Gene Finder

There is no real RNA gene finder as we know it for protein-coding genes. The first ambitious attempt was made by E. Rivas and S.R. Eddy with NCRNASCAN. Unfortunately, RNA genes are quite different from protein-coding genes. There is no golden rule like codon usage of protein-coding genes for RNA genes. However, QRNA and RNAz proposed totally new approach to solve this problem.

Class Specific Finder — by Mituyama Toutai — last modified 2006-11-16 19:04

Gene finders that search for a particular class of RNAs.

RNAz — by admin — last modified 2006-10-27 22:25

RNAz is a combination of Vienna's RNAalifold and SVM, and is a program for predicting structurally conserved and thermodynamic stable RNA secondary structures in multiple sequence alignments. It can be used in genome wide screens to detect functional RNA structures, as found in noncoding RNAs and cis-acting regulatory elements of mRNAs.

EvoFold — by admin — last modified 2006-10-27 22:25

EvoFold is a comparative method for identifying functional RNA structures in multiple-sequence alignments. It is based on a probabilistic model-construction called a phylo-SCFG and exploits the characteristic differences of the substitution process in stem-pairing and unpaired regions to make its predictions. Each prediction consists of a specific secondary structure and a folding potential score.

QRNA — by admin — last modified 2006-10-27 22:25

QRNA by E. Rivas and S. Eddy is the first algorithm that employs genome comparison to find RNA genes that conserved among a pair of genomes.

Infernal — by admin — last modified 2006-10-27 22:25

Infernal is an implementation of covariance model (CM) also knwon as profile SCFG. CM is built from a multiple sequence alignment with secondary structure annotation (major source is RFAM). Infernal aligns target sequences to the given CM and report regions best fit to the model. Recently, Infernal package incorporated Zasha's algorithm which tremendously reduces computational time of Infernal.

RNAGENiE — by admin — last modified 2006-10-27 22:25

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.

NCRNASCAN — by admin — last modified 2006-10-27 22:25

NCRNASCAN is the first program that challenged the hard problem of finding generic RNA genes in a genomic sequence.