Pre-requisites

1. R
2. VelvetOptimiser: Please download it here: https://github.com/tseemann/VelvetOptimiser
3. Cookicutter : Please download it here: https://github.com/ad3002/Cookiecutter

Inputs

1. For every sample i, we assign a folder S_i which consists two seprate FASTQ files which are pair-end reads. All Folders S_i's are stored in a main folder M.

2. All sequences of the reference alleles as a nexus file.

How to run the pipeline

1. The required scripts and files are in PIPELINE folder.
2. Make the script PIPELINE.sh executable by doing as:

chmod +x PIPELINE.sh

2. Run the following script in the PIPELINE folder as follows:

./PIPELINE.sh   dir    path

dir : The directory of the main folder M.

path : The path of the nexus file which contains all reference alleles.

Output

1. The pipeline create a folder named RESULT in PIPELINE folder.
2. The folder RESULT consists a folder S_i for every sample. Every folder S_i consists two fastq files and a folder named leftOver. The fastq files are reads likely to come from the gene of interest from all the reads in sample S_i. The folder leftOver consists two fastq files and a folder named Results. The fastq files are the unassinged reads. The folder Results contains two folders: Fraction, assembly_result. The folder Fraction consists the fraction of alleles which being present in the sample in a csv file. The folder assembly_result consists the output of velvetompimiser over unassgined reads; Moreover, it contains Longestcontigs.fa which is the longest conting assembled from the unassgined reads.