Abstract
BackgroundOne of the primary challenges in translational research data management is breaking down the barriers between the multiple data silos and the integration of 'omics data with clinical information to complete the cycle from the bench to the bedside. The role of contextual metadata, also called provenance information, is a key factor ineffective data integration, reproducibility of results, correct attribution of original source, and answering research queries involving "What", "Where", "When", "Which", "Who", "How", and "Why" (also known as the W7 model). But, at present there is limited or no effective approach to managing and leveraging provenance information for integrating data across studies or projects. Hence, there is an urgent need for a paradigm shift in creating a "provenance-aware" informatics platform to address this challenge. We introduce an ontology-driven, intuitive Semantic Proteomics Dashboard (SemPoD) that uses provenance together with domain information (semantic provenance) to enable researchers to query, compare, and correlate different types of data across multiple projects, and allow integration with legacy data to support their ongoing research.ResultsThe SemPoD platform, currently in use at the Case Center for Proteomics and Bioinformatics (CPB), consists of three components: (a) Ontology-driven Visual Query Composer, (b) Result Explorer, and (c) Query Manager. Currently, SemPoD allows provenance-aware querying of 1153 mass-spectrometry experiments from 20 different projects. SemPod uses the systems molecular biology provenance ontology (SysPro) to support a dynamic query composition interface, which automatically updates the components of the query interface based on previous user selections and efficientlyprunes the result set usinga "smart filtering" approach. The SysPro ontology re-uses terms from the PROV-ontology (PROV-O) being developed by the World Wide Web Consortium (W3C) provenance working group, the minimum information required for reporting a molecular interaction experiment (MIMIx), and the minimum information about a proteomics experiment (MIAPE) guidelines. The SemPoD was evaluated both in terms of user feedback and as scalability of the system.ConclusionsSemPoD is an intuitive and powerful provenance ontology-driven data access and query platform that uses the MIAPE and MIMIx metadata guideline to create an integrated view over large-scale systems molecular biology datasets. SemPoD leverages the SysPro ontology to create an intuitive dashboard for biologists to compose queries, explore the results, and use a query manager for storing queries for later use. SemPoD can be deployed over many existing database applications storing 'omics data, including, as illustrated here, the LabKey data-management system. The initial user feedback evaluating the usability and functionality of SemPoD has been very positive and it is being considered for wider deployment beyond the proteomics domain, and in other 'omics' centers.
Highlights
One of the primary challenges in translational research data management is breaking down the barriers between the multiple data silos and the integration of ‘omics data with clinical information to complete the cycle from the bench to the bedside
We use two principal proteomics workflows used in the Center for Proteomics and Bioinformatics (CPB) as exemplars to describe the design and implementation of Semantic Proteomics Dashboard (SemPoD), namely: 1. The first workflow is affinity-purification mass-spectrometry (AP-MS) workflow that enables the identification of specific protein complexes, identifying proteins that are associated with one another
SemPoD has been deployed at the CPB and has been in use for over 2 months
Summary
One of the primary challenges in translational research data management is breaking down the barriers between the multiple data silos and the integration of ‘omics data with clinical information to complete the cycle from the bench to the bedside. The lack of an effective query platform is a key reason that once the ‘omics data has been acquired, analyzed and interpreted, the data is typically archived and serves no further process. This is important issue both in terms of maximizing the return on research funding and ensuring that the value of ‘omics data can be significantly increased if that data is carefully integrated into a growing corpus of data that can be re-used in different contexts. In response to a newly published finding that Single-Nucleotide Polymorphism (SNP) in gene Y are associated with disease X, the researcher wants to query all of her legacy data and ask
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