Evidence map›Paper›PMID 16436800›Full record

ReviewNucleic acids research2006

Experimental approaches to identify non-coding RNAs.

Alexander Hüttenhofer, Jörg Vogel

Abstract readReview
In one paragraph

Review in Nucleic acids research, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 papers.

0numbers the graph read from it
0cells of the map it votes in
92citing 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

92 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Article
  5. Article
  6. Article
  7. Review
  8. Review
  9. Review
  10. Article
  11. Review
  12. Review
  13. Article
  14. Article
  15. Article
  16. Article
  17. Key players in regulatory RNA realm of bacteria.Biochemistry and biophysics reports · 2022
    Review
  18. Novel Diagnostic Biomarkers in Colorectal Cancer.International journal of molecular sciences · 2022
    Review
  19. Prokaryotic ncRNAs: Master regulators of gene expression.Current research in pharmacology and drug discovery · 2022
    Article
  20. Review

32 more citing papers are in PubMed but not listed here.

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.

Alexander HüttenhoferInnsbruck Biocenter, Division of Genomics and RNomics, Innsbruck Medical University, Fritz-Pregl-Str. 3, 6020 Innsbruck, Austria. alexander.huettenhofer@i-med.ac.at
Jörg Vogel

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cellular RNAs that do not function as messenger RNAs (mRNAs), transfer RNAs (tRNAs) or ribosomal RNAs (rRNAs) comprise a diverse class of molecules that are commonly referred to as non-protein-coding RNAs (ncRNAs). These molecules have been known for quite a while, but their importance was not fully appreciated until recent genome-wide searches discovered thousands of these molecules and their genes in a variety of model organisms. Some of these screens were based on biocomputational prediction of ncRNA candidates within entire genomes of model organisms. Alternatively, direct biochemical isolation of expressed ncRNAs from cells, tissues or entire organisms has been shown to be a powerful approach to identify ncRNAs both at the level of individual molecules and at a global scale. In this review, we will survey several such wet-lab strategies, i.e. direct sequencing of ncRNAs, shotgun cloning of small-sized ncRNAs (cDNA libraries), microarray analysis and genomic SELEX to identify novel ncRNAs, and discuss the advantages and limits of these approaches.

Indexed as

Gene Expression ProfilingGene LibraryGenomicsOligonucleotide Array Sequence AnalysisRNA, UntranslatedSequence Analysis, RNARNA, Untranslated

Identifiers

PMID16436800
PMCPMC1351373

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.