Difference between revisions of "PAKAP"

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Revision as of 04:56, 28 April 2014

INTRODUCTION (from paper?)


We have established a Present/Absent Kmer Analysis Pipeline (PAKAP). By reducing each read to component k-mers and comparing the relative abundance of these sub-sequences, we overcome statistical limitations of whole read comparative analysis.

PAKAP consists of a series of scripts written in Perl, Python and Bash scripts and requires Jellyfish [Marcais 2011] as well as optionally SOAPaligner. The scripts are freely available for non-commercial use.


What does PAKAP depend on?

  • Jellyfish for fast kmer counting
  • Some non-standard Perl modules:
    • bioperl
      • Bio::SeqIO
      • Bio::SearchIO
    • Parallel::ForkManager
    • Statistics::Descriptive
    • Config::IniFiles
    • GD::Graph::linespoints (for the script identifyKmerSize)
  • Optional: SOAPaligner

Download

  • Latest Version 1.0:
    • INSERT LINK

How to install?

  • Download the [link_here DiffKAP package].


How to run?

  • Create your project configuration file by using the example config file. Here it is:


The command is:

   python pipeline.py 
        --s1 ./truncated/truncated_2.150.1sd/truncated_2.150.1sd.R1.fasta ./truncated/truncated_2.150.1sd/truncated_2.150.1sd.R2.fasta 
        --s2 ./whole/whole_2.150.1sd/whole_2.150.1sd.R1.fasta ./whole/whole_2.150.1sd/whole_2.150.1sd.R2.fasta 
        -c default.config  
        -o output_folder/


How to interpret the results?

  • You can download the results of the sample data here.


FAQ


Reference

  • Marçais, G. and Kingsford, C. (2011) A fast, lock-free approach for efficient parallel counting of occurrences of k-mers, Bioinformatics, 27, 764-770.


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