Classification of Genomic Sequences via Wavelet Variance and a Self-Organizing Map with an Application to Mitochondrial DNA Articles uri icon

publication date

  • June 2010

start page

  • 1562

issue

  • 1

volume

  • 9

international standard serial number (ISSN)

  • 1544-6115

abstract

  • We present a new methodology for discriminating genomic symbolic sequences, which combines wavelet analysis and a self-organizing map algorithm. Wavelets are used to extract variation across various scales
    in the oligonucleotide patterns of a sequence. The variation is
    quantified by the estimated wavelet variance, which yields a feature
    vector. Feature vectors obtained from many genomic sequences, possibly
    of different lengths, are then classified with a nonparametric
    self-organizing map scheme. When applied to nearly 200 entire
    mitochondrial DNA sequences, or their fragments, the method predicts
    species taxonomic group membership very well, and allows the results to
    be visualized. When only thousands of nucleotides are available,
    wavelet-based feature vectors of short oligonucleotide patterns are more
    efficient in discrimination than frequency-based feature vectors of
    long patterns. This new data analysis strategy could be extended to
    numeric genomic data. The routines needed to perform the computations
    are readily available in two packages of software R.