  Extracting meaningful patterns from voluminous amount of biological data is a
very big challenge. Motifs are biological patterns of great interest to
biologists. Many different versions of the motif finding problem have been
identified by researchers. Examples include the Planted $(l, d)$ Motif version,
those based on position-specific score matrices, etc. A comparative study of
the various motif search algorithms is very important for several reasons. For
example, we could identify the strengths and weaknesses of each. As a result,
we might be able to devise hybrids that will perform better than the individual
components. In this paper we (either directly or indirectly) compare the
performance of PMSprune (an algorithm based on the $(l, d)$ motif model) and
several other algorithms in terms of seven measures and using well established
benchmarks
  In this paper, we (directly or indirectly) compare the quality of motifs
predicted by PMSprune and 14 other algorithms. We have employed several
benchmark datasets including the one used by Tompa, et.al. These comparisons
show that the performance of PMSprune is competitive when compared to the other
14 algorithms tested.
  We have compared (directly or indirectly) the performance of PMSprune and 14
other algorithms using the Benchmark dataset provided by Tompa, et.al. It is
observed that both PMSprune and DME (an algorithm based on position-specific
score matrices) in general perform better than the 13 algorithms reported in
Tompa et. al.. Subsequently we have compared PMSprune and DME on other
benchmark data sets including ChIP-Chip, ChIP-seq, and ABS. Between PMSprune
and DME, PMSprune performs better than DME on six measures. DME performs better
than PMSprune on one measure (namely, specificity).
