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).
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.
-Thanks to Samuel Gross of the +Thanks to Samuel Gross of the Batzoglou lab at Stanford University for providing these predictions.
Gross SS, Do CB, Sirota M, Batzoglou S. CONTRAST: a discriminative, phylogeny-free approach to multiple informant de novo gene prediction. Genome Biol. 2007;8(12):R269. PMID: 18096039; PMC: PMC2246271