Source code for our *cis*-regulatory module (CRM)-finding alogorithms can be found below.
Yeast data from Lee *et al.* (*Science*, 2002) can be downloaded
here.

Both algorithms learn a CRM as a set of motifs and a *logical* and
*spatial* relationship among them.
The learned CRM distinguishes between a set of positive DNA sequence examples
(*e.g.* promoters of interest) and a set of negative (control)
sequences.

Key differences among these algorithms are listed below for each (see papers
for details). In either case, the user may specify *which* logical and
spatial aspects are allowed for a learned CRM.

**Contact**:
notocs.wisc.edu
(Keith Noto, University of Wisconsin-Madison)

Noto and Craven,
* Learning Probabilistic Models of *cis*-Regulatory Modules that Represent Logical and Spatial Aspects*,
European Conference on Computational Biology (ECCB) 2006
(PDF)

Key points of this algorithm:

- Learns motif (PWMs)
*de novo*. - Learns spatial
*preferences*instead of hard constraints. For example, it learns a smooth probability distribution over possible distances between adjacent motifs instead of a maximum allowable distance constraint. - In fact, the algorithm learns a generalized
*hidden Markov model*(HMM) representation of a CRM, and learns both model structure (number of motifs and logical relationship among them) and parameters (motif PWMs and spatial preferences).

Download:

- Downloaded latest version of source code and documentation (tar.gz)
- View documentation online (includes installation instructions)
- Previous releases (mostly edited for combatibility with g++ on various platforms):

Noto and Craven,
*A Specialized Learner for Inferring Structured *cis*-Regulatory Modules*,
BMC Bioinformatics, 2006
(PDF)

Key points of this algorithm:

- Selects the relevant CRM motifs from a given set of candidate motifs.
These candidates are defined as
*position weight matrices*(PWMs) and may come from a database or suggested motifs from a motif-learning algorithm. - Learns constraints on the spatial relationships among motifs. For example, a CRM may include a maximum distance (in base pairs) between motifs.

Download:

- Download source code and documentation
- View documentation online (includes installation instructions)