  Genomic signal processing has been used successfully in bioinformatics to
analyze biomolecular sequences and gain varied insights into DNA structure,
gene organization, protein binding, sequence evolution, etc. But challenges
remain in finding the appropriate spectral representation of a biomolecular
sequence, especially when multiple variable-length sequences need to be handled
consistently. In this study, we address this challenge in the context of the
well-studied problem of classifying genomic sequences into different taxonomic
units (strain, phyla, order, etc.). We propose a novel technique that employs
signal processing in tandem with Gaussian mixture models to improve the
spectral representation of a sequence and subsequently the taxonomic
classification accuracies. The sequences are first transformed into spectra,
and projected to a subspace, where sequences belonging to different taxons are
better distinguishable. Our method outperforms a similar state-of-the-art
method on established benchmark datasets by an absolute margin of 6.06%
accuracy.
