motifFinding

http://weblogo.berkeley.edu/logo.cgi

Gibbs sampling paper (for proteins): http://science.sciencemag.org/content/262/5131/208.full.pdf+html

Step 1 : Generate datasets

python generateData.py

Or, to create all datasets asked in the requirements,
source generateDatasets.sh

Step 2 : Find motifs

source motifFinderGibbsAlgo.sh

This program runs the gipps.py for all dataset* folders in the directory
The output generated is insite the datasets folders named gibbs_output
10 Runs have been carried out for each dataset, so that later proper analysis can be done

Step 3 : Analysis

In addition to the predictedmotif.txt and predictedsites.txt (as required), an IC.npy file is generated which can be visualized using the following code as an example:

import numpy as np
data1=np.load('gibbs_01_IC.npy')
data2=np.load('gibbs_02_IC.npy')
data3=np.load('gibbs_03_IC.npy')

import matplotlib.pylab as plt

plt.figure(figsize=(10,2))
plt.plot(data1)
plt.plot(data2)
plt.plot(data3)
plt.xlabel('Number of rounds')
plt.ylabel('Information Content (per bit)')
plt.semilogx()

plt.show()