Terminator Detection by Support Vector Machine Utilizing aStochastic Context-Free Grammar

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A 2-stage detector was designed to find rho-independent transcription terminators in the Escherichia coli genome. The detector includes a Stochastic Context Free Grammar (SCFG) component and a Support Vector Machine (SVM) component. To find terminators, the SCFG searches the intergenic regions of nucleotide sequence for local matches to a terminator grammar that was designed and trained utilizing examples of known terminators. The grammar selects sequences that are the best candidates for terminators and assigns them a prefix, stem-loop, suffix structure using the Cocke-Younger-Kasaami (CYK) algorithm, modified to incorporate energy affects of base pairing. The parameters from this inferred structure are ... continued below

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Francis-Lyon, Patricia; Cristianini, Nello & Holbrook, Stephen December 30, 2006.

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A 2-stage detector was designed to find rho-independent transcription terminators in the Escherichia coli genome. The detector includes a Stochastic Context Free Grammar (SCFG) component and a Support Vector Machine (SVM) component. To find terminators, the SCFG searches the intergenic regions of nucleotide sequence for local matches to a terminator grammar that was designed and trained utilizing examples of known terminators. The grammar selects sequences that are the best candidates for terminators and assigns them a prefix, stem-loop, suffix structure using the Cocke-Younger-Kasaami (CYK) algorithm, modified to incorporate energy affects of base pairing. The parameters from this inferred structure are passed to the SVM classifier, which distinguishes terminators from non-terminators that score high according to the terminator grammar. The SVM was trained with negative examples drawn from intergenic sequences that include both featureless and RNA gene regions (which were assigned prefix, stem-loop, suffix structure by the SCFG), so that it successfully distinguishes terminators from either of these. The classifier was found to be 96.4% successful during testing.

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  • 2007 Pacific Symposium on Biocomputing, Maui,Hawaii, Jan 3-7, 2007

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  • Report No.: LBNL--62895
  • Grant Number: DE-AC02-05CH11231
  • Grant Number: NIHSHSH03
  • Office of Scientific & Technical Information Report Number: 928788
  • Archival Resource Key: ark:/67531/metadc899345

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

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  • December 30, 2006

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  • Sept. 27, 2016, 1:39 a.m.

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Francis-Lyon, Patricia; Cristianini, Nello & Holbrook, Stephen. Terminator Detection by Support Vector Machine Utilizing aStochastic Context-Free Grammar, article, December 30, 2006; United States. (digital.library.unt.edu/ark:/67531/metadc899345/: accessed December 13, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.