## Description

This track shows protein-coding gene predictions generated by CONTRAST. Each predicted exon is colored according to confidence level: green (high confidence), orange (medium confidence), or red (low confidence).

## Methods

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.

Please see the CONTRAST web site for details on how these predictions were generated and an estimate of accuracy.

## Credits

Thanks to Samuel Gross of the Batzoglou lab at Stanford University for providing these predictions.

## References

Gross SS, Do CB, Sirota M, Batzoglou S. CONTRAST: A Discriminative, Phylogeny-free Approach to Multiple Informant De Novo Gene Prediction. Genome Biology. 2007 December;8(12):R269.