Pseudo-cgh of next-generation sequencing data
Project description
Overview
Next-generation sequencing of tumor/normal pairs provides a good opportunity to examine large-scale copy number variation in the tumor relative to the normal sample. In practice, this concept seems to extend even to exome-capture sequencing of pairs of tumor and normal. This library consists of a single script, ngCGH, that computes a pseudo-CGH using simple coverage counting on the tumor relative to the normal.
I have chosen to use a fixed number of reads in the normal sample as the “windowing” approach. This has the advantage of producing copy number estimates that should have similar variance at each location. The algorithm will adaptively deal with inhomogeneities across the genome such as those associated with exome-capture technologies (to the extent that the capture was similar in both tumor and normal). The disadvantage is that the pseudo-probes will be at different locations for every “normal control” sample.
Installation
There are several possible ways to install ngCGH.
github
If you are a git user, then simply cloning the repository will get you the latest code.
git clone git://github.com/seandavi/ngCGH.git
Alternatively, click the Download button and get the tarball or zip file.
In either case, change into the resulting directory and:
cd ngCGH python setup.py install
From PyPi
If you have easy_install in place, this should suffice for installation:
easy_install ngCGH
Usage
Usage is very simple:
$ ngCGH -h usage: ngCGH [-h] [-w WINDOWSIZE] [-o OUTFILE] [-l LOGLEVEL] normalbam tumorbam positional arguments: normalbam The name of the bamfile for the normal comparison tumorbam The name of the tumor sample bamfile optional arguments: -h, --help show this help message and exit -w WINDOWSIZE, --windowsize WINDOWSIZE The number of reads captured from the normal sample for calculation of copy number -o OUTFILE, --outfile OUTFILE Output filename, default <stdout> -l LOGLEVEL, --loglevel LOGLEVEL Logging Level, 1-15 with 1 being minimal logging and 15 being everything [10]
Output
The output format is also very simple:
chr1 4851 52735 1000 854 -0.025120 chr1 52736 59251 1000 812 -0.097876 chr1 59251 119119 1000 876 0.011575 chr1 119120 707038 1000 1087 0.322924 chr1 707040 711128 1000 1016 0.225472 chr1 711128 711375 1000 1059 0.285275 chr1 711375 735366 1000 919 0.080709 chr1 735368 798455 1000 972 0.161600
Columns 1-3 describe the chromosome, start, and end for each pseudo-probe. The fourth column is the number of reads in the normal sample in the window while the fifth column represents the reads in the same genomic window from the tumor. The last column contains the median-centered log2 ratio between tumor and normal.
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