title: Promoter prediction on a genomic scale--the Adh experience.
abstract: We describe our statistical system for promoter recognition in genomic DNA with which we took part in the Genome Annotation Assessment Project (GASP1). We applied two versions of the system: the first uses a region-based approach toward transcription start site identification, namely, interpolated Markov chains; the second was a hybrid approach combining regions and signals within a stochastic segment model. We compare the results of both versions with each other and examine how well the application on a genomic scale compares with the results we previously obtained on smaller data sets.
authors.affiliation: Lehrstuhl für Mustererkennung, University of Erlangen-Nuremberg, D-91058 Erlangen, Germany. ohler@informatik.uni-erlangen.de
authors.forename: U
authors.initials: U
authors.lastname: Ohler
mesh_terms: D000426:Alcohol Dehydrogenase / Q000235:genetics*; D000818:Animals; D016208:Databases, Factual; D004331:Drosophila melanogaster / Q000201:enzymology* / Q000235:genetics*; D017344:Genes, Insect / Q000235:genetics; D011336:Probability; D011401:Promoter Regions, Genetic / Q000235:genetics*; D017422:Sequence Analysis, DNA / Q000706:statistics & numerical data; D012984:Software
chemical_list: D000426:Alcohol Dehydrogenase