Biowulf High Performance Computing at the NIH

The NIH HPC group plans, manages and supports high-performance computing systems specifically for the intramural NIH community. These systems include Biowulf, a 105,000+ processor Linux cluster; Helix, an interactive system for file transfer and management, Sciware, a set of applications for desktops, and Helixweb, which provides a number of web-based scientific tools. We provide access to a wide range of computational applications for genomics, molecular and structural biology, mathematical and graphical analysis, image analysis, and other scientific fields.

Current Status    All Services Operational

COVID-19 Research Support

77.2+ Million CPU hours used
1.7+ Million jobs run

Sample projects (All projects):

  • (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 [NCI]
  • 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 [NIAID]
  • Metagenomics of Covid-19 family [NLM]
  • Assessing newly generated and previously known compounds for activity against SARS-Cov-2 [NCI]
  • Simulations of infection spread in populations [NIDDK]
  • Modeling unreported SARS-Cov2 infection from observed cases, computational modeling of Covid-19 biological systems. [NIDDK]
  • Genome-wide association study of COVID-19 genetic variants vs. phenotypes in the UK BioBank [NHLBI]
Biowulf users with COVID-related projects should contact the HPC staff to get increased priority for their jobs.

Quick Links

Biowulf Utilization
Friday, September 17th, 2021
utilization graph
Last 24 hrs
112,894 jobs submitted
82,757 jobs completed
3,912,569 CPU hrs used
24 NIH Institutes
241 Principal Investigators
483 users

Announcements
Recent Papers that used Biowulf & HPC Resources

thumbnail image from paper Immune cell deconvolution of bulk DNA methylation data reveals an association with methylation class, key somatic alterations, and cell state in glial/glioneuronal tumors
Singh, O; Pratt, D; Aldape, K; ,
Acta Neuropathol Commun , DOI://10.1186/s40478-021-01249-9 (2021)

thumbnail image from paper Toward best practice in cancer mutation detection with whole-genome and whole-exome sequencing
Xiao, W; Ren, L; Chen, Z et al.
Nat Biotechnol , DOI://10.1038/s41587-021-00994-5 (2021)

thumbnail image from paper Skin Metagenomic Sequence Analysis of Early Candida auris Outbreaks in U.S. Nursing Homes
Huang, X; Welsh, RM; Deming, C et al.
mSphere , DOI://10.1128/mSphere.00287-21 (2021)

thumbnail image from paper Toward Reducing hERG Affinities for DAT Inhibitors with a Combined Machine Learning and Molecular Modeling Approach
Lee, KH; Fant, AD; Guo, J et al.
J Chem Inf Model , DOI://10.1021/acs.jcim.1c00856 (2021)

Identification of human long noncoding RNAs associated with nonalcoholic fatty liver disease and metabolic homeostasis
Ruan, X; Li, P; Ma, Y et al.
J Clin Invest , DOI://10.1172/JCI136336 (2021)

thumbnail image from paper Magnetization transfer weighted EPI facilitates cortical depth determination in native fMRI space
Chai, Y; Li, L; Wang, Y et al.
Neuroimage , DOI://10.1016/j.neuroimage.2021.118455 (2021)

thumbnail image from paper A Novel ARL3 Gene Mutation Associated With Autosomal Dominant Retinal Degeneration
Ratnapriya, R; Jacobson, SG; Cideciyan, AV et al.
Front Cell Dev Biol , DOI://10.3389/fcell.2021.720782 (2021)

thumbnail image from paper Determinants of conductance of a bacterial voltage-gated sodium channel
Chen, AY; Brooks, BR; Damjanovic, A; ,
Biophys J , DOI://10.1016/j.bpj.2021.06.013 (2021)

thumbnail image from paper Trans-ethnic and Ancestry-Specific Blood-Cell Genetics in 746,667 Individuals from 5 Global Populations
Chen, MH; Raffield, LM; Mousas, A et al.
Cell , DOI://10.1016/j.cell.2020.06.045 (2020)

thumbnail image from paper Genetic risk scores for cardiometabolic traits in sub-Saharan African populations
Ekoru, K; Adeyemo, AA; Chen, G et al.
Int J Epidemiol , DOI://10.1093/ije/dyab046 (2021)