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Gene prediction with a hidden Markov model and a new intron submodel
Abstract
Motivation: The problem of finding the genes in eukaryotic DNA sequences by computational methods is still not satisfactorily solved. Gene finding programs have achieved relatively high accuracy on short genomic sequences but do not perform well on longer sequences with an unknown number of genes in them. Here existing programs tend to predict many false exons. Results: We have developed a new program, AUGUSTUS, for the ab initio prediction of protein coding genes in eukaryotic genomes. The program is based on a Hidden Markov Model and integrates a number of known methods and submodels. It employs a new way of modeling intron lengths. We use a new donor splice site model, a new model for a short region directly upstream of the donor splice site model that takes the reading frame into account and apply a method that allows better GC-content dependent parameter estimation. AUGUSTUS predicts on longer sequences far more human and drosophila genes accurately than the ab initio gene prediction programs we compared it with, while at the same time being more specific. Availability: A web interface for AUGUSTUS and the executable program are located at http://augustus.gobics.de. Supplementary Information: The datasets used for testing and training are available at http://augustus.gobics.de/datasets/ Contact: mstanke@gwdg.de *To whom correspondence should be addressed.
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Augustus is an ab initio gene prediction tool that uses a generalized hidden Markov model (HMM) for gene prediction.
“Augustus is an ab initio gene prediction tool, which uses a generalized hidden Markov model (HMM) for gene prediction.”
From Annotation matters: the effect of structural gene annotation on orthology inference (2025) · cites this paper as Stanke and Waack 2003
Extrinsic evidence such as transcriptome sequences or annotations of closely related genomes can be provided as additional files to Augustus, modifying probabilities during prediction.
“Extrinsic evidence such as transcriptome sequences or annotations of closely related genomes can be provided as additional files, which modify the probabilities during prediction (Stanke and Waack 2003, Hoff and Stanke 2019).”
From Annotation matters: the effect of structural gene annotation on orthology inference (2025) · cites this paper as Stanke and Waack 2003
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Metadata
- Authors
- Stanke, M., & Waack, S.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2003-09-27
- Volume
- 19
- Issue
- suppl_2
- Pages
- ii215-ii225
- ISSN
- 1367-4811
- 1367-4803
- Cited by
- 1,439
- References
- 0
- Language
- en
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