UVA School of Medicine

Research Computing

Enabling scientific breakthroughs at scale with advanced computing


  • Bradycardia and Desaturation Events in Infants

    Episodes of bradycardia and oxygen desaturation (BD) are common among preterm very low birthweight (VLBW) infants and their association with adverse outcomes such as bronchopulmonary dysplasia (BPD) is unclear. A better understanding of this relationship could lead to improved clinical interventions. SOMRC is helping neonatologists describe BD events in a large single-NICU VLBW cohort and test the hypothesis that measures of BD in the neonatal period add to clinical variables to predict BPD or death and other adverse outcomes.
  • 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.
  • Center for Diabetes Technology PriMed

    In their research around constant glucose monitoring and the automated maintenance of insulin for patients, the CDT is exploring data drawn from external data sources such as DexCom and FitBit. SOMRC has assisted the CDT by designing a secure computing footprint in Amazon Web Services to pull in these data, parse and process them, in order to perform deeper analytics through machine learning. In January 2018, CDT sponsored a ski camp at Wintergreen Resort for a group of youth diagnosed with Type I diabetes with the goal of importing glucose, insulin, and exercise metrics at the end of each day through remote web APIs.
  • Functional Connectome Fingerprinting

    Functional magnetic resonance imaging (fMRI) can be used to assess functional activity in the brain and connectivity between different regions of interest (ROIs), and a functional connectome is a map of the interactions between ROIs. Previous research has shown that a functional connectome contains enough unique characteristics, not unlike a fingerprint, that it can be used for accurate identification of an individual subject from a large group. SOMRC is working with the UVA Functional Neuroradiology Lab to perform this fingerprinting analysis for a wide variety of populations and to develop innovative ways to visualize the results.
  • Heart Rate Ranges in Neonates

    There are limited evidence-based published heart rate ranges for premature neonates. However, knowing heart rate reference ranges in the premature neonatal population can be beneficial for bedside assessment in the Neonatal Intensive Care Unit (NICU). SOMRC is collaborating with clinical researchers in the Department of Pediatrics to establish baseline ranges for heart rate data in premature infants. These results are summarized from more than two billion data points collected via bedside monitoring in the NICU.
  • 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.
  • Microbiome Analysis of Hospital Sink Drains

    Sink drains are notoriously characterized as reservoirs of pathogens causing nosocomial transmissions in hospitals worldwide. Outbreaks where sinks have been implicated as source of antibiotic resistant bacteria have upsurged over the last few years. To understand transmission dynamics University of Virginia School of Medicine has established a unique “Sink Lab” for this research. This one-of-the kind laboratory establishes UVa as worldwide frontrunners in investigating sink related antibiotic resistant bacteria and how they spread.
  • PHACTR1 and Smooth Muscle Cell Behavior

    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. SOMRC is working with Redouane Aherrahrou (CPHG) who aims to study the impact of the CAD-associated genetic factors on the cellular and molecular SMC phenotypes.
  • Predicting ER Triage Levels with Machine Learning

    Before patients are admitted to the emergency room, they are assigned a triage level based on the severity of their health problems. This is accomplished using the Emergency Severity Index (ESI), an emergency department triage algorithm that classifies patient cases into five different levels of urgency. Researchers are interested in using machine learning to develop a model to predict patient triage level. This model would not only analyze the typical vital signs that are used in the ESI, but also demographic data and patients’ history of health.
  • Secure Computing for Surgical Research

    SOMRC is working with Dr. Eric Schneider to create a secure computing environment for the research of the Healthcare Surgical Outcome team. Data from this project will contain HIPAA identifiers, as well as Medicare information, and requires more security and control of data ingress/egress than projects previously hosted on the Ivy platform. After successful implementation of this project, SOMRC will create a similar computing environment for DoD blast and traumatic brain injury data collected by Dr.
  • Sonomicrometry Signal Classification

    Researchers are using sonomicrometry to study the biomechanics of the human brain. While at times the signals collected do not require any preprocessing, more frequently they do require some denoising or are too noisy to analyze. Currently, researchers are manually categorizing the quality of thousands of these sonomicrometry signals and preprocessing them individually. SOMRC is helping researchers develop a machine learning model to classify the signals and to determine the necessary preprocessing steps.
  • Transcription factor-chromatin Binding Dynamics

    Two important measures of the in vivo interaction of transcription factors with chromatin are the search time and the residence time. The former refers to the time it takes a factor to find its binding location, while the latter is the time the factor physically attaches to the chromatin. By quantifying the interaction dynamics of transcription factors, researchers hope to understand the role of these factors in basic cellular processes such as transcription and gene regulation.
  • epihet

    SOMRC is working with researchers in the Center for Public Health Genomics to write an R package to calculate Relative Proportion of Sites with Intermediate Methylation (RPIM) scores, which represent the epigenetic heterogeneity in a bisulfite sequencing sample. https://github.com/databio/epihet PI: Nathan Sheffield (Center for Public Health Genomics)
  • simpleCache

    In partnership with researchers in the Center for Public Health Genomics, School of Medicine Research Computing has contributed to the development of a novel package for computationally efficient caching and loading of data in R. simpleCache provides an interface to a series of functions to store and retrieve cached objects, including in the context batch processing or HPC environments. The package further extends base R functionality of saving and loading external representations of objects by enabling caching to pre-defined directories and timed cache operations.
  • 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.