The intent of GEMINI (GEnome MINIing) is to provide a simple, flexible, and
powerful framework for exploring genetic variation for personal and medical genetics.
GEMINI is unique in that it integrated genetic variation (from VCF files) with
a wealth of genome annotations into a unified database framework. Using this
integrated database as the analysis framework, we aim to leverage the expressive
power of SQL for data analysis, while attempting to overcome the fundamental
challenges associated with using databases for very large
(e.g. 1,000,000 variants times 1,000 samples yields one billion genotypes)
datasets.
The official documentation is here: http://gemini.readthedocs.org/en/latest/
The following is a video of a high-level talk from SciPy 2013 describing GEMINI.
http://www.youtube.com/watch?v=p-UWmDG6yj4
If you use GEMINI in your research, please cite the following manuscript:
Paila U, Chapman BA, Kirchner R, Quinlan AR (2013) GEMINI: Integrative Exploration of Genetic Variation and Genome Annotations. PLoS Comput Biol 9(7): e1003153. doi:10.1371/journal.pcbi.1003153
Questions and discussion should be directed to the following discussion list:
https://groups.google.com/forum/?fromgroups#!forum/gemini-variation
GEMINI is being developed in the Quinlan lab (quinlanlab.org) at the University of Virginia and is led by Uma Paila and Aaron Quinlan. Substantial contributions to the code base have also been made by Brad Chapman (@chapmanb) and Rory Kirchner (@roryk) at the Harvard School of Public Health.
Install GEMINI using the automated installation script, gemini_install.py. This
script installs GEMINI along with required python libraries, third party tools and data
files used for variant annotation. The installation documentation contains additional
details on installed files and tools.
http://gemini.readthedocs.org/en/latest/content/installation.html
GEMINI is freely available under the MIT license.
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