Evigan: a hidden variable model for integrating gene evidence for eukaryotic gene prediction. MOTIVATION: The increasing diversity and variable quality of evidence relevant to gene annotation argues for a probabilistic framework that automatically integrates such evidence to yield candidate gene models. RESULTS: Evigan is an automated gene annotation program for eukaryotic genomes, employing probabilistic inference to integrate multiple sources of gene evidence. The probabilistic model is a dynamic Bayes network whose parameters are adjusted to maximize the probability of observed evidence. Consensus gene predictions are then derived by maximum likelihood decoding, yielding n-best models (with probabilities for each). Evigan is capable of accommodating a variety of evidence types, including (but not limited to) gene models computed by diverse gene finders, BLAST hits, EST matches, and splice site predictions; learned parameters encode the relative quality of evidence sources. Since separate training data are not required (apart from the training sets used by individual gene finders), Evigan is particularly attractive for newly sequenced genomes where little or no reliable manually curated annotation is available. The ability to produce a ranked list of alternative gene models may facilitate identification of alternatively spliced transcripts. Experimental application to ENCODE regions of the human genome, and the genomes of Plasmodium vivax and Arabidopsis thaliana show that Evigan achieves better performance than any of the individual data sources used as evidence. AVAILABILITY: The source code is available at http://www.seas.upenn.edu/ strctlrn/evigan/evigan.html.

References in zbMATH (referenced in 5 articles )

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  1. Axelson-Fisk, Marina: Comparative gene finding. Models, algorithms and implementation (2015)
  2. Chen, Minmin; Weinberger, Kilian Q.; Xu, Zhixiang (Eddie); Sha, Fei: Marginalizing stacked linear denoising autoencoders (2015) ioport
  3. Dorri, Fatemeh; Ghodsi, Ali: Minimizing the discrepancy between source and target domains by learning adapting components (2014) ioport
  4. Christiansen, Henning; Have, Christian Theil; Lassen, Ole Torp; Petit, Matthieu: Bayesian annotation networks for complex sequence analysis (2011)
  5. Liu, Qian; Mackey, Aaron J.; Roos, David S.; Pereira, Fernando C. N.: Evigan: a hidden variable model for integrating gene evidence for eukaryotic gene prediction. (2008) ioport

Further publications can be found at: http://www.seas.upenn.edu/~strctlrn/bib/papers.html