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Commit c1a1719c authored by Christophe  BECAVIN's avatar Christophe BECAVIN
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Title: MeRIP-Seq pipeline analysis
Author: Christophe Bécavin, Hub Bioinformatic and Biostatistic
28 rue du Docteur Roux, Institut Pasteur, Paris
GitLab: https://gitlab.pasteur.fr/cbecavin/MeRIPSeq/
Executable (2 versions are available one for local computing, another for slurm cluster computing)
# For local analysis
sh run.sh project_name
For example: sh run.sh Liver
# For Slurm cluster
nohup ./runServer.sh > runServer.log 2>&1 &
# runServer.sh should be executable
# chmod 777 Run.sh
INSTALL first all dependencies: See INSTALL.txt
PARAMETERS:
project-name - Name of the project
The list of file should be in your_folder/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)
###################################################################################
# PIPELINE Description:
# 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
#
# Run on every dataset:
# 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
# 7 - MACS2 - Run MACS2 peak detection
#
# Run only one time per project_name
# 8 - MultiQC for quality control
# 9 - finalize peak detection by filtering out "bad" peaks
# 10 - (Optionnal) Annotate all peaks from all different techniques
# 11 - Regroup peaks from all the detection techniques, annotate them, find overlap position with genes and referent MeRIP-Seq, CLIPSeq and TREW
#
# Run on every dataset:
# 12 - HTSeq - Count number of reads in each methylation sites detected
#
# Run only one time per project_name
# 13 - Perform differential methylation sites analysis in R
#
#
###################################################################################
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