COVID-19 Research Support on Biowulf

Covid-19 Research Support on Biowulf

Biowulf users with COVID-related projects should contact the HPC staff to get increased priority for their jobs.

Covid-Related Research on Biowulf

89.1+ Million CPU hours used
2.2+ Million jobs run
79 Peer-Reviewed papers
26 Preprint papers

Biowulf was used for screening of large virtual libraries containing billions of compounds and allowed for identification of small molecules that bind to SARS-CoV2 spike protein and inhibit viral infection. The image depicts one of such molecules bound to the receptor-binding domain of the protein.
[Nadya Tarasova, NCI]

3D structural model
of the coronavirus spike protein
[Mario Borgnia, NIEHS]

Coronavirus-related work being performed on Biowulf by NIH intramural researchers includes:

Covid-19 transmission modeling and crossover trials. Developing models for flow cytometry data that will be used to gain insights into cellular immune responses to COVID-19 [Dean Follman group, NIAID]
MD Simulations and Glycan Network Analysis of SARS-COV-2 spike protein [Bernard Brooks group, NHLBI]
Investigating the transcriptional regulators of hyper-inflammatory responses to SARs-CoV2 infection [Behdad Afzali group, NIDDK]
Structure of complexes of the SARS-CoV-2 ectodomain with a variety of ligands [Mario Borgnia & William Copeland group, NIEHS]
Surveillance sequencing of SARS-CoV-2 and tracking of emerging variants of concern, Microbiome analysis of respiratory samples from covid19 patients. [Elodie Ghedin group, NIAID]
Studying maturation of the SARS Cov2 main protease [Robert Best group, NIDDK]
Metagenomics of Covid-19 family [Xiaofang Jiang group, NLM]
Investigation of pathogenic characteristics of COVID-19 by analysis of RNA-seq data from human patients [Lothar Hennighausen group, NIDDK]
(1) Identification of drugs that can be repurposed for COVID-19 (2) Using genetic interactions like synthetic lethality and genome scale metabolic modeling to identify new drug targets and combination therapy for COVID-19 [Eytan Ruppin group, NCI]
Genome-wide association study of COVID-19 genetic variants vs. phenotypes in the UK BioBank [Andrew Johnson group, NHLBI]
Atomic-level structure of the SARS-CoV-2 spike using cryo-electron microscopy [Peter Kwong group, NIAID_VRC]
MD simulation of SARS-CoV-2 spike protein [Peter Kwong group, NIAID_VRC]
Genetic Determinants of Susceptibility to Severe COVID-19 Infection [NIAID Consortium. Director: Helen Su group, NIAID]
Image processing and text mining of clinical data for the NIAID COVID Data Management Working Group [Robert Hohman group, NIAID]
Modeling, global surveillance, real-time genomic sequencing and phylodynamics of SARS-CoV-2 [Cecile Viboud group, FIC]
Determining the incidence of age-related clonal hematopoiesis in mild vs. severe COVID-19 patient [Cynthia Dunbar group, NHLBI]
Studying Interferon-induced variability in ACE2 expression levels [Liudmila Prokunina-Olsson group, NCI]
Text mining of COVID-related scientific literature in LitCovid and CORD-19 [Zhiyong Lu group, NLM/NCBI]
SARS-CoV-2 RNA structural analyses [Markus Hafner group, NIAMS]
Deep Learning models of chest Xray and CT images [Ronald Summers group, CC]
Assessing newly generated and previously known compounds for activity against SARS-Cov-2 [Hiroaki Mitsuya group, NCI]
Stochastic modeling of COVID-19 pandemic infection patterns [Michael Stern group, NIA]
Text mining of COVID-related scientific literature in LitCovid and CORD-19 [Zhiyong Lu group, NLM/NCBI]
Cryo-electron microscopy of virus proteins and complexes [Natalia de Val & Eric Freed group, NCI_CCR]
Simulations of infection spread in populations [Vipul Periwal group, NIDDK]
In silico screening of drug candidates [Nadya Tarasova group, NCI]
Modeling unreported SARS-Cov2 infection from observed cases, computational modeling of Covid-19 biological systems. [Carson Chow group, NIDDK]
Transcriptomics and network analysis of SARS-CoV-2 infection [Mehdi Pirooznia & Robert Balaban group, NHLBI]
Genome-wide association studies to identify common and rare germline genetic variants associated with susceptibility to severe or fatal COVID-19 disease [Stephen Chanock group, NCI_DCEG]