SiteSifter finds highly conserved DNA motifs embedded within coding
regions. Each instance of a motif is scored based on the chance that
its constituent codons are conserved over and above that required for
amino acid conservation.
COALESCE uses large collections of genomic data and Bayesian integration to predict coregulated gene modules, the conditions of regulation, and the consensus binding motifs for regulation. It uses a synthesis of gene expression biclustering, motif prediction, and data integration (including expression, sequence, nucleosome positioning, and evolutionary conservation). It is available as part of the Sleipnir library.
Nearest Neighbor Networks
Nearest Neighbor Networks (NNN) is a graph-based algorithm used to cluster genes with similar microarray expression profiles. The NNN clustering method is an alternative to classical techniques such as hierarchical and K-means clustering. NNN generates clusters of functionally related genes with high precision, and the clusters generally represent a broader selection of biological processes than those produced by other methods; NNN performs best on data sets with many conditions and on datasets that are modular (i.e. contain several grouped subsets of conditions).