Vector space classification of DNA sequences

H. M. Müller, S. E. Koonin

Research output: Contribution to journalArticlepeer-review


Revisiting the problem of intron-exon identification, we use a principal component analysis (PCA) to classify DNA sequences and present first results that validate our approach. Sequences are translated into document vectors that represent their word content; a principal component analysis then defines Gaussian-distributed sequence classes. The classification uses word content and variation of word usage to distinguish sequences. We test our approach with several data sets of genomic DNA and are able to classify introns and exons with an accuracy of up to 96%. We compare the method with the best traditional coding measure, the non-overlapping hexamer frequency count, and find that the PCA method produces better results. We also investigate the degree of cross-validation between different data sets of introns and exons and find evidence that the quality of a data set can be detected.

Original languageEnglish (US)
Pages (from-to)161-169
Number of pages9
JournalJournal of Theoretical Biology
Issue number2
StatePublished - Jul 21 2003


  • Clustering
  • Document vector
  • Gene structure
  • Genomics
  • Intron-exon identification
  • Principal component analysis

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology
  • General Agricultural and Biological Sciences
  • Applied Mathematics


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