GenAMap is an open-source platform for visual machine learning of genome-phenome associations.

The proliferation of genomic data has increased the usefulness of complex machine learning algorithms for the next generation of genome-wide association studies (GWAS). These methods effectively relate genetic polymorphisms with phenotypes, but they require algorithmic expertise to run code and domain expertise to analyze results. To overcome these challenges, the GenAMap software platform has been developed to provide an intuitive, visual interface for next-generation GWAS. The user experience is an intuitive web application with a focus on simplicity and ease of use. For use with private or large data, it is easy to set up on your own server with a simple Docker image for all dependencies. Please contact us with any questions.

Documentation:

To get started, see the docs, which include usage examples and development information.

Installation:

To install GenAMap locally, first you need to Install Docker, and then execute the following in your terminal:

curl https://raw.githubusercontent.com/blengerich/GenAMap/master/Documentation/Installation/run_genamap.sh > run_genamap.sh
chmod +x run_genamap.sh
./run_genamap.sh $DATA_FOLDER $CONFIG_FOLDER

where $DATA_FOLDER is the folder where your dataset is and $CONFIG_FOLDER is the folder where your authorization file that contains administrator email and password is. These should be absolute, not relative paths.

Further, we have to note that if you want to use your own data, please follow the GenAMap/Documentation/Development/README.md.

After that, you can enjoy the next-generation GWAS by visiting localhost on Linux. On Mac, this is 192.168.99.100, or 0.0.0.0 if your docker version > 1.2.x