Author: aram.amalietti@gmail.com
Dependencies (these are the versions the script was developed with, newer versions should work, but if they don't, please use these versions):
python=3.7
pandas=1.2.3
numpy=1.19.2
pybedtools=0.8.1
iteration_utilities=0.11.0
Usage:
python3 <path_to_script> <motif1,motif2,...motifn> <xl_in> <prtxn> <fasta> <fai> <window> <len> <cores> <chunk_size> <output_dir> <consensus>
motif1,motif2,...motifn is the group of motifs that is searched for around the landmarks, for example AAAA,CCCC,GGGG;
xl_in is a BED file with landmarks around which the motifs are being searched for;
prtxn is the path to the file containing relevant positions where the motif needs to be in order to be detected, it is an output from another script, PEKA;
fasta is the path to the genome in fasta format;
fai is the path to the genome index file;
window is the flanking distance in bases around the landmarks (one base) within which the motifs are being searched for (30 is the usual value);
len is the length of the motifs (4-7 is the usual range);
cores is the number of threads used in the process;
chunk_size is the number of rows per thread (10000 is the usual value);
output_dir is the directory where the results will be saved (make sure it exists);
consensus is a meta motif representing the motif group and will be used in output file names;
Optionally this script can be ran without prtxn file and in that case it will use all the motifs found around crosslinks instead of only those on positions defined in prtxn file.
Outputs:
BED files representing the binding sites found within the given window around the given landmarks, binding sites are basically found motifs merged.
There are two merging distances and two corresponding files, one is merged0<file_name> representing merging distance 0 and the other merged30 for 30.