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Gene prediction in eukaryotes with a generalized hidden Markov model that uses hints from external sources

BMC Bioinformatics · 2006 · Vol. 7(1) · pp. 62–62

Mario Stanke✉(Universitätsmedizin Göttingen)Oliver Schöffmann(Universitätsmedizin Göttingen)Burkhard MorgensternStephan Waack(Universitätsmedizin Göttingen)

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Abstract

Sensitive probabilistic modeling of extrinsic evidence such as sequence database matches can increase gene prediction accuracy. When a match of a sequence interval to an EST or protein sequence is used it should be treated as compound information rather than as information about individual positions.

Genomics and Phylogenetic Studies Machine Learning in Bioinformatics RNA and protein synthesis mechanismsHidden Markov modelComputer scienceSequence (biology)GenomeGene predictionGeneComputational biologyMarkov chainProbabilistic logicChromosome

MeSH terms

AlgorithmsAnimalsArtificial IntelligenceBase SequenceChromosome MappingComputer SimulationHumansMarkov ChainsModels, GeneticMolecular Sequence DataPattern Recognition, AutomatedStochastic ProcessesGenetic VariationModels, StatisticalInformation Storage and Retrieval