Home
Run program
The authors
News
Instructions
Ode to RNA
Predictions
RNA Gene Prediction
RNAGENiE Manuscript Published
The paper that describes this work was published in Nucleic Acids Research.Carter R.J., Dubchak I., Holbrook S.R., "A computational approach to identify genes for functional RNAs in Genomic Sequences", Nucleic Acids Res 2001 Oct 1;29(19):3928-38
This program locates novel RNA genes based on the premise that all stable, functional RNAs share common structural elements and that sequences corresponding to these elements would occur preferentially in RNA genes. These structural elements include double helices, GNRA tetraloops, uridine turns, and non-Watson Crick mispairs in a symmetric internal loop. Also UNCG tetraloops, tetraloop receptors and adenosine platforms are expected to occur in higher frequency in RNA genes and thus to be useful for their identification. Initially neural networks have been trained to recognize RNA genes in E. coli. Since neural networks based on the occurrence of RNA structural elements can not be expected to identify non-RNA sequences in a positive manner, we used additional sequence based neural networks based on global sequence descriptors (previously applied to protein fold prediction) to discriminate RNA genes from non-RNA genes.
We have used 6 structural parameters for RNA gene prediction:- number of UNCG tetraloops;
- GNRA tetraloops;
- uridine turns;
- tetraloop receptors;
- CTAG tetramers
- the calculated free energy of folding.
- 4 composition parameters - the number of A, C, G, T bases;
- 16 transition parameters, (dinucleotide frequency).
