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CHPC - Research Computing 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 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. Visit our Getting Started page for more information.

CHPC Fall 2019 Newsletter 

K80 GPUs on notchpeak-shared-short partition

Posted October 4th, 2019

CHPC DOWNTIME: OS kernel updates on Clusters

  • (COMPLETED) October 8thstarting at 7:30
    Compute and interactive nodes onlonepeak, kingspeak, tangent, ash, and redwood.  Includes the frisco, atmos and meteo nodes
  • (COMPLETED) September 25th starting at 7:30
    Compute and interactive nodes on ember and notchpeak 

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

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: 2019-12-13 00:23:01
General Nodes
system cores % util.
ember 876/876 100%
kingspeak 736/816 90.2%
notchpeak 1076/1076 100%
lonepeak 488/2252 21.67%
Owner/Restricted Nodes
system cores % util.
ash 6736/7436 90.59%
notchpeak 2663/4256 62.57%
ember 716/1220 58.69%
kingspeak 4872/5812 83.83%
lonepeak 220/400 55%

Protected Environment

last update: 2019-12-13 00:20:02
General Nodes
system cores % util.
redwood 28/408 6.86%
Owner/Restricted Nodes
system cores % util.
redwood 704/3744 18.8%

Cluster Utilization

Last Updated: 12/5/19