ArticleNucleic acids research2002
Prediction of consensus structural motifs in a family of coregulated RNA sequences.
Article in Nucleic acids research, 2002. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- RNA motif discovery: a computational overview.Biology direct · 2015Review
- Classification and assessment tools for structural motif discovery algorithms.BMC bioinformatics · 2013Article
- RNAspa: a shortest path approach for comparative prediction of the secondary structure of ncRNA molecules.BMC bioinformatics · 2007Article
- Evolutionary patterns of non-coding RNAs.Theory in biosciences = Theorie in den Biowissenschaften · 2005Article
- Genome-wide analysis of mRNA stability using transcription inhibitors and microarrays reveals posttranscriptional control of ribosome biogenesis factors.Molecular and cellular biology · 2004Article
- RNAProfile: an algorithm for finding conserved secondary structure motifs in unaligned RNA sequences.Nucleic acids research · 2004Article
- GPRM: A genetic programming approach to finding common RNA secondary structure elements.Nucleic acids research · 2003Article
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1 author.
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Abstract
Given a set of homologous or functionally related RNA sequences, the consensus motifs may represent the binding sites of RNA regulatory proteins. Unlike DNA motifs, RNA motifs are more conserved in structures than in sequences. Knowing the structural motifs can help us gain a deeper insight of the regulation activities. There have been various studies of RNA secondary structure prediction, but most of them are not focused on finding motifs from sets of functionally related sequences. Although recent research shows some new approaches to RNA motif finding, they are limited to finding relatively simple structures, e.g. stem-loops. In this paper, we propose a novel genetic programming approach to RNA secondary structure prediction. It is capable of finding more complex structures than stem-loops. To demonstrate the performance of our new approach as well as to keep the consistency of our comparative study, we first tested it on the same data sets previously used to verify the current prediction systems. To show the flexibility of our new approach, we also tested it on a data set that contains pseudoknot motifs which most current systems cannot identify. A web-based user interface of the prediction system is set up at http://bioinfo. cis.nctu.edu.tw/service/gprm/.
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