/tag/shiny
OmegaSync
The Computing Hardware Research Lab (CHRL) worked with the DAC to develop a pipeline connecting a web app to HPC resources for solving computationally hard combinatorial optimization problems such as computing the MaxCut in complex graphs using OmegaSync. The DAC created an RShiny app running on Kubernetes that collects user information and graph files. The app formats data, saves it to the HPC filesystem, and automates job submissions. It also triggers email notifications to users upon job start and completion, providing the results they need. This project highlights the DAC’s role in supporting faculty with complex research workflows.
PI: Nikhil Shukla, PhD (Department of Electrical and Computer Engineering)
Workshops
UVA Research Computing provides training opportunities covering a variety of data analysis, basic programming and computational topics. All of the classes listed below are taught by experts and are freely available to UVa faculty, staff and students.
New to High-Performance Computing? We have core training that is essential to getting up to speed working in the UVA HPC environment. We offer virtual orientation sessions to introduce you to the Afton & Rivanna HPC systems. Trainings are hosted Wednesdays. Registration is required.
The training material is hosted on our learning page where you can find a full YouTube video series and the workshop content of our core training.
education,
workshops
bioinformatics,
containers,
HPC,
image processing,
Ivy,
Matlab,
programming,
Python,
R,
Rivanna,
Shiny
LOLAweb
The past few years have seen an explosion of interest in understanding the role of regulatory DNA. This interest has driven large-scale production of functional genomics data resources and analytical methods. One popular analysis is to test for enrichment of overlaps between a query set of genomic regions and a database of region sets. In this way, annotations from external data sources can be easily connected to new genomic data.
SOM Research Computing is working with faculty in the UVA Center for Public Health Genomics to implement LOLAweb, an online tool for performing genomic locus overlap annotations and analyses. This project, written in the statistical programming language R, allows users to specify region set data in BED format for automated enrichment analysis.
