CONNECTOMICS AND GENOMICS RESEARCH DESIGNATED HYBIRD GPU/CPU CLUSTER Project Summary/Abstract We request a high performance hybrid GPU/CPU computing cluster (HPC) to support the growing collaboration of connectomics and genomics studies at the University of Maryland School of Medicine (UMSOM). The equipment will be shared across the UMSOM. This inter-departmental resource will support a large portfolio of research projects among scientists at the Maryland Psychiatric Research Center (MPRC) in the Department of Psychiatry, the Department of Medicine Division of Endocrinology, Diabetes & Nutrition (DOM-EDN), and the Institute for Genomic Studies (IGS). The research programs within these centers are collaborative, complementary, and focused on analyses of high dimensional phenotype and genotype data. The combined research portfolio includes thirty one eligible projects (U01/54, P30/50/54 and R01 grants) separated into seventeen major and fourteen minor users. We highlight the recently awarded Amish Connectome Project (ACP) in Mental Illness that was funded through a Human Connectome in Disorders NIH initiative and Adolescent Brain Cognitive Development (ABCD) projects as examples of collaborative projects undertaken by our multidisciplinary science team. The ACP and ABCD projects high-dimensional connectomics and genomic data will be co-analyzed to identify genomic-connectomic-disorder pathways. Many similar projects flourish and cross-pollinate through a dedicated and shared computational resource support in our campus. The recent expansion of our research portfolio has prompted the need to upgrade of existing capabilities. Our team is uniquely position to operate such a resource because of its NIH-funded scientific software development methods for high-performance computing. The hybrid GPU/CPU analyses methods for imaging and genetic analysis software capable of performing massively parallel genomic analyses in imaging phenotypes to be tested using this resource will benefit researchers in other institutions that rely on our tools. Given the synergy and collaborative nature of our research we seek an HPC that can meet our collective needs rather than upgrading the current computing infrastructure for each program individually.
This application will support the acquisition of a hybrid GPU/CPU cluster and its integration into existing infrastructure to support ?Big Data? scientific research at the University Of Maryland School Of Medicine. This resource is essential to support collaborative, complementary and diverse NIH research projects in connectomics-based imaging, large-scale genome sequencing, and annotation efforts for genomes of bacterial and parasitic pathogens. This inter-institutional resource will also support the development of high-performance computational software that will be used by biomedical researchers across the country.
| Chen, Shuo; Xing, Yishi; Kang, Jian et al. (2018) Bayesian modeling of dependence in brain connectivity data. Biostatistics : |
| Chen, Yu-Han; Howell, Breannan; Edgar, J Christopher et al. (2018) Associations and Heritability of Auditory Encoding, Gray Matter, and Attention in Schizophrenia. Schizophr Bull : |
| Ryan, Meghann C; Kochunov, Peter; Sherman, Paul M et al. (2018) Miniature pig magnetic resonance spectroscopy model of normal adolescent brain development. J Neurosci Methods 308:173-182 |
| Adhikari, Bhim M; Jahanshad, Neda; Shukla, Dinesh et al. (2018) Comparison of heritability estimates on resting state fMRI connectivity phenotypes using the ENIGMA analysis pipeline. Hum Brain Mapp 39:4893-4902 |
| Petrov, Dmitry; Gutman, Boris A; Yu, Shih-Hua Julie et al. (2017) Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging. Mach Learn Med Imaging 10541:371-378 |
