Difference between revisions of "PAKAP"

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* Create your project configuration file by using the example config file. Here it is:
 
* Create your project configuration file by using the example config file. Here it is:
  
 +
<nowiki>
 +
[PIPELINE]
 +
# This should be the path to the directory containing the pipeline scripts.
 +
SCRIPTS_PATH = /ebi/bscratch/jarrah/uqphilippb/2014_4_17_FixPAV/CSR_pipeline
 +
# OPTIONAL: Path to reference
 +
REFERENCE_PATH = /ebi/bscratch/jarrah/uqphilippb/2014_4_17_FixPAV/CSR_pipeline/A.thaliana.whole.fasta
 +
# OPTIONAL: Path to gene annotation on reference
 +
GFF_PATH =
 +
 +
[JELLYFISH]
 +
# The path to the local installation of jellyfish (http://www.cbcb.umd.edu/software/jellyfish/)
 +
PATH = /work2/NCISF/MAS/W26/jarrah/apps/bin
 +
# The minimum k-mer size will be checked when KMER_SIZE=AUTO. Default is 5
 +
MIN_KMER_SIZE=5
 +
# The maximum k-mer size will be checked when KMER_SIZE=AUTO, jellyfish limits to the maximum k-mer size to 31. Default is 22
 +
MAX_KMER_SIZE=22
 +
 +
[IDENTIFYKMERSIZE]
 +
# Number of CPU will be used. Be aware of that the total amount of memory will be shared among all CPUs.
 +
NUM_OF_PROCESSOR=4
 +
# treatment 1 ID which will be used for naming files
 +
T1_ID=CON
 +
# treatment 2 ID which will be used for naming files
 +
T2_ID=STE
 +
OUT_DIR=/ebi/bscratch/jarrah/uqphilippb/2014_4_17_FixPAV/CSR_pipeline/
 +
 +
# directory storing treatment 1 data files. The data files can be fasta or fastq formats.
 +
DATA_DIR_T1=/ebi/bscratch/jarrah/uqphilippb/2014_4_17_FixPAV/CSR_pipeline/whole/whole_2.150.1sd/
 +
# directory storing treatment 2 data files. The data files can be fasta or fastq formats.
 +
DATA_DIR_T2=/ebi/bscratch/jarrah/uqphilippb/2014_4_17_FixPAV/CSR_pipeline/truncated/truncated_2.150.1sd/
 +
 +
[ADVANCED]
 +
# Important setting in jellyfish for tuning jellyfish performance. A larger hash size, more memory will be used
 +
#  but less sub-count files will be generated. Default is 10000000.
 +
JELLYFISH_HASH_SIZE=10000000
 +
# Size of the jellyfish hash table. Use a size large enough to contain all of the K-mers such that 80% * s > number of distinct K-mers. [Default: 10000000].
 +
#jellyfish_hash_size = 16G
 +
# Length of counter in the hash table. See jellyfish manual for explanation. [Default: 4]
 +
JELLYFISH_COUNTER_BITS = 18
 +
# Number of bases used for splitting the k-mer files into sub-files. A larger number reduces RAM usage. Maximum at the moment is 3. [Default: 2]
 +
N_SPLIT_BASES = 3
 +
# Minimum occurrence count for a k-mer to be considered as a candidate Presence / Absence K-mer. [Default: 4]
 +
MIN_OCC = 4<nowiki>
  
 
The command is:
 
The command is:

Revision as of 05:20, 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:
<nowiki>

[PIPELINE]

  1. This should be the path to the directory containing the pipeline scripts.

SCRIPTS_PATH = /ebi/bscratch/jarrah/uqphilippb/2014_4_17_FixPAV/CSR_pipeline

  1. OPTIONAL: Path to reference

REFERENCE_PATH = /ebi/bscratch/jarrah/uqphilippb/2014_4_17_FixPAV/CSR_pipeline/A.thaliana.whole.fasta

  1. OPTIONAL: Path to gene annotation on reference

GFF_PATH =

[JELLYFISH]

  1. The path to the local installation of jellyfish (http://www.cbcb.umd.edu/software/jellyfish/)

PATH = /work2/NCISF/MAS/W26/jarrah/apps/bin

  1. The minimum k-mer size will be checked when KMER_SIZE=AUTO. Default is 5

MIN_KMER_SIZE=5

  1. The maximum k-mer size will be checked when KMER_SIZE=AUTO, jellyfish limits to the maximum k-mer size to 31. Default is 22

MAX_KMER_SIZE=22

[IDENTIFYKMERSIZE]

  1. Number of CPU will be used. Be aware of that the total amount of memory will be shared among all CPUs.

NUM_OF_PROCESSOR=4

  1. treatment 1 ID which will be used for naming files

T1_ID=CON

  1. treatment 2 ID which will be used for naming files

T2_ID=STE OUT_DIR=/ebi/bscratch/jarrah/uqphilippb/2014_4_17_FixPAV/CSR_pipeline/

  1. directory storing treatment 1 data files. The data files can be fasta or fastq formats.

DATA_DIR_T1=/ebi/bscratch/jarrah/uqphilippb/2014_4_17_FixPAV/CSR_pipeline/whole/whole_2.150.1sd/

  1. directory storing treatment 2 data files. The data files can be fasta or fastq formats.

DATA_DIR_T2=/ebi/bscratch/jarrah/uqphilippb/2014_4_17_FixPAV/CSR_pipeline/truncated/truncated_2.150.1sd/

[ADVANCED]

  1. Important setting in jellyfish for tuning jellyfish performance. A larger hash size, more memory will be used
  2. but less sub-count files will be generated. Default is 10000000.

JELLYFISH_HASH_SIZE=10000000

  1. Size of the jellyfish hash table. Use a size large enough to contain all of the K-mers such that 80% * s > number of distinct K-mers. [Default: 10000000].
  2. jellyfish_hash_size = 16G
  3. Length of counter in the hash table. See jellyfish manual for explanation. [Default: 4]

JELLYFISH_COUNTER_BITS = 18

  1. Number of bases used for splitting the k-mer files into sub-files. A larger number reduces RAM usage. Maximum at the moment is 3. [Default: 2]

N_SPLIT_BASES = 3

  1. Minimum occurrence count for a k-mer to be considered as a candidate Presence / Absence K-mer. [Default: 4]

MIN_OCC = 4<nowiki>

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/

or, easier: 

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

This will read the configuration from default.config and generate all output files in 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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