  Alignment-based sequence similarity searches, while accurate for some type of
sequences, can produce incorrect results when used on more divergent but
functionally related sequences that have undergone the sequence rearrangements
observed in many bacterial and viral genomes. Here, we propose a classification
model that exploits the complementary nature of alignment-based and
alignment-free similarity measures with the aim to improve the accuracy with
which DNA and protein sequences are characterized. Our model classifies
sequences using a combined sequence similarity score calculated by adaptively
weighting the contribution of different sequence similarity measures. Weights
are determined independently for each sequence in the test set and reflect the
discriminatory ability of individual similarity measures in the training set.
Since the similarity between some sequences is determined more accurately with
one type of measure rather than another, our classifier allows different sets
of weights to be associated with different sequences. Using five different
similarity measures we show that our model significantly improves the
classification accuracy over the current composition and alignment based
models, when predicting the taxonomic lineage for both short viral sequence
fragments and complete viral sequences. We also show that our model can be used
effectively for the classification of reads from a real metagenome dataset as
well as protein sequences.
