UVA School of Medicine

Research Computing

Enabling scientific breakthroughs at scale with advanced computing


  • Cardiovascular Genomics

    Coronary artery disease (CAD) is the major cause of morbidity and mortality worldwide. Recent genome wide association studies (GWAS) have revealed more than 50 genomic loci that are associated with increased risk for CAD. However, the pathological mechanisms for majority of the GWAS loci leading to increased susceptibility to this complex disorder are still unclear. Many of the CAD loci appear to act through the vessel wall, presumably affecting smooth muscle cell (SMC) function.
  • Moving Big Data

    School of Medicine Research Computing works with researchers in the UVA Center for Public Health Genomics, to transfer large genomics datasets from partner institutions. Using Globus, an asynchronous data transfer utility (created at Argonne Laboratory and based on GridFTP), transfers of data larger than 40TB has been made easier and more reliable. Such large transfers benefit from dedicated, high-speed connectivity between Internet2 member institutions like UVA, Cornell University, and Washington University in St.
  • Bioinformatics & Genomics

    School of Medicine Research Computing (SOMRC) provides state-of-the-art resources and expertise in handling and analyzing genomics and metagenomics data. Bioinformatics is a quickly evolving field with new biological and computational techniques being formalized and adopted at a fast pace. Hence, the following is only a brief cross-section of the ways researchers can use SOMRC’s expertise and computing resources for their bioinformatics research. Next-generation sequence data analyis SOMRC can support in-depth analysis of various next-generation sequencing experiment datasets on your desktop/Rivanna/Ivy/Cloud.
  • Bioinformatics Resources on Rivanna

    UVA research community has access to numerous bioinformatics software installed and ready-to-use on Rivanna. They are all available via the LMod module system. In addition, Click here for a comprehensive list. Popular Bioinformatics Software Below are some popular tools and useful links for their documentation and usage: .tg {border-collapse:collapse;border-spacing:0;border-color:#ccc;} .tg td{font-family:Arial, sans-serif;font-size:14px;padding:10px 5px;border-style:solid;border-width:0px;overflow:hidden;word-break:normal;border-color:#ccc;color:#333;background-color:#fff;} .tg th{font-family:Arial, sans-serif;font-size:14px;font-weight:normal;padding:10px 5px;border-style:solid;border-width:0px;overflow:hidden;word-break:normal;border-color:#ccc;color:#333;background-color:#f0f0f0;} .tg .tg-hy9w{background-color:#eceeef;border-color:inherit;vertical-align:middle;} .tg .tg-dc35{background-color:#f9f9f9;border-color:inherit;vertical-align:middle;} .tg .tg-hy9w-nw{background-color:#eceeef;border-color:inherit;vertical-align:middle;white-space:nowrap;} .tg .tg-dc35-nw{background-color:#f9f9f9;border-color:inherit;vertical-align:middle;white-space:nowrap;} .tg .tg-0qmj{font-weight:bold;background-color:#eceeef;border-color:inherit;vertical-align:middle;} .scroll thead, .
  • Bioinformatics User Guides

    Bioinformatics on Rivanna UVA’s High-performance Computing Cluster All faculty, research staff and graduate students of UVA have access to Rivanna, university’s high-performance computing system with 290+ compute nodes (6500+ cores) for high-throughput multithreaded jobs, parallel jobs as well as memmory intensive large-scale data analyses. The architecture is specifically suited for large scale distributed genomic data analysis, with 100+ bioinformatics software packages installed and ready to use. Learn more Bioinformatics using FireCloud FireCloud Home