Author: Christophe Bécavin, Anne Biton, Hub Bioinformatic and Biostatistic
28 rue du Docteur Roux, Institut Pasteur, Paris
Previous repository: https://gitlab.pasteur.fr/cbecavin/MeRIPSeq/
Executable (2 versions are available one for local computing, another for slurm cluster computing)
sh run.sh project_name
For example: sh run.sh Liver
nohup ./RunSlurmExample.sh > runServer.log 2>&1 &
RunSlurmExample.sh should be executable chmod 777 RunSlurmExample.sh
project-name - Name of the project
The list of file should be in your_folder/Exp_Design/project_name_exp_design.txt Your sequencing data should be in : your_folder/RNASeq_raw/ our experiment design file should be a tab-separated table with at least 5 columns:
DataName | IP_dataset | Input_dataset | BioCond | Seq |
---|---|---|---|---|
Am_ZT3_1 | Am_ZT3_IP_1 | Am_ZT3_Input_1 | Am | 1 |
Am_ZT13_1 | Am_ZT13_IP_1 | Am_ZT13_Input_1 | Am | 2 |
CONV_ZT3_1 | CONV_ZT3_IP_1 | CONV_ZT3_Input_1 | CONV | 1 |
CONV_ZT13_4 | CONV_ZT13_IP_4 | CONV_ZT13_Input_4 | CONV | 2 |
(See example/Liver_exp_design.txt)
Setup true or false each step of the workflow to run it or not
INSTALL first all dependencies: See INSTALL.txt
1 - setup.sh - Prepare Analysis by creating all necessary folders
2 - genome.sh - Prepare genome sequence, annotation, and windows for peak detection
WARNING - Steps 3 to 7 are run separately for each datasets.
WARNING - They might take a ot of time to perform, so they can be parallelized on a cluster.
3 - Trimming, mapping, BAM file filtering and quality control, And WIG calculation for peak detection
4 - Calculate SeqDepth for peak detection techniques
5 - HTSeq - Count number of reads per genes
6 - coverage_window.py - Count number of reads per window
7 - peak_detection.py - Run Fisher, POI,and RPMF peak detection techniques
8 - MACS2 - Run MACS2 peak detection
9 - Search for median coverage for max coverage search
10 - MultiQC for quality control
11 - finalize peak detection by filtering out "bad" peaks, searchning for max coverage and peak center
12 - (Optionnal) Annotate all peaks from all different techniques
13 - Search max coverage of every peaks and center peak position on it.
14 - Regroup peaks from all the detection techniques, annotate them, find overlap position with genes and referent MeRIP-Seq, CLIPSeq and TREW
15 - HTSeq - Count number of reads in each methylation sites detected
16 - Perform differential methylation sites analysis in R
17 - Run differential splicing events
18 - Run Motif presence algorithm
19 - Run GuitarPlot for every list of peak
20 - (Optional) Compare two peak lists to Ref peaks