative Therapies, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany.
Federico CalegariDFG-Research Center and Cluster of Excellence for Regenerative Therapies, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany federico.calegari@crt-dresden.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Evidence on the role of long non-coding (lnc) RNAs has been accumulating over decades, but it has been only recently that advances in sequencing technologies have allowed the field to fully appreciate their abundance and diversity. Despite this, only a handful of lncRNAs have been phenotypically or mechanistically studied. Moreover, novel lncRNAs and new classes of RNAs are being discovered at growing pace, suggesting that this class of molecules may have functions as diverse as protein-coding genes. Interestingly, the brain is the organ where lncRNAs have the most peculiar features including the highest number of lncRNAs that are expressed, proportion of tissue-specific lncRNAs and highest signals of evolutionary conservation. In this work, we critically review the current knowledge about the steps that have led to the identification of the non-coding transcriptome including the general features of lncRNAs in different contexts in terms of both their genomic organisation, evolutionary origin, patterns of expression, and function in the developing and adult mammalian brain.

Indexed as

AnimalsBrainHumansNeural Stem CellsRNA, Long NoncodingRNA, Long Noncodingbrain developmentlong non‐coding RNAsneural stem cells

Identifiers

PMID26516210
PMCPMC4687686

What OpenQuestion holds

Textmetadata
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.