* Ubuntu, or other compatible OS (tested with ubuntu 16.04)
* Python 3 (should be installed by default)
* Docker
* Git
* Unzip
* Bgzip / tabix
These can be installed by running the following commands:
$ apt update
$ apt install git unzip tabix docker.io python-all

* Storage: 256 GB
* Memory (RAM): 8 GB
* Processors (cores): 4

1. Create all required folders and configuration files.
2. Move any provided FASTQ files to their appropriate location.
3. Prepare the configuration file with links to required reference files.
4. Create and run scripts for analysing each sample. 

0. It is recommended to use a service like 'screen' or 'tmux' to detach the process. The terminal can then be closed without interrupting the program.
1. Create a new project folder and move to this location
$ mkdir projectA
$ cd projectA
 
2. Copy all fastq files to the current folder. The name of the fastq files have to follow the following pattern: <sample_name>.1.fastq.gz
$ cp <fastq_file1/2> <sample_name>.1.fastq.gz
$ cp <fastq_file2/2> <sample_name>.2.fastq.gz

3. Copy the bed file containing the target regions into the current folder.
$ cp <target_regions>.bed .

4. Download the pipeline code from GitHub:
$ git clone https://github.com/si-medbif/exomeseq.git

5. Start the command to setup and run the pipeline 
$ exomeseq/runme.sh

5. Start the command to setup the pipeline 
$ exomeseq/runsetup.sh

6. Check the FastQC output (in "sample/FastQC_pre/") and then do either 6a or 6b:

6a. Run the pipeline without trimming reads  
$ exomeseq/run_analysis.sh

6b. Run the pipeline after trimming reads
$ exomeseq/run_qc_analysis.sh

$ ls -l full_report.txt