Evidence map›Paper›PMID 25392423›Full record

ArticleNucleic acids research2014

Profiling small RNA reveals multimodal substructural signals in a Boltzmann ensemble.

Emily Rogers, Christine E Heitsch

Abstract read
In one paragraph

Article in Nucleic acids research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

20 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. How Parameters Influence SHAPE-Directed Predictions.Methods in molecular biology (Clifton, N.J.) · 2024
    Article
  5. Article
  6. Article
  7. Article
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  9. The roles of structural dynamics in the cellular functions of RNAs.Nature reviews. Molecular cell biology · 2019
    Review
  10. Article
  11. Article
  12. Article
  13. 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

2 authors.

Emily RogersSchool of Computational Science and Engineering, Georgia Institute of Technology, 266 Ferst Drive, Atlanta, GA 30332-0765, USA.
Christine E HeitschSchool of Mathematics, Georgia Institute of Technology, 686 Cherry St., Atlanta, GA 30332-0160, USA heitsch@math.gatech.edu.

Funding

Combinational and Computational Methids for the Analysis, Prediction, and DesignR01GM083621 · NIGMS · GEORGIA INSTITUTE OF TECHNOLOGY · PI HEITSCH, CHRISTINE E · 2007 to 2011
$1.3M
NIGMS NIH HHS R01 GM083621
6 · The paper itself

Abstract

As the biomedical impact of small RNAs grows, so does the need to understand competing structural alternatives for regions of functional interest. Suboptimal structure analysis provides significantly more RNA base pairing information than a single minimum free energy prediction. Yet computational enhancements like Boltzmann sampling have not been fully adopted by experimentalists since identifying meaningful patterns in this data can be challenging. Profiling is a novel approach to mining RNA suboptimal structure data which makes the power of ensemble-based analysis accessible in a stable and reliable way. Balancing abstraction and specificity, profiling identifies significant combinations of base pairs which dominate low-energy RNA secondary structures. By design, critical similarities and differences are highlighted, yielding crucial information for molecular biologists. The code is freely available via http://gtfold.sourceforge.net/profiling.html.

Indexed as

Base PairingData Interpretation, StatisticalModels, MolecularNucleic Acid ConformationRNA, BacterialRNA, Small UntranslatedSequence Analysis, RNAVibrio choleraeRNA, BacterialRNA, Small Untranslated

Identifiers

PMID25392423
PMCPMC4267672

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.