diff --git a/Tuesday/GREAT/GREAT_analysis_forEpigenomicData.md b/Tuesday/GREAT/GREAT_analysis_forEpigenomicData.md
index 056ab6ceb921699df2d53d11b6fd8db9e2e997d5..0f78e02a7dafdaa9b2815db11813a4388936f198 100644
--- a/Tuesday/GREAT/GREAT_analysis_forEpigenomicData.md
+++ b/Tuesday/GREAT/GREAT_analysis_forEpigenomicData.md
@@ -1,236 +1,39 @@
# Hands-On: ChIPseq functional analysis
-GREAT is a tool GREAT for the functional analysis of cis-regulatory regions. Assigns biological meaning to a set of non-coding genomic regions by analyzing the annotations of the nearby genes. It is thus useful for peaks identified by any epigenomic profiling technique.
+GREAT is a tool GREAT for the functional analysis of cis-regulatory regions. Assigns biological meaning to a set of non-coding genomic regions by analyzing the annotations of the nearby genes. It uses an ORA (Over-Representation analyis) approach. It is useful for peaks identified by any epigenomic profiling technique.
http://great.stanford.edu/public/html/
## Data
-Regions (peaks) showing significant changes in SUMO enrichment among cell cycle phases, classified in 4 profiles by profile clustering.
+ChIP-seq for SUMO2 in synchronized WI-38 fibroblasts at four different cell cycle phases: G1, S, late S and G2/M.
+<details>
+ <summary markdown="span">What's the actual input data for the functional analyis?</summary>
-## get ePeak on your home
+Input data for functional analysis are regions (peaks) showing significant changes in SUMO enrichment among cell cycle (CC) phases, classified in 4 profiles by profile clustering.
+ChIPseq analyis done using hg19.
-* Load modules (ON CLUSTER ONLY)
+</details>
-```
-module load snakemake/6.5.0
-module load python/3.7
-module load singularity
-module load git-lfs/2.13.1
-module load pysam
-```
-* Clone workflow:
+## Perfom analysis
-`git clone https://gitlab.pasteur.fr/hub/ePeak.git`
+* Go to http://great.stanford.edu/public/html/
-* Download singularity container:
-```
-cd ePeak
-singularity pull --arch amd64 --name epeak.img library://rlegendre/epeak/epeak:1.0
-```
+* Choose test and backgroung regions
-## configure ePeak
+<details>
+ <summary markdown="span">How how you chose the background regions?</summary>
-Open config/config.yaml and config/design.txt files
+Depends on your question: Do you want to compare the functional enrichment in comparison to any random region of the region (Whole genome) or, else, in comparison to a given subset of regions that may have a specific genomic context (e.g. all the SUMO peaks that change along the CC).
-* **Design file:** tabulated file of 4 columns.
+</details>
-**Column 1** is the name of the IP file
+* Define association rules. What's the main effect of the the different rules?
-**Column 2** is the name of the corresponding INPUT file
+* Repeat the analysis for the 4 clusters.
-**Column 3** is the replicate number of IP file
+## Conclude
-**Column 4** is the replicate number of the corresponding INPUT file
-
-
-```
-IP_NAME INPUT_NAME NB_IP NB_INPUT
-H3K27ac_shCtrl INPUT_shCtrl 1 1
-H3K27ac_shCtrl INPUT_shCtrl 2 1
-H3K27ac_shUbc9 INPUT_shUbc9 1 1
-H3K27ac_shUbc9 INPUT_shUbc9 2 1
-Klf4_shCtrl INPUT_shCtrl 1 1
-Klf4_shCtrl INPUT_shCtrl 2 2
-Klf4_shUbc9 INPUT_shUbc9 1 1
-Klf4_shUbc9 INPUT_shUbc9 2 2
-```
-
-* **Config file:** yaml file containing all tools parameters
-
-This file is divided into _chunks_. Each chunk correspond to one step or one tool.
-
-
-This first chunk provides input information and assigns working directories.
-`input_dir` path to FASTQ files directory.
-`input_mate` mate pair format (i.e. `_R[12]` for *MATE* = R1 or R2) , must match the *MATE* parameter in FASTQ files.
-`input_extension` filename extension format (i.e. `fastq.gz` or `fq.gz`).
-`analysis_dir` path to analysis directory.
-`tmpdir` path to temporary directory (i.e. `/tmp/` or other)
-
-```
-input_dir: ../ChIP_data
-input_mate: '_R[12]'
-input_extension: '.fastq.gz'
-analysis_dir: $HOME #define for each user
-tmpdir: $TMPDIR
-```
-
-
-The design chunk aims to check that the FASTQ files name match the design file information. The `marks`, `conditions` and `replicates` parameters must respectively match the *MARK*, *COND* and *REP* parameters of the FASTQ files name and the design file.
-For spike-in data, set `spike` on "True" and provide the spike-in genome FASTA file path through the `spike_genome_file` parameter.
-
-```
-design:
- design_file: config/design.txt
- marks: H3K27ac, Klf4
- condition: shCtrl, shUbc9
- replicates: Rep
- spike: false
- spike_genome_file: genome/dmel9.fa
-```
-
-
-This genome chunk provides information about reference genome - directory, name of the index and path to fasta file.
-
-```
-genome:
- index: yes
- genome_directory: genome/
- name: mm10
- fasta_file: genome/mm10_chr1.fa
-```
-
-The fastqc chunk provides quality control checking of fastq files.
-
-```
-fastqc:
- options: ''
- threads: 4
-```
-
-The adapters chunk is relative to quality trimming and adapter removal with cutadapt. A list of common adapters is provided under config directory and give to cutadapt (adapter_list). Then, different parameters are tuned to match precisely with the data.
-
-
-```
-adapters:
- remove: yes
- adapter_list: file:config/adapt.fa
- m: 25
- mode: a
- options: -O 6 --trim-n --max-n 1
- quality: 30
- threads: 4
-```
-
-
-The bowtie2_mapping chunk is relative to the reads mapping against genome file (provided by the genome chunk)
-
-```
-bowtie2_mapping:
- options: "--very-sensitive --no-unal"
- threads: 4
-```
-
-
-The mark duplicates chunk allows to mark PCR duplicate in BAM files. For ChIPseq data, IP and INPUT need to be deduplicated, so the dedup_IP parameter is set to True.
-
-
-```
-mark_duplicates:
- do: yes
- dedup_IP: 'True'
- threads: 4
-```
-
-The remove_biasedRegions chunk is relative to remove biased genomic regions (previously named blacklisted regions)
-
-```
-remove_biasedRegions:
- do: yes
- bed_file: genome/mm10.blacklist.bed
- threads: 1
-```
-
-To produce metaregion profiles, coverages from each samples need to be producted.
-
-See https://deeptools.readthedocs.io/en/latest/content/feature/effectiveGenomeSize.html
-
-```
-bamCoverage:
- do: yes
- options: "--binSize 10 --effectiveGenomeSize 2652783500 --normalizeUsing RPGC"
- spike-in: no
- threads: 4
-```
-
-Set yes to geneBody chunk to produce metaregion profiles. This step need a gene model file in bed format.
-
-```
-geneBody:
- do: yes
- regionsFileName: genome/mm10_chr1_RefSeq.bed
- threads: 4
-```
-
-Set all following chunks 'do' to 'no' for now.
-
-
-## run ePeak
-
-
-Test your configuration by performing a dry-run via:
-
-`snakemake --use-singularity -n --cores 1`
-
-Execute the workflow locally using $N cores via:
-
-```
-export PICARD_TOOLS_JAVA_OPTS="-Xmx8G"
-N=8
-snakemake --use-singularity --singularity-args "-B '/home/'" --cores $N
-```
-
-
-Run it specifically on Slurm cluster:
-
-`sbatch snakemake --use-singularity --singularity-args "-B '$HOME'" --cluster-config config/cluster_config.json --cluster "sbatch --mem={cluster.ram} --cpus-per-task={threads} " -j 200 --nolock --cores $SLURM_JOB_CPUS_PER_NODE`
-
-
-## analyse QC reports
-
-### Look at MultiQC report
-
-- General statistics
-
-<img src="images/Multiqc_mainStats.png" width="1000" align="center" >
-
-- Mapping with bowtie2
-
-<img src="images/bowtie2_se_plot.png" width="700" align="center" >
-
-- Deduplication with MarkDuplicates
-
-<img src="images/picard_deduplication.png" width="700" align="center" >
-
-- Fingerplot
-
-<img src="images/deeptools_fingerprint_plot.png" width="700" align="center" >
-
-
-### Look at 05-QC directory
-
-- Cross correlation
-
- <img src="images/H3K27ac_shCtrl_ppqt.png" width="700" align="center" > <img src="images/Klf4_shCtrl_ppqt.png" width="700" align="center" >
-
-- GeneBody plot/heatmap
-
-<img src="images/geneBodyplot.png" width="700" align="center" >
-
-
-
-
-Would you proceed to the analysis ? justify why
+Use the output to identify if there is a functional difference between the SUMO regions associated to each profile. How would you proceed?