Evidence map›Paper›PMID 34564318›Full record

ArticleNon-coding RNA2021

Systematic Identification and Functional Validation of New snoRNAs in Human Muscle Progenitors.

Baptiste Bogard, Claire Francastel, Florent Hubé

Open access · goldAbstract read
In one paragraph

Article in Non-coding RNA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact, top 91% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. 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

3 authors at 1 institution in 1 country.

Baptiste BogardUMR7216 Épigénétique et Destin Cellulaire, CNRS, Université de Paris, F-75013 Paris, France.
Claire FrancastelUMR7216 Épigénétique et Destin Cellulaire, CNRS, Université de Paris, F-75013 Paris, France.ORCID 0000-0002-6353-4320
Florent HubéUMR7216 Épigénétique et Destin Cellulaire, CNRS, Université de Paris, F-75013 Paris, France.ORCID 0000-0001-7560-2530
Centre National de la Recherche Scientifique · FR

Funding

AFM-Téléthon #21363AFM-Téléthon #22341
6 · The paper itself

Abstract

Small non-coding RNAs (sncRNAs) represent an important class of regulatory RNAs involved in the regulation of transcription, RNA splicing or translation. Among these sncRNAs, small nucleolar RNAs (snoRNAs) mostly originate from intron splicing in humans and are central to posttranscriptional regulation of gene expression. However, the characterization of the complete repertoire of sncRNAs in a given cellular context and the functional annotation of the human transcriptome are far from complete. Here, we report the large-scale identification of sncRNAs in the size range of 50 to 200 nucleotides without a priori on their biogenesis, structure and genomic origin in the context of normal human muscle cells. We provided a complete set of experimental validation of novel candidate snoRNAs by evaluating the prerequisites for their biogenesis and functionality, leading to their validation as genuine snoRNAs. Interestingly, we also found intergenic snoRNAs, which we showed are in fact integrated into candidate introns of unannotated transcripts or degraded by the Nonsense Mediated Decay pathway. Hence, intergenic snoRNAs represent a new type of landmark for the identification of new transcripts that have gone undetected because of low abundance or degradation after the release of the snoRNA.

Indexed as

gene annotationhuman muscle progenitorsintergenic snoRNAintronmedium RNA-seqnonsense mediated decaynucleolarsnoRNAsnoRNA host-gene

Identifiers

PMID34564318
PMCPMC8482216
OpenAlexW3201381085

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.