Evidence map›Paper›PMID 19861422›Full record

ArticleRNA (New York, N.Y.)2009

The tedious task of finding homologous noncoding RNA genes.

Peter Menzel, Jan Gorodkin, Peter F Stadler

Abstract read
In one paragraph

Article in RNA (New York, N.Y.), 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.

0numbers the graph read from it
0cells of the map it votes in
39citing 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

39 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Unbiased anchors for reliable genome-wide synteny detection.Algorithms for molecular biology : AMB · 2025
    Article
  6. Article
  7. Article
  8. Article
  9. The Genome of the "Sea Vomit"Life (Basel, Switzerland) · 2021
    Article
  10. Article
  11. Article
  12. The locality dilemma of Sankoff-like RNA alignments.Bioinformatics (Oxford, England) · 2020
    Article
  13. Article
  14. Review
  15. Article
  16. Article
  17. Article
  18. Article
  19. Review
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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.

Peter MenzelSection for Genetics and Bioinformatics, IBHV, and Center for Applied Bioinformatics, University of Copenhagen, DK-1870 Frederiksberg, Denmark.
Jan Gorodkin
Peter F Stadler

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

User-driven in silico RNA homology search is still a nontrivial task. In part, this is the consequence of a limited precision of the computational tools in spite of recent exciting progress in this area, and to a certain extent, computational costs are still problematic in practice. An important, and as we argue here, dominating issue is the dependence on good curated (secondary) structural alignments of the RNAs. These are often hard to obtain, not so much because of an inherent limitation in the available data, but because they require substantial manual curation, an effort that is rarely acknowledged. Here, we qualitatively describe a realistic scenario for what a "regular user" (i.e., a nonexpert in a particular RNA family) can do in practice, and what kind of results are likely to be achieved. Despite the indisputable advances in computational RNA biology, the conclusion is discouraging: BLAST still works better or equally good as other methods unless extensive expert knowledge on the RNA family is included. However, when good curated data are available the recent development yields further improvements in finding remote homologs. Homology search beyond the reach of BLAST hence is not at all a routine task.

Indexed as

Nucleic Acid ConformationSequence Homology, Nucleic AcidAnimalsComputational BiologyHumansRNA, UntranslatedSequence Analysis, DNARNA, Untranslated

Identifiers

PMID19861422
PMCPMC2779685

What OpenQuestion holds

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Read underepoch 390

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.