  We present a new approach for improving motif scanning accuracy, based on
analysis of in-between similarity. Given a set of motifs obtained from a
scanning process, we construct an associated weighted graph. We also compute
the expected weight of an edge in such a graph. It turns out that restricting
results to the maximal clique in the graph, computed with respect to the
expected weight, greatly increases precision, hence improves accuracy of the
scan. We tested the method on an ungapped motif-characterized protein family
from five plant proteomes. The method was applied to three iterative motif
scanners - PSI-BLAST, JackHMMer and IGLOSS - with very good results
