Automated gene-model curation using global discriminative learning

Abstract

Gene-model curation creates consensus gene models by combining multiple sources of protein-coding evidence that may be incomplete or inconsistent. To date, manual curation still produces the highest quality models. However, manual curation is too slow and costly to be completed even for the most important organisms. In recent years, machine-learned ensemble gene predictors have become a viable alternative to manual curation. Current approaches make use of signal and genomic region consistency among sources and some voting scheme to resolve conflicts in the evidence. As a further step in that direction, we have developed eCRAIG (ensemble CRAIG), an automated curation tool that combines multiple sources of evidence using global discriminative training. This allows efficient integration of different types of genomic evidence with complex statistical dependencies to maximize directly annotation accuracy. Our method goes beyond previous work in integrating novel non-linear annotation agreement features, as well as combinations of intrinsic features of the target sequence and extrinsic annotation features. We achieved significant improvements over the best ensemble predictors available for Homo sapiens, Caenorhabditis elegans and Arabidopsis thaliana. In particular, eCRAIG achieved a relative mean improvement of 5.1% over Jigsaw, the best published ensemble predictor in all our experiments. The source code and datasets are both available at http://www.seas.upenn.edu/abernal/ecraig.tgz.