New techniques for DNA sequence classification.

Published

Journal Article

DNA sequence classification is the activity of determining whether or not an unlabeled sequence S belongs to an existing class C. This paper proposes two new techniques for DNA sequence classification. The first technique works by comparing the unlabeled sequence S with a group of active motifs discovered from the elements of C and by distinction with elements outside of C. The second technique generates and matches gapped fingerprints of S with elements of C. Experimental results obtained by running these algorithms on long and well conserved Alu sequences demonstrate the good performance of the presented methods compared with FASTA. When applied to less conserved and relatively short functional sites such as splice-junctions, a variation of the second technique combining fingerprinting with consensus sequence analysis gives better results than the current classifiers employing text compression and machine learning algorithms.

Full Text

Duke Authors

Cited Authors

  • Wang, JT; Rozen, S; Shapiro, BA; Shasha, D; Wang, Z; Yin, M

Published Date

  • 1999

Published In

Volume / Issue

  • 6 / 2

Start / End Page

  • 209 - 218

PubMed ID

  • 10421523

Pubmed Central ID

  • 10421523

International Standard Serial Number (ISSN)

  • 1066-5277

Digital Object Identifier (DOI)

  • 10.1089/cmb.1999.6.209

Language

  • eng

Conference Location

  • United States