Publication Date:
2012-10-10
Description:
Messenger ribonucleic acids (RNAs) contain a large number of cis -regulatory RNA elements that function in many types of post-transcriptional regulation. These cis -regulatory elements are often characterized by conserved structures and/or sequences. Although some classes are well known, given the wide range of RNA-interacting proteins in eukaryotes, it is likely that many new classes of cis -regulatory elements are yet to be discovered. An approach to this is to use computational methods that have the advantage of analysing genomic data, particularly comparative data on a large scale. In this study, a set of structural discovery algorithms was applied followed by support vector machine (SVM) classification. We trained a new classification model (CisRNA-SVM) on a set of known structured cis -regulatory elements from 3'-untranslated regions (UTRs) and successfully distinguished these and groups of cis -regulatory elements not been strained on from control genomic and shuffled sequences. The new method outperformed previous methods in classification of cis -regulatory RNA elements. This model was then used to predict new elements from cross-species conserved regions of human 3'-UTRs. Clustering of these elements identified new classes of potential cis -regulatory elements. The model, training and testing sets and novel human predictions are available at: http://mRNA.otago.ac.nz/CisRNA-SVM .
Keywords:
Computational Methods, Genomics, Transcriptome Mapping - Monitoring Gene Expression
Print ISSN:
0305-1048
Electronic ISSN:
1362-4962
Topics:
Biology
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