Agene – Automatic Generation of Species Specific Gene Predictors

Agene

:: DESCRIPTION

Agene automatically generates a species-specific gene predictor from a set of reliable mRNA sequences and a genome.Author applies a Hidden Markov model (HMM) that implements explicit length distribution modelling for all gene structure blocks using acyclic discrete phase type distributions. The state structure of the each HMM is generated dynamically from an array of sub-models to include only gene features represented in the training set.

::DEVELOPER

Kasper Munch @ The Bioinformatics Centre , University of Copenhagen

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux

:: DOWNLOAD

Agene

:: MORE INFORMATION

Citation:

BMC Bioinformatics. 2006 May 21;7:263.
Automatic generation of gene finders for eukaryotic species.
Munch K, Krogh A.

CRAIG 1.0 – Gene Predictor

CRAIG 1.0

:: DESCRIPTION

CRAIG is a new program for ab initio gene prediction based on a conditional random field model with semi-Markov structure that is trained with an online large-margin algorithm related to multiclass SVMs. Our experiments on benchmark vertebrate datasets and on regions from the ENCODE project show significant improvements in prediction accuracy over published gene predictors that use intrinsic features only, particularly at the gene level and on genes with long introns.

::DEVELOPER

Structured Learning at Penn

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
Perl

:: DOWNLOAD

CRAIG

:: MORE INFORMATION

Citation

Global Discriminative Training for Higher-Accuracy Computational Gene Prediction
Bernal A, Crammer K, Hatzigeorgiou A, Pereira F.
PLoS Comput Biol 3(3):e54. 2007

CONTRAST 1.0 – Multiple Sequence de novo Gene Predictor

CONTRAST 1.0

:: DESCRIPTION

CONTRAST predicts protein-coding genes from a multiple genomic alignment using a combination of discriminative machine learning techniques. A two-stage approach is used, in which output from local classifiers is combined with a global model of gene structure. CONTRAST is trained using a novel procedure designed to maximize expected coding region boundary detection accuracy.

::DEVELOPER

Chuong Do (chuongdo@cs.stanford.edu)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux

:: DOWNLOAD

CONTRAST

:: MORE INFORMATION

Citation

Gross SS, Do CB, Sirota M, Batzoglou S.
CONTRAST: A Discriminative, Phylogeny-free Approach to Multiple Informant De Novo Gene Prediction.
Genome Biology, submitted.