Sequence alignment kernel for recognition of promoter regions

Leo Gordon*, Alexey Ya Chervonenkis, Alex J. Gammerman, Ilham Shahmuradov, Victor V. Solovyev

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

95 Scopus citations

Abstract

In this paper we propose a new method for recognition of prokaryotic promoter regions with startpoints of transcription. The method is based on Sequence Alignment Kernel, a function reflecting the quantitative measure of match between two sequences. This kernel function is further used in Dual SVM, which performs the recognition. Several recognition methods have been trained and tested on positive data set, consisting of 669 σ70-promoter regions with known transcription startpoints of Escherichia coli and two negative data sets of 709 examples each, taken from coding and non-coding regions of the same genome. The results show that our method performs well and achieves 16.5% average error rate on positive & coding negative data and 18.6% average error rate on positive & non-coding negative data.

Original languageEnglish (US)
Pages (from-to)1964-1971
Number of pages8
JournalBioinformatics
Volume19
Issue number15
DOIs
StatePublished - Oct 12 2003

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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