High-Performance Computing at the NIH
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The NIH HPC group plans, manages and supports high-performance computing systems specifically for the intramural NIH community. These systems include Biowulf, an 20,000+ processor Linux cluster; Helix, an interactive system for short jobs, 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.

Recent Papers that used Biowulf & HPC Resources

thumbnail image from paper A digital 3D atlas of the marmoset brain based on multi-modal MRI
Liu, C; Ye, FQ; Chern-Chyi Yen, C et al.
Neuroimage , DOI://10.1016/j.neuroimage.2017.12.004 (2017)

thumbnail image from paper OCT4 supports extended LIF-independent self-renewal and maintenance of transcriptional and epigenetic networks in embryonic stem cells
He, R; Xhabija, B; Al-Qanber, B; Kidder, BL;
Sci Rep , DOI://10.1038/s41598-017-16611-y (2017)

Power Analysis for Genetic Association Test (PAGEANT) provides insights to challenges for rare variant association studies
Derkach, A; Zhang, H; Chatterjee, N;
Bioinformatics , DOI://10.1093/bioinformatics/btx770 (2017)

thumbnail image from paper Demystifying P2Y1 Receptor Ligand Recognition through Docking and Molecular Dynamics Analyses
Ciancetta, A; O'Connor, RD; Paoletta, S; Jacobson, KA;
J Chem Inf Model , DOI://10.1021/acs.jcim.7b00528 (2017)

thumbnail image from paper A STAT3-dependent transcriptional circuitry inhibits cytotoxic gene expression in T cells
Ciucci, T; Vacchio, MS; Bosselut, R;
Proc. Natl. Acad. Sci. U.S.A. , DOI://10.1073/pnas.1711160114 (2017)

Natural Language Processing for Large-Scale Medical Image Analysis Using Deep Learning
Hoo-Chang Shin, Le Lu, Ronald M. Summers
in Deep Learning for Medical Image Analysis, Elsevier , (p405-421) (2017)

thumbnail image from paper Excessive burden of lysosomal storage disorder gene variants in Parkinson's disease
Robak, LA; Jansen, IE; van Rooij, J et al.
Brain , DOI://10.1093/brain/awx285 (2017)