Pub Date : 2012-12-14DOI: 10.1109/BIOMEDCOM.2012.8
R. Flight, Abdallah M Eteleeb, E. Rouchka
Affymetrix® GeneChip® micro array design defines probe sets consisting of 11, 16, or 20 distinct 25 base pair (BP) probes for determining mRNA expression for a specific gene, which may be covered by one or more probe sets. Each probe has a corresponding perfect match (PM) and mismatch (MM) set. Traditional analytical techniques have either used the MM probes to determine the level of cross-hybridization or reliability of the PM probe, or have been completely ignored. Given the availability of reference genome sequences, we have reanalyzed the mapping of both PM and MM probes to reference genomes in transcript regions. Our results suggest that depending of the species of interest, 66%-93% of the PM probes can be used reliably in terms of single unique matches to the genome, while a small number of the MM probes (typically less than 1%) could be incorporated into the analysis. In addition, we have examined the mapping of PM and MM probes to five different human genome projects, resulting in approximately a 70% overlap of uniquely mapping PM probes, and a subset of 51 uniquely mapping MM probes commonly found in all five projects, 24 of which are found within annotated exonic regions. These results suggest that individual variation in transcriptome regions provides an additional complexity to micro array data analysis. Given these results, we conclude that the development of custom chip definition files (CDFs) should include MM probe sequences to provide the most effective means of transcriptome analysis of Affymetrix® GeneChip® arrays.
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Pub Date : 2012-12-14DOI: 10.1109/BIOMEDCOM.2012.29
K. Hashizume, E. Fernández, M. Larrondo-Petrie
The three primary types of cloud computing services are Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS). IaaS delivers computer infrastructure including servers, storage and network. PaaS offers the computer platform as a service which facilitates development and deployment of applications. In SaaS, applications are hosted and maintained by a cloud provider and delivered to the users as services on demand. We have developed two patterns for cloud delivery services: IaaS and PaaS patterns. We develop here a pattern for Software-as-a-Service to complete all the three cloud service levels. These patterns will be used to study cloud security requirements.
{"title":"A Pattern for Software-as-a-Service in Clouds","authors":"K. Hashizume, E. Fernández, M. Larrondo-Petrie","doi":"10.1109/BIOMEDCOM.2012.29","DOIUrl":"https://doi.org/10.1109/BIOMEDCOM.2012.29","url":null,"abstract":"The three primary types of cloud computing services are Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS). IaaS delivers computer infrastructure including servers, storage and network. PaaS offers the computer platform as a service which facilitates development and deployment of applications. In SaaS, applications are hosted and maintained by a cloud provider and delivered to the users as services on demand. We have developed two patterns for cloud delivery services: IaaS and PaaS patterns. We develop here a pattern for Software-as-a-Service to complete all the three cloud service levels. These patterns will be used to study cloud security requirements.","PeriodicalId":146495,"journal":{"name":"2012 ASE/IEEE International Conference on BioMedical Computing (BioMedCom)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125586135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Molecular dynamics (MD) simulations provide a molecular-resolution physical description of the folding and assembly processes, but the size and the timescales of simulations are limited because the underlying algorithm is computationally demanding. We recently introduced a parallel neighbor list algorithm that was specifically optimized for MD simulations on GPUs. In our present study, we analyze the performance of the algorithm in our MD simulation software, and we observe that the major of the overall execution time is spent performing the force calculations and the evaluation of the neighbor list and pair lists. The overall speedup of the GPU-optimized MD simulations as compared to the CPU-optimized version is N-dependent and ~30x for the full 70s ribosome (10,219 beads). The pair and neighbor list evaluations have performance speedups of ~25x and ~55x, respectively. We then make direct How biomolecules fold and assemble into well-defined structures that correspond to cellular functions is a fundamental problem in biophysics with direct biomedical application because some functions lead to diseases such as Alzheimer's, Parkinson's, and cancer. Molecular dynamics (MD) simulations provide a molecular-resolution physical description of the folding and assembly processes, but the computational demands of the algorithms restrict the size and the timescales one can simulate. In a recent study, we introduced a parallel neighbor list algorithm that was specifically optimized for MD simulations on GPUs. We now analyze the performance of our MD simulation code that incorporates the algorithm, and we observe that the force calculations and the evaluation of the neighbor list and pair lists constitutes a majority of the overall execution time. The overall speedup of the GPU-optimized MD simulations as compared to the CPU-optimized version is N-dependent and ~30x for the full 70s ribosome (10,219 beads). The pair and neighbor list evaluations have performance speedups of ~25x and ~55x, respectively. We then make direct comparisons with the performance of our MD simulation code with that of the SOP model implemented in the simulation code of HOOMD, a leading general particle dynamics simulation package that is specifically optimized for GPUs.
{"title":"Performance Analyses of a Parallel Verlet Neighbor List Algorithm for GPU-Optimized MD Simulations","authors":"Tyson J. Lipscomb, Anqi Zou, Samuel S. Cho","doi":"10.1145/2382936.2382977","DOIUrl":"https://doi.org/10.1145/2382936.2382977","url":null,"abstract":"Molecular dynamics (MD) simulations provide a molecular-resolution physical description of the folding and assembly processes, but the size and the timescales of simulations are limited because the underlying algorithm is computationally demanding. We recently introduced a parallel neighbor list algorithm that was specifically optimized for MD simulations on GPUs. In our present study, we analyze the performance of the algorithm in our MD simulation software, and we observe that the major of the overall execution time is spent performing the force calculations and the evaluation of the neighbor list and pair lists. The overall speedup of the GPU-optimized MD simulations as compared to the CPU-optimized version is N-dependent and ~30x for the full 70s ribosome (10,219 beads). The pair and neighbor list evaluations have performance speedups of ~25x and ~55x, respectively. We then make direct How biomolecules fold and assemble into well-defined structures that correspond to cellular functions is a fundamental problem in biophysics with direct biomedical application because some functions lead to diseases such as Alzheimer's, Parkinson's, and cancer. Molecular dynamics (MD) simulations provide a molecular-resolution physical description of the folding and assembly processes, but the computational demands of the algorithms restrict the size and the timescales one can simulate. In a recent study, we introduced a parallel neighbor list algorithm that was specifically optimized for MD simulations on GPUs. We now analyze the performance of our MD simulation code that incorporates the algorithm, and we observe that the force calculations and the evaluation of the neighbor list and pair lists constitutes a majority of the overall execution time. The overall speedup of the GPU-optimized MD simulations as compared to the CPU-optimized version is N-dependent and ~30x for the full 70s ribosome (10,219 beads). The pair and neighbor list evaluations have performance speedups of ~25x and ~55x, respectively. We then make direct comparisons with the performance of our MD simulation code with that of the SOP model implemented in the simulation code of HOOMD, a leading general particle dynamics simulation package that is specifically optimized for GPUs.","PeriodicalId":146495,"journal":{"name":"2012 ASE/IEEE International Conference on BioMedical Computing (BioMedCom)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132028812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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