Many algorithms for sequence analysis rely on word matching or word
statistics. Often, these approaches can be improved if binary patterns
representing match and don't-care positions are used as a filter, such that
only those positions of words are considered that correspond to the match
positions of the patterns. The performance of these approaches, however,
depends on the underlying patterns. Herein, we show that the overlap complexity
of a pattern set that was introduced by Ilie and Ilie is closely related to the
variance of the number of matches between two evolutionarily related sequences
with respect to this pattern set. We propose a modified hill-climbing algorithm
to optimize pattern sets for database searching, read mapping and
alignment-free sequence comparison of nucleic-acid sequences; our
implementation of this algorithm is called rasbhari. Depending on the
application at hand, rasbhari can either minimize the overlap complexity of
pattern sets, maximize their sensitivity in database searching or minimize the
variance of the number of pattern-based matches in alignment-free sequence
comparison. We show that, for database searching, rasbhari generates pattern
sets with slightly higher sensitivity than existing approaches. In our Spaced
Words approach to alignment-free sequence comparison, pattern sets calculated
with rasbhari led to more accurate estimates of phylogenetic distances than the
randomly generated pattern sets that we previously used. Finally, we used
rasbhari to generate patterns for short read classification with CLARK-S. Here
too, the sensitivity of the results could be improved, compared to the default
patterns of the program. We integrated rasbhari into Spaced Words; the source
code of rasbhari is freely available at http://rasbhari.gobics.de
