Comments (1)
No such df-feature unfortunately. Should not be too hard to implement though.
Input files are pooled after removing duplicate reads. The same is true for ChIP btw. If you want to avoid pooling, you should just run epic multiple times, once on each dataset.
I am finishing my PhD right now, so I do not have time to work much on epic. But it isn't abandoned, will do bug fixes and such.
from epic.
Related Issues (20)
- No Releases HOT 2
- epic 0.2.9 compute_score_threshold.py:24 RuntimeWarning: divide by zero encountered in log HOT 6
- salmo salar genome request HOT 11
- run epic without control samples HOT 2
- Best choice parameters HOT 1
- epic-merge throws an error of ValueError: Cannot convert NA to integer HOT 3
- epic and conda HOT 2
- trackline and recommanded parameters HOT 9
- problem with output HOT 10
- cannot recognize genome HOT 5
- conda still installs 0.2.9 HOT 10
- are read ids necessary in SE mode? HOT 1
- calculating memory requirements based on bam file size HOT 7
- Epic effective for HOT 11
- How does EPIC work ? HOT 6
- Epic depricated HOT 1
- Epic-effective: TypeError: cannot concatenate 'str' and 'NoneType' objects HOT 5
- Genomes cannot find
- ChIP-seq WT vs mutants comparison HOT 2
- Arabidopsis thaliana HOT 2
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from epic.