CONTRAST is a gene predictor that directly incorporates information from multiple alignments and uses discriminative machine learning techniques to give large improvements in prediction over previous methods

Chuong B Do

Marina Sirota

Samuel S Gross

Serafim Batzoglou

English

PubMed

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CONTRAST: a discriminative, phylogeny-free approach to multiple informant de novo gene prediction

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We describe CONTRAST, the first system for vertebrate protein-coding gene prediction to successfully use the information present in multiple alignments to achieve greater accuracy than the best method based on two-species alignments. CONTRAST predicts exact coding region structures for 65 % more genes than the previous state-of-the-art method, misses 46 % fewer exons, and displays comparable gains in specificity. Background In this work, we consider the task of predicting the locations and structures of the protein-coding genes in a genome. Gene recognition is one of the best-studied problems in computational biology, and as such has been approached through the use of a wide variety of different methods. Gene recognition methods can be broadly divided into three categories, depending on the type of information they employ. Ab initio predictors use only DNA sequence from the genome in which predictions are desired (the target genome). Predictors such as GENSCAN [1], Genie [2], and CRAIG [3] fall into this category. De novo gene predictors additionally make use of aligned DNA sequence from other genomes 1 (informant genomes). Alignments can increase predictive accuracy since protein-coding genes exhibi

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CONTRAST: A Discriminative, Phylogeny-free Approach to Multiple Informant De Novo Gene Prediction

CONTRAST: a discriminative, phylogeny-free approach to multiple informant gene prediction

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CONTRAST: a discriminative, phylogeny-free approach to multiple informant de novo gene prediction

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