New techniques for DNA sequence classification

Document Type

Article

Publication Date

1-1-1999

Abstract

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.

Identifier

0033052632 (Scopus)

Publication Title

Journal of Computational Biology

External Full Text Location

https://doi.org/10.1089/cmb.1999.6.209

ISSN

10665277

PubMed ID

10421523

First Page

209

Last Page

218

Issue

2

Volume

6

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