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• W1679661372 abstract "Abstract Background The number of sequenced eukaryotic genomes is rapidly increasing. This means that over time it will be hard to keep supplying customised gene finders for each genome. This calls for procedures to automatically generate species-specific gene finders and to re-train them as the quantity and quality of reliable gene annotation grows. Results We present a procedure, Agene, that automatically generates a species-specific gene predictor from a set of reliable mRNA sequences and a genome. We apply a Hidden Markov model (HMM) that implements explicit length distribution modelling for all gene structure blocks using acyclic discrete phase type distributions. The state structure of the each HMM is generated dynamically from an array of sub-models to include only gene features represented in the training set. Conclusion Acyclic discrete phase type distributions are well suited to model sequence length distributions. The performance of each individual gene predictor on each individual genome is comparable to the best of the manually optimised species-specific gene finders. It is shown that species-specific gene finders are superior to gene finders trained on other species." @default.
• W1679661372 created "2016-06-24" @default.
• W1679661372 creator A5083542637 @default.
• W1679661372 creator A5091503957 @default.
• W1679661372 date "2006-05-21" @default.
• W1679661372 modified "2023-10-16" @default.
• W1679661372 title "Automatic generation of gene finders for eukaryotic species" @default.
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• W1679661372 doi "https://doi.org/10.1186/1471-2105-7-263" @default.
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