Analysis of genomic data has become highly complicated, and a focus of modern biomedical research. Individual labs have growing needs to analyze different types of genomic data in a customized fashion to meet unique research goals. No matter the genomic data were collected from their own experiments or public repositories, preparing them for custom analysis is very time-consuming and beyond the capacity of most biomedical researchers.
We are currently developing the Awsomics framework (http://awsomics.org) to make genomic data more accessible for custom data analysis. Awsomics has three major components. The backend is a growing archive of genomic information, covering from standard annotations (dbSNP, Entrez gene, OMIM, etc.) to public experimental data sets (GWAS, transcriptome, protein interaction, etc.) The frontend is a Shiny web server, hosting a series of generic and project-specific APPs. The middle layer implements existing and novel bioinformatics methods that perform integrative analysis of archived data and deliver the results to users through the web server. Running on an instance of Amazon Web Service (AWS), the whole framework is highly portable and can be easily deployed on other public or private servers.
The goal of Awsomics to promote exploratory analysis of complex genomic data and facilitates knowledge discovery in biomedical research. Its targeted users are biomedical scientists with unique needs of custom analysis, but lack time or resource to do so. It will help them to incorporate enormous genomic data in public repositories into their own research. The “bottom-up” and “data-centered” developing strategy of the framework makes it high flexible and expandible and the field of genome sciences keep evolving.
We have been developing the Awsomics framework for ~18 months. The archive of genomic data keeps growing, and currently includes over 10 major categories. For example:
- Over 7,000 results sets of GWAS analysis
- Close to 300 curated trascriptome data sets in 8 collections
- 3 million pre-defined gene sets
We have developed several generic bioinformatics APPs that support general exploratory analysis of archived data:
We also worked closely with several research labs to develop project-specific APPs. For example: