https://github.com/blengerich/genamap
Visual Machine Learning of Genome-Phenome Associations
https://github.com/blengerich/genamap
bioinformatics gwas structured-association-mapping
Last synced: about 1 month ago
JSON representation
Visual Machine Learning of Genome-Phenome Associations
- Host: GitHub
- URL: https://github.com/blengerich/genamap
- Owner: blengerich
- License: mit
- Created: 2016-01-29T00:58:42.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2022-10-04T21:33:17.000Z (about 3 years ago)
- Last Synced: 2025-04-21T21:43:04.793Z (6 months ago)
- Topics: bioinformatics, gwas, structured-association-mapping
- Language: C++
- Homepage: http://genamap.org
- Size: 652 MB
- Stars: 22
- Watchers: 19
- Forks: 7
- Open Issues: 26
-
Metadata Files:
- Readme: README.md
- License: License.md
Awesome Lists containing this project
README
[](http://genamap.org)
[](https://github.com/blengerich/GenAMap/blob/master/License.md) [](https://github.com/blengerich/GenAMap/pulls) [](https://github.com/blengerich/GenAMap/issues)
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](http://hub.docker.com/r/blengerich/genamap) for all dependencies. Please contact us with any questions.
## Documentation:
To get started, see the [docs](https://github.com/blengerich/GenAMap/tree/master/Documentation), which include [usage examples](https://github.com/blengerich/GenAMap/tree/master/Documentation/ExampleData) and [development information](https://github.com/blengerich/GenAMap/tree/master/Documentation/Development).
## Installation:
To install GenAMap locally, first you need to [Install Docker](https://docs.docker.com/engine/installation/), and then execute the following in your terminal:
```shell
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
[//]: # (## Contact:)
[//]: # (Have a question about GenAMap? Email us at genamap.team@gmail.com. To help us to get to know you better, please provide your name and affiliation when requesting support. We also have a [Google group] for users.)