Ranking of Superenhancers
Issue #3
resolved
Hi! I have been running ROSE perfectly and I am happy with the output.
But I wonder how exactly the ranking of the superenhancer works... does it consider both the density of reads and the size of the superenhancer?
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Additional information on how super-enhancers are identified is from Whyte et al., Cell, 2013: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3653129/. Supplemental document 1 describes this process in detail.
Also, this text is copied from the CODE PROCEDURE section of this site.
4) CODE PROCEDURE:
ROSE_main.py will:
format output directory hierarchy Root name of input .gff ([input_enhancer_list].gff) used as naming root for output files. stitch enhancer constituents in INPUT_CONSTITUENT_GFF based on STITCHING_DISTANCE and make .gff and .bed of stitched collection TSS exclusion, if not zero, is attempted before stitching Names of stitched regions start with number of regions stitched followed by leftmost constituent ID call bamToGFF.py to get density of RANKING_BAM and CONTROL_BAM in stitched regions and constituents Maximum time to wait for bamToGFF.py is 12h but can be changed -- quits if running too long call callSuper.R to sort stitched enhancers by their background-subtracted density of RANKING_BAM and separate into two groups