Toward interoperable bioscience data

Abstract

To make full use of research data, the bioscience community needs to adopt technologies and reward mechanisms that support interoperability and promote the growth of an open 'data commoning' culture. Here we describe the prerequisites for data commoning and present an established and growing ecosystem of solutions using the shared 'Investigation-Study-Assay' framework to support that vision.

Figure 1

The ISA framework in action in the stem cell–based system of the Harvard Stem Cell Institute (HSCI). The data management workflow of the HSCI’s Stem Cell Discovery Engine (SCDE) system, powered by the ISA framework. (a) Curators use the ISAconfigurator and ISAcreator software modules to consistently curate a variety of internally generated stem cell-based genomics profiles according to community-developed minimum information guidelines and terminologies; published transcriptomics-based studies are also collected via the MAGEtoISA module, then curated and enriched for consistency. (b) Consistently represented investigations are loaded in the BioInvestigation Index (BII) component that stores and serves the (public and private) data sets to the HSCI and wider community. (c) Upon publication, investigations are directly submitted to those public repositories using ISA-Tab format, or converted to/from other supported formats via the ISAconverter.

Figure 2

Building the ‘ISA commons’, a growing ecosystem of resources that work to provide a data commons. (a) Data sets of interest to each community are collected and curated. (b) Capture systems, either powered by the ISA software suite or supporting the hierarchical ISA-Tab structure, deliver a common representation of experimental content that transcends individual domains. (c) To achieve broader data integration, the next step is to explore the growing Linked Data universe. The European Innovative Medicines Initiative (IMI) Open PHACTS project, for example, will use semantic web approaches to make existing knowledge available for linking, querying and where possible, reasoning. This project will benefit greatly from study descriptions that draw on the ISA model to connect quantified information held in semantic triple stores to data from actual experiments performed. As a result, the project will connect public and private datasets to genomics resources, enabling the combination of existing and new experimental data.