miRNAFold: a web server for fast miRNA precursor prediction in genomes

Computational methods are required for prediction of non-coding RNAs (ncRNAs), which are involved in many biological processes, especially at post-transcriptional level. Among these ncRNAs, miRNAs have been largely studied and biologists need efficient and fast tools for their identification. In par...

Full description

Saved in:
Bibliographic Details
Published inNucleic acids research Vol. 44; no. W1; pp. W181 - W184
Main Authors Tav, Christophe, Tempel, Sébastien, Poligny, Laurent, Tahi, Fariza
Format Journal Article
LanguageEnglish
Published England Oxford University Press 08.07.2016
Subjects
Algorithms
Animals
Bacteriology
Biochemistry, Molecular Biology
Cellular Biology
Computer Graphics
Environmental Sciences
Genetics
Genome
Humans
Immunology
Information Storage and Retrieval
Internet
Life Sciences
Microbiology and Parasitology
MicroRNAs - classification
MicroRNAs - genetics
Plants - genetics
RNA Folding
RNA Precursors - classification
RNA Precursors - genetics
Sequence Analysis, RNA
Software
Web Server issue
SEQUENCES
CLASSIFICATION
TOOL
Online AccessGet full text

Cover

More Information
Summary:Computational methods are required for prediction of non-coding RNAs (ncRNAs), which are involved in many biological processes, especially at post-transcriptional level. Among these ncRNAs, miRNAs have been largely studied and biologists need efficient and fast tools for their identification. In particular, ab initio methods are usually required when predicting novel miRNAs. Here we present a web server dedicated for miRNA precursors identification at a large scale in genomes. It is based on an algorithm called miRNAFold that allows predicting miRNA hairpin structures quickly with high sensitivity. miRNAFold is implemented as a web server with an intuitive and user-friendly interface, as well as a standalone version. The web server is freely available at: http://EvryRNA.ibisc.univ-evry.fr/miRNAFold.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0305-1048
1362-4962
DOI:10.1093/nar/gkw459