Code used in Leemans et al 2019: "Promoter-intrinsic and local chromatin features determine gene repression in LADs"
In order to run the code successfully, a couple of Dependencies are required. Easiest way of installing them would be to use conda:
conda env create -f config/conda_environment.yaml
Dependencies: cutadapt v1.11 chipseq-greylist v1.0.1 deeptools v3.2.0 snakemake v4.0.0 sambamba v0.6.8 samtools v1.9
R Dependencies (separate from conda):
Before generating the data, there are a couple of paths that will need to be set in each of the config files.
The ChIP and TRIP data is downloaded automatically from SRA. PROseq and GROcap data can be obtained by running the proseq_analysis.bash and grocap_analysis.bash scripts respectively.
For SuRE data re-processed to hg38, please contact: b.v.steensel@nki.nl
In order to generate the data following code can be used.
for the TRIP experiment:
snakemake -s scripts/trip/trip.snake
--configfile config/TRIP_config.yaml
--use-conda
Epigenetic effect on promoter expression (SuRE vs GROcap):
snakemake -s scripts/promoters_sure/hg38_lad_repression.snakemake
--configfile config/hg38_lad_repression.yaml
When multiple cores are available "-j [# of cores]" can be used to increase the number of cores used. For TF de-novo motif analysis and affinity calculations, code snippets are in the respecitive R markdown file.
Reports generating figures can be found in Promoters_in_LADs/scripts/reports Which file creates which figures can be found below:
cl20181031_plasmid_mixes.Rmd
- Figure 4
cl20181220_TRIP_feature_plots.Rmd
- Figure S4
cl20190109_lad_detachement.R
- Figure 2, S2
cl20190109_prom_SuRE_vs_GROcap.R
- Figure 1, 6I, S1
cl20190128_TRIP_figures.Rmd
- Figure 3, 6, S3
fc190129_trip_modeling.Rmd
- Figure 5, S5
cl20190304_enh_affinity.Rmd
- Figure 7