Correlation Analysis on Gene Expression - Finding genes with similar expression.
FlyCAGE is a bioinformatics web application that uses the correlation coefficient between gene expression profiles to retrieve insights about gene similarity.
Visit flycage.herokuapp.com to try out our tool.
Visit http://flycage.herokuapp.com/help to learn how to use our tool.
Are you a developer interested in contributing to science, research, or medicine!? Scroll below to see more information on how to contribute to FlyCAGE!
Common transcriptomic analysis methods such as differential gene expression analysis and agglomertive gene clustering are excellent ways to analyze transcriptomic data. However, the analysis from these methods are limited to the data retrieved from the experiment and does not utilize the "big-data" available.
Non-technical: most methods that analyze the activity of genes do not take advantage of public datasets.
FlyCAGE is a web-accessible application that uses several public data sources to analyze fly mRNA expression data to discover genes with similar expression patterns.
Non-technical: FlyCAGE tries to find genes with similar gene activity.
Due to its cost-effectiveness, many genetics labs will retrieve the gene expression profiles to understand more about the underlying experiment. This retrieval is done through RNA-seq or microarrays. There are several simple ways to analyze gene expression data (DESeq, GSEA, etc.). We hope that FlyCAGE will be another tool that scientists will use to easily retrieve insights about their experimental data (i.e. identify candidate genes involved in a given process, to discover regulatory interactions in genetic networks, and to verify the quality of gene clustering techniques).
Non-technical: Many biology labs can easily obtain data on gene activity. There are several simple ways to analyze this data - we believe that FlyCAGE can be another method scientists can add to their toolbox.
Interested in the next steps of FlyCAGE? View the ROADMAP.md
Specifically, we are looking for the following:
- Technologies consists of HTML, CSS, JS, Bootstrap 4, jQuery, Plotly.js, Datatables.
- We are interested in aesthetic revamping, migration towards a framework (i.e. Angular), improve result visualization, etc.
- No biology knowledge required.
- Technologies consist of Java, Spring MVC (Boot), Thymeleaf, asynchronous Spring programming, WS and DB communication, etc.
- We are interested in defining web endpoints, rendering dynamic information, biologic data source communication, implementation of biological analysis, deployment and maintanence, etc.
- Biology knowledge preferred for context, but not required.
- We are interested in designing statistical methods to conduct analysis.
- Biology knowledge preferred for context, but not required.
- Any scripting language preferred.
- We are interested in designing or implementing bioinformatics algorithms to conduct analysis.
- Biology knowledge necessary (in as many -omics topics as possible).
- We are interested in providing ideas for desired features of the application
- Any discipline of life sciences desired.
Although, any sort of help on the project would be much appreciated!
If you are interested in contributing, please see the CONTRIBUTING.md for more information!
This project adheres to the Contributor Covenant code of conduct. By participating, you are expected to uphold this code.
Ready to start developing? Take a look at our workstation setup.
For any questions about the project, please contact the main developer at bbecer2@ilstu.edu