Promoter prediction on a genomic scale--the Adh experience.
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.
Ohler
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Lehrstuhl für Mustererkennung, University of Erlangen-Nuremberg, D-91058 Erlangen, Germany. ohler@informatik.uni-erlangen.de
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
D000426:Alcohol Dehydrogenase