This track shows protein-coding gene predictions generated by
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
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
Genome Biol. 2007;8(12):R269.
PMID: 18096039; PMC: PMC2246271