We present an algorithm for predicting transcription factor binding sites based 
on ChIP-chip and phylogenetic footprinting data. Our algorithm is robust against 
low promoter sequence similarity and motif rearrangements, because it does not 
depend on multiple sequence alignments. This, in turn, allows us to incorporate 
information from more distant species. Representative random data sets are used 
to estimate the score significance. Our algorithm is fully automatic, and does 
not require human intervention. On a recent S. cerevisiae data set, it achieves 
higher accuracy than the previously best algorithms. Adaptive ChIP-chip 
threshold and the modular positional bias score are two general features of our 
algorithm that increase motif prediction accuracy and could be implemented in 
other algorithms as well. In addition, since our algorithm works partly 
orthogonally to other algorithms, combining several algorithms can increase 
prediction accuracy even further. Specifically, our method finds 6 motifs not 
found by the 2nd best algorithm.