Dataset Information

ViPAR: a software platform for the Virtual Pooling and Analysis of Research Data.

ABSTRACT: Research studies exploring the determinants of disease require sufficient statistical power to detect meaningful effects. Sample size is often increased through centralized pooling of disparately located datasets, though ethical, privacy and data ownership issues can often hamper this process. Methods that facilitate the sharing of research data that are sympathetic with these issues and which allow flexible and detailed statistical analyses are therefore in critical need. We have created a software platform for the Virtual Pooling and Analysis of Research data (ViPAR), which employs free and open source methods to provide researchers with a web-based platform to analyse datasets housed in disparate locations.Database federation permits controlled access to remotely located datasets from a central location. The Secure Shell protocol allows data to be securely exchanged between devices over an insecure network. ViPAR combines these free technologies into a solution that facilitates 'virtual pooling' where data can be temporarily pooled into computer memory and made available for analysis without the need for permanent central storage.Within the ViPAR infrastructure, remote sites manage their own harmonized research dataset in a database hosted at their site, while a central server hosts the data federation component and a secure analysis portal. When an analysis is initiated, requested data are retrieved from each remote site and virtually pooled at the central site. The data are then analysed by statistical software and, on completion, results of the analysis are returned to the user and the virtually pooled data are removed from memory.ViPAR is a secure, flexible and powerful analysis platform built on open source technology that is currently in use by large international consortia, and is made publicly available at [http://bioinformatics.childhealthresearch.org.au/software/vipar/].

SUBMITTER: Carter KW

PROVIDER: S-EPMC4864874 | biostudies-other | 2016 Apr

REPOSITORIES: biostudies-other

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Publications

ViPAR: a software platform for the Virtual Pooling and Analysis of Research Data.

Carter Kim W KW Francis Richard W RW Carter K W KW Francis R W RW Bresnahan M M Gissler M M Grønborg T K TK Gross R R Gunnes N N Hammond G G Hornig M M Hultman C M CM Huttunen J J Langridge A A Leonard H H Newman S S Parner E T ET Petersson G G Reichenberg A A Sandin S S Schendel D E DE Schalkwyk L L Sourander A A Steadman C C Stoltenberg C C Suominen A A Surén P P Susser E E Sylvester Vethanayagam A A Yusof Z Z

International journal of epidemiology 20151008 2

<h4>Background</h4>Research studies exploring the determinants of disease require sufficient statistical power to detect meaningful effects. Sample size is often increased through centralized pooling of disparately located datasets, though ethical, privacy and data ownership issues can often hamper this process. Methods that facilitate the sharing of research data that are sympathetic with these issues and which allow flexible and detailed statistical analyses are therefore in critical need. We ...[more]

PMID: 26452388

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Project description:BackgroundElectroanatomic mapping systems are used to support electrophysiology research. Data exported from these systems is stored in proprietary formats which are challenging to access and storage-space inefficient. No previous work has made available an open-source platform for parsing and interrogating this data in a standardized format. We therefore sought to develop a standardized, open-source data structure and associated computer code to store electroanatomic mapping data in a space-efficient and easily accessible manner.MethodsA data structure was defined capturing the available anatomic and electrical data. OpenEP, implemented in MATLAB, was developed to parse and interrogate this data. Functions are provided for analysis of chamber geometry, activation mapping, conduction velocity mapping, voltage mapping, ablation sites, and electrograms as well as visualization and input/output functions. Performance benchmarking for data import and storage was performed. Data import and analysis validation was performed for chamber geometry, activation mapping, voltage mapping and ablation representation. Finally, systematic analysis of electrophysiology literature was performed to determine the suitability of OpenEP for contemporary electrophysiology research.ResultsThe average time to parse clinical datasets was 400 ± 162 s per patient. OpenEP data was two orders of magnitude smaller than compressed clinical data (OpenEP: 20.5 ± 8.7 Mb, vs clinical: 1.46 ± 0.77 Gb). OpenEP-derived geometry metrics were correlated with the same clinical metrics (Area: R 2 = 0.7726, P < 0.0001; Volume: R 2 = 0.5179, P < 0.0001). Investigating the cause of systematic bias in these correlations revealed OpenEP to outperform the clinical platform in recovering accurate values. Both activation and voltage mapping data created with OpenEP were correlated with clinical values (mean voltage R 2 = 0.8708, P < 0.001; local activation time R 2 = 0.8892, P < 0.0001). OpenEP provides the processing necessary for 87 of 92 qualitatively assessed analysis techniques (95%) and 119 of 136 quantitatively assessed analysis techniques (88%) in a contemporary cohort of mapping studies.ConclusionsWe present the OpenEP framework for evaluating electroanatomic mapping data. OpenEP provides the core functionality necessary to conduct electroanatomic mapping research. We demonstrate that OpenEP is both space-efficient and accurately representative of the original data. We show that OpenEP captures the majority of data required for contemporary electroanatomic mapping-based electrophysiology research and propose a roadmap for future development.

Project description:Modern biomedical research aims at drawing biological conclusions from large, highly complex biological datasets. It has become common practice to make extensive use of high-throughput technologies that produce big amounts of heterogeneous data. In addition to the ever-improving accuracy, methods are getting faster and cheaper, resulting in a steadily increasing need for scalable data management and easily accessible means of analysis. We present qPortal, a platform providing users with an intuitive way to manage and analyze quantitative biological data. The backend leverages a variety of concepts and technologies, such as relational databases, data stores, data models and means of data transfer, as well as front-end solutions to give users access to data management and easy-to-use analysis options. Users are empowered to conduct their experiments from the experimental design to the visualization of their results through the platform. Here, we illustrate the feature-rich portal by simulating a biomedical study based on publically available data. We demonstrate the software's strength in supporting the entire project life cycle. The software supports the project design and registration, empowers users to do all-digital project management and finally provides means to perform analysis. We compare our approach to Galaxy, one of the most widely used scientific workflow and analysis platforms in computational biology. Application of both systems to a small case study shows the differences between a data-driven approach (qPortal) and a workflow-driven approach (Galaxy). qPortal, a one-stop-shop solution for biomedical projects offers up-to-date analysis pipelines, quality control workflows, and visualization tools. Through intensive user interactions, appropriate data models have been developed. These models build the foundation of our biological data management system and provide possibilities to annotate data, query metadata for statistics and future re-analysis on high-performance computing systems via coupling of workflow management systems. Integration of project and data management as well as workflow resources in one place present clear advantages over existing solutions.

Dataset Information

ViPAR: a software platform for the Virtual Pooling and Analysis of Research Data.

Publications

ViPAR: a software platform for the Virtual Pooling and Analysis of Research Data.

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