Must have python 2 or 3.
Additional python packages: intervaltree
Please install via: pip install intervaltree
arg 0: Name of the step to execute.
arg 1: Tab separated file containing the structural variants (SV) to be merged.
arg 2: File containing tandem repeat coordinates given under folder trf_coords.
arg 3: SV type, e.g. DEL, INS.
python main.py MERGE ./test_data/toy_SV_data.csv ./trf_coords/chr21.trf.sorted.gor DEL
Note: Merges SVs within and outside separately. Uses an overlap threshold of 50% for SVs outside of tandem repeats, and a threshold of 85% for SVs within the tandem repeats.
arg 0: Name of the step to execute.
arg 1: Tab separated file containing the structural variants (SV) to be merged.
arg 2: File containing tandem repeat coordinates given under folder trf_coords.
arg 3: Output file name for this step.
arg 4: Relaxation parameter while finding SVs within/outside tandem repeats. E.g. When (SV.begin >= TRR.begin - relaxation) and (SV.end <= TRR.end + relaxation), where SV and TRR denotes a structural variant and tandem repeat region, respectively, the SV is accepted as within the given TRR.
python main.py FIND_TRR_OVERLAPS ./test_data/toy_SV_data.csv ./trf_coords/chr21.trf.sorted.gor ./test_data/toy_SV_data.csv.trr_overlap 5
arg 0: Name of the step to execute.
arg 1: File name containing SV and TRR overlaps, i.e. the output file name from the previous step.
arg 2: Output file name for this step.
arg 3: Merging overlap percentage. Note: If different merging overlap parameters will be used for SVs within and outside TRRs, use the smaller percentage in this step.
arg 4: Boolean flag for using the tandem repeat coordinates in SV pre-clustering and merging. Using 1 will carry the SVs within TRRs to the start site of their respective TRRs, using 0 will use the original SV sites.
python main.py PRE_CLUSTER ./test_data/toy_SV_data.csv.trr_overlap ./test_data/toy_SV_data.csv.precluster 50 1
arg 0: Name of the step to execute.
arg 1: File name containing pre-clustered SVs, i.e. output file name from the previous step.
arg 2: Output file name for merged SVs within TRRs.
arg 3: Output file name for merged SVs outside TRRs.
arg 4: SV type, e.g. DEL, INS.
arg 5: Overlap percentage for SVs within TRRs.
arg 6: Overlap percentage for SVs outside TRRs.
python main.py FIND_CLIQUES ./test_data/toy_SV_data.csv.precluster ./test_data/toy_SV_data.csv.intrr.merged.csv ./test_data/toy_SV_data.csv.outtrr.merged.csv DEL 85 50
0: chromosome
1: begin site
2: end site (use begin site + SV length for both insertions and deletions)
3: SV id (unique identifier for the SVs)
4: Sample id (unique identifier for the sample the given SV is found in)
5: Method/algorithm finding the SV
6: SV type (e.g. DEL, INS)
7: SV length
The columns are as follows:
0: SV ids
1: The final clique id for given SV id in the 1st column.
For each clique id, a representative SV can be chosen if there are more than 1 SV per clique id. One approach would be to pick an SV with the most frequent begin, end, or begin-and-end coordinate among the SVs within the same clique id.