Dataset Information

Developing and Deploying a Scalable Computing Platform to Support MOOC Education in Clinical Data Science.

ABSTRACT: One of the challenges of teaching applied data science courses is managing individual students' local computing environment. This is especially challenging when teaching massively open online courses (MOOCs) where students come from across the globe and have a variety of access to and types of computing systems. There are additional challenges with using sensitive health information for clinical data science education. Here we describe the development and performance of a computing platform developed to support a series of MOOCs in clinical data science. This platform was designed to restrict and log all access to health datasets while also being scalable, accessible, secure, privacy preserving, and easy to access. Over the 19 months the platform has been live it has supported the computation of more than 2300 students from 101 countries.

SUBMITTER: Mayer D

PROVIDER: S-EPMC8378646 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Developing and Deploying a Scalable Computing Platform to Support MOOC Education in Clinical Data Science.

Mayer David D Russell Seth S Wilson Melissa P MP Kahn Michael G MG Wiley Laura K LK

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science 20210517

One of the challenges of teaching applied data science courses is managing individual students' local computing environment. This is especially challenging when teaching massively open online courses (MOOCs) where students come from across the globe and have a variety of access to and types of computing systems. There are additional challenges with using sensitive health information for clinical data science education. Here we describe the development and performance of a computing platform deve ...[more]

PMID: 34457158

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Project description:BackgroundHeterogeneous omics data, increasingly collected through high-throughput technologies, can contain hidden answers to very important and still unsolved biomedical questions. Their integration and processing are crucial mostly for tertiary analysis of Next Generation Sequencing data, although suitable big data strategies still address mainly primary and secondary analysis. Hence, there is a pressing need for algorithms specifically designed to explore big omics datasets, capable of ensuring scalability and interoperability, possibly relying on high-performance computing infrastructures.ResultsWe propose RGMQL, a R/Bioconductor package conceived to provide a set of specialized functions to extract, combine, process and compare omics datasets and their metadata from different and differently localized sources. RGMQL is built over the GenoMetric Query Language (GMQL) data management and computational engine, and can leverage its open curated repository as well as its cloud-based resources, with the possibility of outsourcing computational tasks to GMQL remote services. Furthermore, it overcomes the limits of the GMQL declarative syntax, by guaranteeing a procedural approach in dealing with omics data within the R/Bioconductor environment. But mostly, it provides full interoperability with other packages of the R/Bioconductor framework and extensibility over the most used genomic data structures and processing functions.ConclusionsRGMQL is able to combine the query expressiveness and computational efficiency of GMQL with a complete processing flow in the R environment, being a fully integrated extension of the R/Bioconductor framework. Here we provide three fully reproducible example use cases of biological relevance that are particularly explanatory of its flexibility of use and interoperability with other R/Bioconductor packages. They show how RGMQL can easily scale up from local to parallel and cloud computing while it combines and analyzes heterogeneous omics data from local or remote datasets, both public and private, in a completely transparent way to the user.

Dataset Information

Developing and Deploying a Scalable Computing Platform to Support MOOC Education in Clinical Data Science.

Publications

Developing and Deploying a Scalable Computing Platform to Support MOOC Education in Clinical Data Science.

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