In the field of genomic medical research, the amount of large-scale information continues to increase due to advances in measurement technologies, such as high-performance sequencing and spatial omics, as well as the progress made in genomic cohort studies involving more than one million individuals. Therefore, researchers require more computational resources to analyze this information. Here, we introduce a hybrid cloud system consisting of an on-premise supercomputer, science cloud, and public cloud at the Kyoto University Center for Genomic Medicine in Japan as a solution. This system can flexibly handle various heterogeneous computational resource-demanding bioinformatics tools while scaling the computational capacity. In the hybrid cloud system, we demonstrate the way to properly perform joint genotyping of whole-genome sequencing data for a large population of 11,238, which can be a bottleneck in sequencing data analysis. This system can be one of the reference implementations when dealing with large amounts of genomic medical data in research centers and organizations.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9908893 | PMC |
| http://dx.doi.org/10.1038/s41439-023-00231-2 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Hybrid salp swarm maximum power point tracking algorithm for photovoltaic systems in highly fluctuating environmental conditions.
Sci Rep
January 2025
Department of Electrical Engineering, Imam Khomeini Naval Science University of Nowshahr, Nowshahr, Iran.
The maximum power delivered by a photovoltaic system is greatly influenced by atmospheric conditions such as irradiation and temperature and by surrounding objects like trees, raindrops, tall buildings, animal droppings, and clouds. The partial shading caused by these surrounding objects and the rapidly changing atmospheric parameters make maximum power point tracking (MPPT) challenging. This paper proposes a hybrid MPPT algorithm that combines the benefits of the salp swarm algorithm (SSA) and hill climbing (HC) techniques.
View Article and Find Full Text PDFA hybrid AI based framework for enhancing security in satellite based IoT networks using high performance computing architecture.
Sci Rep
December 2024
Computer Engineering Department, UET Taxila, Rawalpindi, Punjab, 47050, Pakistan.
IoT device security has become a major concern as a result of the rapid expansion of the Internet of Things (IoT) and the growing adoption of cloud computing for central monitoring and management. In order to provide centrally managed services each IoT device have to connect to their respective High-Performance Computing (HPC) clouds. The ever increasing deployment of Internet of Things (IoT) devices linked to HPC clouds use various medium such as wired and wireless.
View Article and Find Full Text PDFEpihiper-A high performance computational modeling framework to support epidemic science.
PNAS Nexus
January 2025
Biocomplexity Institute, University of Virginia, Charlottesville, VA, USA.
This paper describes Epihiper, a state-of-the-art, high performance computational modeling framework for epidemic science. The Epihiper modeling framework supports custom disease models, and can simulate epidemics over dynamic, large-scale networks while supporting modulation of the epidemic evolution through a set of user-programmable interventions. The nodes and edges of the social-contact network have customizable sets of static and dynamic attributes which allow the user to specify intervention target sets at a very fine-grained level; these also permit the network to be updated in response to nonpharmaceutical interventions, such as school closures.
View Article and Find Full Text PDFREAL-TIME RECURSIVE ESTIMATION OF, AND UNCERTAINTY QUANTIFICATION FOR, BREATH ALCOHOL CONCENTRATION VIA LQ TRACKING CONTROL-BASED INVERSE FILTERING OF TRANSDERMAL ALCOHOL BIOSENSOR SIGNALS.
Appl Math Mod Chall
March 2024
Department of Mathematics University of Southern California Los Angeles CA 90089-2532, USA.
The utility of newly developed wearable biosensors for passively, non-invasively, and continuously measuring transdermal alcohol levels in the body in real time has been limited by the fact that raw transdermal alcohol data does not consistently correlate (quantitatively or temporally) with interpretable metrics of breath and blood across individuals, devices, and the environment. A novel method using a population model in the form of a random abstract hybrid system of ordinary and partial differential equations and linear quadratic tracking control in Hilbert space is developed to estimate blood or breath alcohol concentration from the biosensor-produced transdermal alcohol level signal. Using human subject data in the form of 270 drinking episodes, the method is shown to produce estimates of blood or breath alcohol concentration that are highly correlated and thus good predictors of breath analyzer measurements.
View Article and Find Full Text PDFPHyPO: Priority-based Hybrid task Partitioning and Offloading in mobile computing using automated machine learning.
PLoS One
December 2024
School of Computing and Mathematical Sciences, Faculty of Engineering and Science, University of Greenwich, London, United Kingdom.
With the increasing demand for mobile computing, the requirement for intelligent resource management has also increased. Cloud computing lessens the energy consumption of user equipment, but it increases the latency of the system. Whereas edge computing reduces the latency along with the energy consumption, it has limited resources and cannot process bigger tasks.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!