Boost-HiC current implementation is in python 3 and need the current list of package : -h5py -numpy -copy -sklearn -scipy -skimage

Boost-HiC use HiC-Pro output format (described in : http://nservant.github.io/HiC-Pro/MANUAL.html#browsing-the-results ) for raw contact map.

The contact map is stored in tab separated file as : bin_i / bin_j / counts_ij Only no zero values are stored. Contact map are symmetric

The bin are described in a separated bed file which give the genomic coordinate of each bin.

In a first step, the contact map are convert in hdf5 by the pipeline.

The script is made to be most easy to use ad possible, just eddit the file main.py and run it as : python main.py

Important parameter are : -bedfilename : bed file of genomic coordinate of each bin -Operation : 'Boost' or 'Sample' -repositoryout : where output file are stored

-resolution : default is 10000 basepair, change it if you need -alpha : the alpha used to Boost-HiC

Some usefull tool are available in HiCtools.py if you need to made your own script.

Every ouput contact map are stored in hdf5 with the key 'data' in the hdf5 dict.

-inputmat.hdf5 : The contact map that is load in .matrix at the begin, just convert in hdf5 -inputmat_filtered.hdf5 : The original contact map with some filtered bin -filteredbin.txt : list of raw/col that are filtered or not in the contact map as a boolean list. -boostedmat.hdf5 : The input mat improved by BoostHiC procedure.