Evidence map›Paper›PMID 23315997›Full record

ArticleHuman mutation2013

RNAsnp: efficient detection of local RNA secondary structure changes induced by SNPs.

Radhakrishnan Sabarinathan, Hakim Tafer, Stefan E Seemann, Ivo L Hofacker, Peter F Stadler, Jan Gorodkin

Erratum issuedOpen access · greenAbstract read
In one paragraph

Article in Human mutation, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 93 papers.

0numbers the graph read from it
0cells of the map it votes in
93citing papers in PubMed
5.7field-weighted citation impact, top 3% 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

93 citing papers in PubMed, 152 citations in OpenAlex.

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  18. The effects ofMolecular biology research communications · 2024
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33 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

  • Erratum issued
5 · Who and what money

Authors and funding

6 authors at 5 institutions in 5 countries.

Radhakrishnan SabarinathanCenter for non-coding RNA in Technology and Health, University of Copenhagen, Frederiksberg, Denmark.
Hakim Tafer
Stefan E Seemann
Ivo L Hofacker
Peter F Stadler
Jan Gorodkin
Frederiksberg Hospital · DKLeipzig University · DEMax Planck Institute for Mathematics · DEUniversity of Copenhagen · DKUniversity of Vienna · AT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Structural characteristics are essential for the functioning of many noncoding RNAs and cis-regulatory elements of mRNAs. SNPs may disrupt these structures, interfere with their molecular function, and hence cause a phenotypic effect. RNA folding algorithms can provide detailed insights into structural effects of SNPs. The global measures employed so far suffer from limited accuracy of folding programs on large RNAs and are computationally too demanding for genome-wide applications. Here, we present a strategy that focuses on the local regions of maximal structural change between mutant and wild-type. These local regions are approximated in a "screening mode" that is intended for genome-wide applications. Furthermore, localized regions are identified as those with maximal discrepancy. The mutation effects are quantified in terms of empirical P values. To this end, the RNAsnp software uses extensive precomputed tables of the distribution of SNP effects as function of length and GC content. RNAsnp thus achieves both a noise reduction and speed-up of several orders of magnitude over shuffling-based approaches. On a data set comprising 501 SNPs associated with human-inherited diseases, we predict 54 to have significant local structural effect in the untranslated region of mRNAs.

Indexed as

Polymorphism, Single NucleotideSoftwareAlgorithmsGenetic Association StudiesHumansNucleic Acid ConformationRNARNA FoldingRNA

Identifiers

PMID23315997
PMCPMC3708107
OpenAlexW1981311921

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.