Evidence map›Paper›PMID 22537005›Full record

ArticleBMC bioinformatics2012

Stable stem enabled Shannon entropies distinguish non-coding RNAs from random backgrounds.

Yingfeng Wang, Amir Manzour, Pooya Shareghi, Timothy I Shaw, Ying-Wai Li, Russell L Malmberg, Liming Cai

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.7field-weighted citation impact, top 16% 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

4 citing papers in PubMed, 34 citations in OpenAlex.

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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

7 authors at 1 institution in 1 country.

Yingfeng WangDepartment of Computer Science, University of Georgia, Athens, Georgia 30602, USA. ywang802@uga.edu
Amir Manzour
Pooya Shareghi
Timothy I Shaw
Ying-Wai Li
Russell L Malmberg
Liming Cai
University of Georgia · US

Funding

Searching Genomes for Non-Coding RNAs by Their StructureR01GM072080 · NIGMS · UNIVERSITY OF GEORGIA · PI CAI, LIMING · 2006 to 2009
$867k
NIGMS NIH HHS R01GM072080-01A1
6 · The paper itself

Abstract

backgroundThe computational identification of RNAs in genomic sequences requires the identification of signals of RNA sequences. Shannon base pairing entropy is an indicator for RNA secondary structure fold certainty in detection of structural, non-coding RNAs (ncRNAs). Under the Boltzmann ensemble of secondary structures, the probability of a base pair is estimated from its frequency across all the alternative equilibrium structures. However, such an entropy has yet to deliver the desired performance for distinguishing ncRNAs from random sequences. Developing novel methods to improve the entropy measure performance may result in more effective ncRNA gene finding based on structure detection.

resultsThis paper shows that the measuring performance of base pairing entropy can be significantly improved with a constrained secondary structure ensemble in which only canonical base pairs are assumed to occur in energetically stable stems in a fold. This constraint actually reduces the space of the secondary structure and may lower the probabilities of base pairs unfavorable to the native fold. Indeed, base pairing entropies computed with this constrained model demonstrate substantially narrowed gaps of Z-scores between ncRNAs, as well as drastic increases in the Z-score for all 13 tested ncRNA sets, compared to shuffled sequences.

conclusionsThese results suggest the viability of developing effective structure-based ncRNA gene finding methods by investigating secondary structure ensembles of ncRNAs.

Indexed as

EntropyNucleic Acid ConformationAlgorithmsBase PairingProbabilityRNA FoldingRNA, UntranslatedRNA, Untranslated

Identifiers

PMID22537005
PMCPMC3358654
OpenAlexW2092777420

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.