Tools used to process MVP data on Google Cloud Platform
This code was generated as part of the Department of Veteran's Affairs Million Veteran's Program. As part of this program, whole-genome sequencing was performed on genetic samples from 1902 individuals. Sequencing was done by Personalis and the data delivered to Bina for variant-calling and further analyses. These included generation of quality-control metrics, identification of copy-number variants, and analysis of structural variants. Results from Bina analyses were delivered to Stanford via encrypted tar archives uploaded to a shared Google Cloud Storage bucket.
The code here was used to decrypt and extract the results, validate the integrity of the data, and then generate cohort-level quality control (QC) metrics. Data integrity was verified by comparing sha1 checksums and file sizes against those reported by Bina. Three software packages were used to generate QC metrics; FastQC, Samtools Flagstat, and Real-time Genomcis VCFstats. Each analysis was performed on the individual sample level after which results from all samples were combined.
The code used to perform each operation has been added to individual Jupyter notebooks. Clone this repo, navigate to an task directory (i.e. fastqc-bam), and run the Jupyter notebook, to get started.
- Clone the mvp-on-gcp repo
git clone https://github.com/StanfordBioinformatics/mvp-on-gcp.git
- Create and activate a Python virtualenv (optional but strongly recommended).
# (You can do this in a directory of your choosing.)
virtualenv -p /usr/bin/python/3.5 --no-site-packages mvp_libs
source mvp_libs/bin/activate
- Install Jupyter notebooks and Jupyter kernel for bash
pip3 install jupyter
pip3 install bash_kernel
- Launch Jupyter notebook
jupyter notebook