HyperGen

HyperGen©: Empowering Genomics Research through Streamlined Pipelines

HyperGen is a powerful web application designed to automate complex bioinformatics pipelines and streamline the genomics analysis process for researchers at the University of Rochester, University of Rochester Medical Center, and the Wilmot Cancer Institute.

Translating the analysis of bioinformatics pipelines into self-service no-code workflows, HyperGen puts the power of genomics analysis directly in the hands of the researcher. By lowering the entry barrier to complex genomics analysis through a user-friendly interface, researchers are empowered to directly analyze data on demand with the additional ability to control analytical conditions like defining analysis set comparisons.

Features:

• Analysis Suites- HyperGen contains analysis suites for RNA-Seq bulk transcriptomics differential gene expression and Whole Genome Sequence analysis.  Currently integrated workflows include: DESeq2, GATK4, and OpenCRAVAT.
• Data Upload and Management- Researchers can upload and process data files, which are then securely stored on the server using a Python-based API.
• Automated Analysis and Visualization- Insights are automatically generated based on the workflow. Researchers can view or download results, simplifying dissemination of research findings. DESeq2 generates an HTML report as well as charts, images and data from the analysis. Unmapped BAM files used in the GATK4 process create VCF files, which can be uploaded to OpenCRAVAT for analysis and visualization.
• Pipeline Management- Upon login, researchers can immediately view their previous pipeline runs. This provides an organized overview of completed tasks, aiding in tracking and referencing past analysis.
• User Authentication- Data security and authorized platform access is ensured by integration with the University's Active Directory authentication system.
• Conda and R-Powered Backend- HyperGen integrates R's powerful analytical capabilities into DESeq2 analysis. DESeq2’s backend is implemented using RPlumber, a framework turning R code into web APIs, permitting seamless execution of the DESeq2 workflow. GATK4 is run using a Conda server allowing WDL workflows to be run in a Conda environment. OpenCRAVAT is integrated using Python.

The HyperGen analytical platform also collaboratively supports the UR Medical Center’s Genomics Research Core services, freeing up highly skilled bioinformaticists from high-volume data processing to innovate on new analytical pipelines. As we look to expand the HyperGen suite in the future, we strive to continuously improve the efficiency and accessibility of genomics analysis tools for our research community.