CONTRAST: a discriminative, phylogeny-free approach to multiple informant de novo gene prediction.
We describe CONTRAST, a gene predictor which directly incorporates information from multiple alignments rather than employing phylogenetic models. This is accomplished through the use of discriminative machine learning techniques, including a novel training algorithm. We use a two-stage approach, in which a set of binary classifiers designed to recognize coding region boundaries is combined with a global model of gene structure. CONTRAST predicts exact coding region structures for 65% more human genes than the previous state-of-the-art method, misses 46% fewer exons and displays comparable gains in specificity.
Gross
Samuel S
SS
Computer Science Department, Stanford University, Stanford, CA, USA. ssgross@cs.stanford.edu
Do
Chuong B
CB
Sirota
Marina
M
Batzoglou
Serafim
S
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D001483:Base Sequence; D005091:Exons; D020224:Expressed Sequence Tags; D015894:Genome, Human; D023281:Genomics; D006801:Humans; D011506:Proteins / Q000235:genetics*; D016415:Sequence Alignment / Q000379:methods*; D012984:Software
D011506:Proteins