chip-seq-analysis-replication

This project is a replication of ChiP-Seq analysis from an experiment regarding gene induction and repression during terminal erythropoiesis that are mediated by distinct epigenetic changes

You can find the research project here

Data

The following samples from the project data have been used:

Sample Name	GEO Accession	Chip Anti-Body	Project Report Shortname
SRR1157326	GSM688808	H3K4me2	Sample0
SRR1157329	GSM688811	H3K27me3	Sample1
SRR1157333	GSM688815	RNA Pol II	Sample2
SRR1157341	GSM688824	RNA Pol II	Sample3

Donwload all four samples in .fastq format and place them into folder Data>original_samples respectively under the names:
sra_data0.fastq
sra_data1.fastq
sra_data2.fastq
sra_data3.fastq

Be sure that there is not an empty line at the end of each sample file.

No control or input/baseline was among the samples selected.

In general, the mouse genome (mm9 version) was used for reference during the stages of Bowtie, MACS, IGV and MEME. Download it and place it in the folder data>bowtie_indexes so it can be used when executing the pipeline later.

Tools you'll need:

• Python 2.7.1
• Samtools 1.11 (Utility)
• Bedtools 2.27.1 (Utility)
• FastQC 0.11.9 (Quality Control)
• Minion (Adapter Prediction)
• Cutadapt 1.9.1 (Adapter Trimming)
• Bowtie 1.0.0 (Alignment)
• MACS 1.4.1 (Peak Calling)
• MEME 5.0.2 (Motif analysis)

Protocol

1. Quality Control (FastQC)
2. Adapter Prediction (Minion)
3. Adapter Trimming (Cutadapt)
4. Quality Control (FastQC)
5. Alignment (Bowtie)
6. Peak Calling (MACS)
7. Visualization (IGV)
8. Motif Analysis (MEME)

Run

To run the configuration and the whole analysis execute the 'GeneralPipeline.sh'

Notes

You can find further information for each analysis stage in the project report.
The Pipeline.sh can be optimized in terms of code.