GibbsILR README
===============
last updated: July 28, 2006



If you use this program in your research, please cite:

Patrick Ng, Niranjan Nagarajan, Neil Jones, and Uri Keich. "Apples to 
apples: improving the performance of motif finders and their 
significance analysis in the Twilight Zone." 
Bioinformatics 2006 22(14):e393-e401

Abstract
Motivation: Effective algorithms for finding relatively weak motifs are an 
important practical necessity while scanning long DNA sequences for 
regulatory elements. The success of such an algorithm hinges on the ability 
of its scoring function combined with a significance analysis test to discern
real motifs from random noise.

Results: In the first half of the paper we show that the paradigm of 
relying on entropy scores and their E-values can lead to undesirable 
results when searching for weak motifs and we offer alternate approaches 
to analyzing the significance of motifs. In the second half of the paper
we reintroduce a scoring function and present a motif-finder that
optimizes it that are more effective in finding relatively weak motifs
than other tools.


INSTALLATION
============
To extract the files, use "tar -zxvf gibbsilr.tar.gz". After the 
extraction, the program can be compiled by typing "./compile" or
by going into the "code" directory and typing "make". That should 
create "gibbsilr.out". I have previously ran this program under
Cygwin, linux 32-bit, and linux 64-bit. 

The benchmarks in the paper were done without the -funroll-loops
compiler option. It seems that this compiler option speeds up 
GibbsILR significally, but it was left out in the benchmarks 
so that all finders were on equal footing.


RUNNING THE PROGRAM
===================
For help on the parameters of the program, simply type "./gibbsilr.out". 
GibbsILR used the following parameters on the benchmarks shown in the
paper:

./gibbsilr sample.fa -l 13 -t 250 -L 200 -p 0.05