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06 Jul 2011 (Updated )

a MATLAB GUI platform for determination and evaluation of biologically significant motifs

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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.

Required Products Bioinformatics Toolbox
Symbolic Math Toolbox
MATLAB release MATLAB 7.10 (R2010a)
Other requirements MEME suite, STAMP platform, SetPartFolder
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17 Aug 2011 1.1

The new version includes bug fixes for motif searches that do not seek to find motifs on the reverse compliment strand, two new options for the site inclusion/exclusion protocol, and updated README file.

03 Oct 2011 1.2

More bug fixes, functionality added to incorporate a custom background file in motif searches.

17 Nov 2011 1.3

variables changed to match choices with upcoming publications, extraneous functions removed, code re-commented, some bug fixes, synthetic data example included and explained

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