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CHPC - Research Computing and Data Support for the University

In addition to deploying and operating high performance computational resources and providing advanced user support and training, CHPC serves as an expert team to broadly support the increasingly diverse research computing and data needs on campus. These needs include support for big data, big data movement, data analytics, security, virtual machines, Windows science application servers, protected environments for data mining and analysis of protected health information, and advanced networking.

If you are new to CHPC, the best place to start to get more information on CHPC resources and policies is our Getting Started page.

Upcoming Events:

CHPC Downtime: Tuesday March 5 starting at 7:30am

Posted February 8th, 2024


Two upcoming security related changes

Posted February 6th, 2024


Allocation Requests for Spring 2024 are Due March 1st, 2024

Posted February 1st, 2024


CHPC ANNOUNCEMENT: Change in top level home directory permission settings

Posted December 14th, 2023


CHPC Spring 2024 Presentation Schedule Now Available

CHPC PE DOWNTIME: Partial Protected Environment Downtime  -- Oct 24-25, 2023

Posted October 18th, 2023


CHPC INFORMATION: MATLAB and Ansys updates

Posted September 22, 2023


CHPC SECURITY REMINDER

Posted September 8th, 2023

CHPC is reaching out to remind our users of their responsibility to understand what the software being used is doing, especially software that you download, install, or compile yourself. Read More...

News History...

Column Basis Vectors comparing Normal Tissue Bin Types to Tumor Bins

Comparative spectral decompositions, such as the GSVD, underlie a mathematically universal description of the genotype-phenotype relations in cancer

By Katherine A. Aiello1,2, Sri Priya Ponnapalli1, and Orly Alter1,2,3

1Scientific Computing and Imaging Institute, 2Department of Bioengineering, 3Huntsman Cancer Institute and Department of Human Genetics, University of Utah

Abstract: DNA alterations have been observed in astrocytoma for decades. A copy-number genotype predictive of a survival phenotype was only discovered by using the generalized singular value decomposition (GSVD) formulated as a comparative spectral decomposition. Here, we use the GSVD to compare whole-genome sequencing (WGS) profiles of patient-matched astrocytoma and normal DNA. First, the GSVD uncovers a genome-wide pattern of copy-number alterations, which is bounded by patterns recently uncovered by the GSVDs of microarray-profiled patient-matched glioblastoma (GBM) and, separately, lower-grade astrocytoma and normal genomes. Like the microarray patterns, the WGS pattern is correlated with an approximately one-year median survival time. By filling in gaps in the microarray patterns, the WGS pattern reveals that this biologically consistent genotype encodes for transformation via the Notch together with the Ras and Shh pathways. Second, like the GSVDs of the microarray profiles, the GSVD of the WGS profiles separates the tumor-exclusive pattern from normal copy-number variations and experimental inconsistencies. These include the WGS technology-specific effects of guanine-cytosine content variations across the genomes that are correlated with experimental batches. Third, by identifying the biologically consistent phenotype among the WGS-profiled tumors, the GBM pattern proves to be a technology-independent predictor of survival and response to chemotherapy and radiation, statistically better than the patient's age and tumor's grade, the best other indicators, and MGMT promoter methylation and IDH1 mutation. We conclude that by using the complex structure of the data, comparative spectral decompositions underlie a mathematically universal description of the genotype-phenotype relations in cancer that other methods miss.

Read the article in APL Bioengineering.

System Status

General Environment

last update: 2024-02-29 20:23:05
General Nodes
system cores % util.
kingspeak 644/972 66.26%
notchpeak 3089/3212 96.17%
lonepeak 1408/2804 50.21%
Owner/Restricted Nodes
system cores % util.
ash 1152/1152 100%
notchpeak 14132/18156 77.84%
kingspeak 312/5468 5.71%
lonepeak 0/416 0%

Protected Environment

last update: 2024-02-29 20:20:04
General Nodes
system cores % util.
redwood 368/616 59.74%
Owner/Restricted Nodes
system cores % util.
redwood 1240/6064 20.45%


Cluster Utilization

Last Updated: 2/20/24