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#KAT - The K-mer Analysis Toolkit

KAT is a suite of tools that analyse jellyfish hashes or sequence files (fasta or fastq) using kmer counts. The following tools are currently available in KAT:

• sect: SEquence Coverage estimator Tool. Estimates the coverage of each sequence in a file using K-mers from another sequence file.

• comp: K-mer comparison tool. Creates a matrix of shared K-mers between two (or three) sequence files or hashes.

• gcp: K-mer GC Processor. Creates a matrix of the number of K-mers found given a GC count and a K-mer count.

• hist: Create an histogram of k-mer occurrences from a sequence file. Adds metadata in output for easy plotting.

• plot: Plotting tool. Contains several plotting tools to visualise K-mer and compare distributions. Requires gnuplot. The following plot tools are available:

  • density: Creates a density plot from a matrix created with the "comp" tool. Typically this is used to compare two K-mer hashes produced by different NGS reads.
  • profile: Creates a K-mer coverage plot for a single sequence. Takes in fasta coverage output coverage from the "sect" tool
  • spectra-cn: Creates a stacked histogram using a matrix created with the "comp" tool. Typically this is used to compare a jellyfish hash produced from a read set to a jellyfish hash produced from an assembly. The plot shows the amount of distinct K-mers absent, as well as the copy number variation present within the assembly.
  • spectra-hist: Creates a K-mer spectra plot for a set of K-mer histograms produced either by jellyfish-histo or kat-histo.
  • spectra-mx: Creates a K-mer spectra plot for a set of K-mer histograms that are derived from selected rows or columns in a matrix produced by the "comp".

In addition, KAT contains a python script for analysing the mathematical distributions present in the K-mer spectra in order to determine how much content is present in each peak.

##Installation:

Generic installation description can be found in the INSTALL file. Short summary:

• Acquire the source code. Either download and decompress the distributable ("tar -xvf kat-.tar.gz"), or clone the git repository (For ssh: git clone [email protected]:TGAC/KAT.git; or for https: git clone https://github.com/TGAC/KAT.git), into a directory on your machine.
• "cd" into root directory of the installation
• Ensure these tools are correctly installed and available on your system:
  • gcc tool chain
  • make
  • gnuplot (required for plotting at runtime, must be available on the path to use this functionality) - http://www.gnuplot.info
• If you cloned the git repository you must first run "./autogen.sh" to create the configure and make files for your project. (If you downloaded a source code distribution tarball then you can skip this step.)
• For a typical installation on a machine where you have root access type ./configure; make; sudo make install;

The configure script can take several options as arguments. One commonly modified option is --prefix, which will install KAT to a custom directory. By default this is "/usr/local", so the KAT executable would be found at "/usr/local/bin" by default. In addition, some options specific to managing KAT dependencies located in non-standard locations are:

• --with-boost - for specifying a custom boost directory (boost is only required for unit testing)

Type ./configure --help for full details.

The Makefile for KAT can take several goals. Full details of common make goals can be found in the INSTALL file. Typically, the following options can optionally used by KAT:

• make check - runs unit tests. NOTE: Requires boost unit test framework to be installed and available.
• make dist - packages the installation into a tarballed distributable.
• make distcheck - runs some sanity tests to ensure the tarballed distributable is likely to work.

KAT also come with a python script called "dist_analysis.py", which allows the user to determine the amount of content under each peak in the K-mer spectra. This script will automatically be moved into your selected install directory after running "make install". However, before running this script you will need to install python and the python numpy and scipy libraries. In future versions this script will be properly integrated into KAT as another subtool written in C++.

##Operating Instructions:

After KAT has been installed, the following tools should be available:

• kat - a single executable binary file that contains a number of subtools.
• dist_analysis.py - a python script for determining the amount of content in each peak in the K-mer spectra

Running kat --help will bring up a list of available tools within kat. To get help on any of these subtools simple type: kat <tool> --help. For example: kat sect --help will show details on how to use the sequence coverage estimator tool.

KAT supports file globbing for input, this is particularly useful when trying to count and analyse kmers for paired end files. For example, assuming you had two files: LIB_R1.fastq, LIB_R2.fastq in the current directory then kat hist -C -m27 LIB_R?.fastq, will consume any files matching the pattern LIB_R?.fastq as input, i.e. LIB_R1.fastq, LIB_R2.fastq. The same result could be achieved listing the files at the command line: kat hist -C -m27 LIB_R1.fastq LIB_R2.fastq

Note, the KAT comp subtool takes 2 or three groups of inputs as positional arguments therefore we need to distinguish between the file groups. This is achieved by surrounding any glob patterns or file lists in single quotes. For example, assuming we have LIB1_R1.fastq, LIB1_R2.fastq, LIB2_R1.fastq, LIB2_R2.fastq in the current directory, and we want to compare LIB1 against LIB2, instead of catting the files together, we might run either: kat comp -C -D 'LIB1_R?.fastq' 'LIB2_R?.fastq'; or ```kat comp -C -D 'LIB1_R1.fastq LIB1_R2.fastq' 'LIB2_R1.fastq LIB2_R2.fastq'. Both commands do the same thing.

##Licensing:

GNU GPL V3. See COPYING file for more details.

##Authors:

Bernardo Clavijo Daniel Mapleson Darren Heavens Sarah Ayling Mario Caccamo

See AUTHORS file for more details.

##Acknowledgements:

Affiliation: The Genome Analysis Centre (TGAC) Funding: The Biotechnology and Biological Sciences Research Council (BBSRC)