0.Introduction
QuicK-mer is an efficient, paralog-sensitive CNV estimation pipeline based around Jellyfish-2. It counts the occurrences of each predefined k-mer inside Illumina sequencing data and normalizes to correct copy number based on pre-defined control regions. QuicK-mer supports both FASTQ and BAM format as input.

1.Prerequisites
Before using the QuicK-mer CNV pipeline, here is a list of programs required:
1)	Jellyfish 2
2)	Python 2.7
3)	matplotlib 1.1.0 or later
4)	samtools (only necessary if input file is in BAM format)

2.Download QuicK-mer
QuicK-mer is distributed as a source package on github. Grab QuicK-mer using the following command:

git clone http://github.com/KiddLab/QuicK-mer

3.Compile
There are 3 required executables written in a compiled language to increase pipeline efficiency. Pre-compiled binaries are included in the distribution. If an OS/CPU not supported by the existing distributed binary is used, the programs should be compiled by the user.
1)	KmerCor
This is the core program for GC bias estimation and depth normalization in QuicK-mer. To compile use the below command:

cd kmer/
fpc -O KmerCor.lpr

2)	kmer2window
This program is used to convert depth data into copy number in a bedGraph format based on predefined window sizes and control regions. Each window contains a fixed number of k-mers. Note that the last window at the end of each chromosome may contain fewer. 

cd kmer/
g++ -O -o kmer2window kmer2window.cpp

3)	CorDepthCombine
The CorDepthCombine program is used to merge each GC-corrected sequencing library (or sequencing lane) from the same sample together. Each sequencing library (or lane) usually contains distinctive GC bias patterns and should be run through QuicK-mer separately.

cd kmer/
fpc -O CorDepthCombine.lpr

5.Installation
QuicK-mer does not need to be installed, all you need to do is add the application folders to your path directory. 

QuicK-mer/
QuicK-mer/kmer/

To do so in unix-like systems, open your .bashrc file in the home directory using a text editor or with vi. Add the following line: 

PATH=$PATH: path_before_Quick-mer/QuicK-mer/:path_before_Quick-mer /QuicK-mer/kmer/
Then execute using:
 source .bashrc.

6.Premade 30-mer lists available for download
The following genomes have unique 30-mer catalogs ready for download
http://kiddlabshare.umms.med.umich.edu/public-data/QuicK-mer/Ref/
1) mm10
2) hg19
3) panTro4
4) canFam3.1

7.Running QuicK-mer
start_kmer_pipeline.py inputfile\*.fastq.gz –o output_prefix hg19/