GIMSAN: motif-finder with biologically realistic and reliable statistical significance analysis
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column_dependency_app/code
conf_examples
data
gibbslib
gibbsmarkov
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README.md
compile_clean.sh
gimsan_result.py
gimsan_submit.py
requirements.txt

README.md

GIMSAN

http://www.cs.cornell.edu/~ppn3/gimsan/

Installation

  1. Install required Python libraries:

     sudo pip install -r requirements.txt
    
  2. Compile GibbsMarkov and ColumnDependency:

     ./compile_clean.sh
    

Usage Example

  1. Set and verify GIMSAN job configuration file conf_examples/test_window_sampling.cfg. Ensure your gimsan_home and r_path directory/path are pointing to the correct location.

  2. Submit GIMSAN job using window-sampling:

      ./gimsan_submit.py --conf=conf_examples/test_window_sampling.cfg -v
    
  3. Generate results and HTML output for GIMSAN job. If parameter main_output_dir=testout/ is set in configuration file, then run:

      ./gimsan_result.py --dir=testout
    
  4. Open HTML file testout/ABF1_YPD_mod/output.html