Dataset Information

Bringing numerous methods for expression and promoter analysis to a public cloud computing service.

ABSTRACT:

Summary

Every year, a large number of novel algorithms are introduced to the scientific community for a myriad of applications, but using these across different research groups is often troublesome, due to suboptimal implementations and specific dependency requirements. This does not have to be the case, as public cloud computing services can easily house tractable implementations within self-contained dependency environments, making the methods easily accessible to a wider public. We have taken 14 popular methods, the majority related to expression data or promoter analysis, developed these up to a good implementation standard and housed the tools in isolated Docker containers which we integrated into the CyVerse Discovery Environment, making these easily usable for a wide community as part of the CyVerse UK project.

Availability and implementation

The integrated apps can be found at http://www.cyverse.org/discovery-environment, while the raw code is available at https://github.com/cyversewarwick and the corresponding Docker images are housed at https://hub.docker.com/r/cyversewarwick/.

Contact

info@cyverse.warwick.ac.uk or D.L.Wild@warwick.ac.uk.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Polanski K

PROVIDER: S-EPMC6030968 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Bringing numerous methods for expression and promoter analysis to a public cloud computing service.

Polanski Krzysztof K Gao Bo B Mason Sam A SA Brown Paul P Ott Sascha S Denby Katherine J KJ Wild David L DL

Bioinformatics (Oxford, England) 20180301 5

<h4>Summary</h4>Every year, a large number of novel algorithms are introduced to the scientific community for a myriad of applications, but using these across different research groups is often troublesome, due to suboptimal implementations and specific dependency requirements. This does not have to be the case, as public cloud computing services can easily house tractable implementations within self-contained dependency environments, making the methods easily accessible to a wider public. We ha ...[more]

PMID: 29126246

Similar Datasets

Project description:IntroductionHealthcare delivery systems across the world have been shown to fall short of the ideals of being cost-effective and meeting pre-established standards of quality but the problem is more pronounced in Africa. Cloud computing emerges as a platform healthcare institutions could leverage to address these shortfalls. The aim of this study was to establish the extent of cloud computing adoption and its influence on health service delivery by public health facilities in Kisumu County.MethodsThe study employed a cross-sectional study design in one-time data collection among facility in-charges and health records officers from 57 public health facilities. The target population was 114 healthcare personnel and the sample size (n = 88) was computed using Yamane formula and drawn using stratified random sampling. Poisson regression was used to determine the influence of cloud computing adoption on the number of realized benefits to health service delivery.ResultsAmong 80 respondents, Cloud computing had been adopted by 42 (53%) while Software-as-a-Service, Platform-as-a-Service and Infrastructure-as-a-Service implementations were at 100%, 0% and 5% among adopters, respectively. Overall, those who had adopted cloud computing realized a significantly higher number of benefits to health service delivery compared to those who had not (Incident-rate ratio (IRR) =1.93, 95% confidence interval (95% CI) [1.36-2.72]). A significantly higher number of benefits was realized by those who had implemented Infrastructure-as-a-Service alongside Software-as-a-Service (IRR = 2.22, 95% CI [1.15-4.29]) and those who had implemented Software-as-a-Service only (IRR = 1.89, 95% CI [1.33-2.70]) compared to non-adopters. We observed similar results in the stratified analysis looking at economic, operational, and functional benefits to health service delivery.ConclusionCloud computing resulted in improved health service delivery with these benefits still being realized irrespective of the service implementation model deployed. The findings buttress the need for healthcare institutions to adopt cloud computing and integrate it in their operations in order to improve health service delivery.

Dataset Information

Bringing numerous methods for expression and promoter analysis to a public cloud computing service.

Summary

Availability and implementation

Contact

Supplementary information

Publications

Bringing numerous methods for expression and promoter analysis to a public cloud computing service.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets