Evidence map›Paper›PMID 22809414›Full record

ArticleJournal of bioinformatics and computational biology2012

A method for discovering common patterns from two RNA secondary structures and its application to structural repeat detection.

Lei Hua, Jason T L Wang, Xiang Ji, Ankur Malhotra, Mugdha Khaladkar, Bruce A Shapiro, Kaizhong Zhang

Open access · greenAbstract read
In one paragraph

Article in Journal of bioinformatics and computational biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.3field-weighted citation impact, top 42% 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

0 citing papers in PubMed, 3 citations in OpenAlex.

No citing paper in PubMed yet.

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 5 institutions in 2 countries.

Lei HuaDepartment of Computer Science, New Jersey Institute of Technology, Newark, New Jersey 07102, USA. lh56@njit.edu
Jason T L Wang
Xiang Ji
Ankur Malhotra
Mugdha Khaladkar
Bruce A Shapiro
Kaizhong Zhang
New Jersey Institute of Technology · USCenter for Cancer Research · USUniversity of California, San Diego · USUniversity of Pennsylvania · USWestern University · CA

Funding

Computational Approaches for RNA StructureFunction DeterminationZIABC008382 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI SHAPIRO, BRUCE · 2009 to 2021
$5.8M
Intramural NIH HHS ZIA BC008382
6 · The paper itself

Abstract

We propose an ab initio method, named DiscoverR, for finding common patterns from two RNA secondary structures. The method works by representing RNA secondary structures as ordered labeled trees and performs tree pattern discovery using an efficient dynamic programming algorithm. DiscoverR is able to identify and extract the largest common substructures from two RNA molecules having different sizes without prior knowledge of the locations and topologies of these substructures. We also extend DiscoverR to find repeated regions in an RNA secondary structure, and apply this extended method to detect structural repeats in the 3'-untranslated region of a protein kinase gene. We describe the biological significance of a repeated hairpin found by our method, demonstrating the usefulness of the method. DiscoverR is implemented in Java; a jar file including the source code of the program is available for download at http://bioinformatics.njit.edu/DiscoverR.

Indexed as

Nucleic Acid Conformation3' Untranslated RegionsAlgorithmsBase SequenceComputational BiologyMolecular Sequence DataProtein KinasesRNASequence Analysis, RNA3' Untranslated RegionsProtein KinasesRNA

Identifiers

PMID22809414
PMCPMC6319932
OpenAlexW2021405914

What OpenQuestion holds

Textmetadata
LicenceTDM
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.