To get to work without too much reading just use ./run.sh and follow the directions provided.
If anything is not clear, try the wiki first:
https://github.com/rstraver/wisecondor/wiki
WISECONDOR was developed and tested using Python2.7. Using any other version may cause errors or faulty results. The working version is tested using SAMTOOLS on .bam files created by BWA. Original work was based on SOAP2 files, and any SOAP2 mapped input stream should work as well although it requires additional arguments when using consam.py
Using Ubuntu, this should obtain the required packages:
sudo apt-get install python-numpy python-matplotlib python-biopython
For any other unix based system, if easy_install is installed, you should be able to install all of these using this command in a terminal:
sudo easy_install numpy matplotlib biopython
To start testing for aberrations, WISECONDOR needs to learn how genomic regions behave when compared to eachother. To do so, reference data should be prepared in the same way as the test sample data later on, using the same genomic reference, the same GC-correction, etc.
Settings we used for external tools:
bwa aln -n 0 -k 0
bwa samse -n -1
picardtools sortsam SO=coordinate VALIDATION_STRINGENCY=LENIENT CREATE_INDEX=true
samtools rmdup -s sample.bam - | samtools view - -q 1
To convert a .bam file to a file for analysis, pipe the output from the samtools step shown above into consam.py:
samtools rmdup -s sample.bam - | samtools view - -q 1 | python consam.py -outfile sample.pickle
To create a GC-count table for the genomic reference you use, use countgc.py:
python countgc.py path/to/reference.fa ./ref/gccount
Applying the GC-correction should be done for any sample file you have:
python ./gcc.py ./in/sample.pickle ./ref/gccount ./in/sample.gcc
Then, all reference files should be fed into the newref.py script. The current implementation used all samples in a single folder, so after applying all your corrections to your data, move the reference samples into a separate folder and start the reference-build script:
python newref.py ./in/refs/ ./ref/reference
To improve your results you probably want to change a few parameters. Most of the values used in WISECONDOR can be altered using arguments. To find out what arguments can be passed into any script, try running it with -h as argument, for example:
python test.py -h
To create plots, use the file created by test.py as input for plot.py. This script turns the prepared data into a visualization, which can be customized or replaced to accommodate for personal preferences without the need to make changes to the original algorithm.
In addition the the PDF file as created using plot.py, it is also possible to export the results to a JSON object which can be loaded into the view.html provided in the WISECONDOR package. To do this, run makejson.py on an output file from test.py and save the result to a file using the '>' function:
python makejson.py sampleName.out > sampleName.json
However, due to requiring additional data, the output files are now larger in filesize as they contain all data available on the sample rather than just the results. The average filesize is about 3,1 MB now. Note: the viewer is designed and tested for Google Chrome, any other browser is not supported (but may work). Also, although it is a webpage, there is no uploading of data or calling home involved, to comply with diagnostics. Everything is fully local on the machine you use it on. For more information, check out the WISECONDOR wiki pages on github.
Do not use any of the example data for your own tests, if available. Every reference file, laboratory and sequencing machine has its own effect on how read depth per bin behaves. Any results obtained by combining files from different origins are unreliable.