MotifCatcher is a MATLAB platform that seeks to extend the utility of

existing motif-finding programs by systematic inclusion/exclusion of input

sequence entries, and organization of results in a tree of motifs.

MotifCatcher works best when the user enters a moderate number of input sequences

(between about 20 and 200), of which the user expects some will contain a significant motif and some will not. An example data set might be a Chromatin Immunopreciptiation

experiment followed by microarray hybridization (ChIP-chip) experiment, in which

proteins could localize to particular segments of DNA due to either direct

DNA-protein contact (in which case, we may find a subsequence pattern) or

indirect protein-protein interactions (in which case, there will be no

subsequence pattern).

MotifCatcher utilized random sampling within a Monte Carlo framework to

define motifs for subsets of the whole dat aset. The default search style

coordinates iteratively between the MEME and MAST programs (please see

'Installation'). Plausible meaningful subsets of the whole

input data set are organized into a distance tree with help from the STAMP platform

(please see 'Installation') based on motif similarity.

MotifCatcher also has additional comparative analyses available, and a

GUI interface, which allows the user to conveniently visualize the data.

More information is available in the 'README' file.