A family of gene prediction programs . Includes :Gene finder based on generalized Hidden Markov ModelsFinds splice sites and protein coding exons, construct gene model and recognize the promotor and poly-A region
Identification of complete gene structures in genomic DNA .Predicts the locations and exon-intron structures of genes in genomic sequences from a variety of organisms.
Protein-Coding Gene Prediction
Grail (will put the link soon)
Glimmer is a system for finding genes in microbial DNA, especially the genomes of bacteria and archaea. Glimmer (Gene Locator and Interpolated Markov Modeler) uses interpolated Markov models (IMMs) to identify the coding regions and distinguish them from noncoding DNA
Neural network predictions of splice site prediction in Arabidopsis thaliana DNA
Neural network predictions of splice sites in human, C. elegans and A. thaliana DNA.
The ORF Finder (Open Reading Frame Finder) is a graphical analysis tool which finds all open reading frames of a selectable minimum size in a user's sequence or in a sequence already in the database.
ORF Gene - Gene Structure Prediction using Homologous Proteins.
Predicts Promoter regions based on scoring homologies with putative eukaryotic Pol II promoter sequences.
A method to identify potential splice sites in (plant) pre-mRNA by sequence inspection using Bayesian statistical models
Produces a list of predicted genes given a sequence of prokaryotic DNA. Each prediction is attributed with a significance score (R-value) indicating how likely it is to be just a non-coding open reading frame rather than a real gene.
This server accepts gene tables or Affymetrix CEL files as input, performs numerical and statistical analysis, links the results to various databases, and returns a report of the results. (Previously known as GeneMachine).
Software system for gene prediction in complete bacterial genomes and large genomic fragments.
DNA Structural Analysis of Sequenced Microbial Genomes
Prediction of vertebrate and C. elegans genes
The NetStart server produces neural network predictions of translation start in vertebrate and Arabidopsis thaliana nucleotide sequences.
Promoter2.0 predicts transcription start sites of vertebrate PolII promoters in DNA sequences. It has been developed as an evolution of simulated transcription factors that interact with sequences in promoter regions. It builds on principles that are common to neural networks and genetic algorithms.
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