LabPipe: an extensible bioinformatics toolkit to manage experimental data and metadata
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Open Access
SOFTWARE
LabPipe: an extensible bioinformatics toolkit to manage experimental data and metadata Bo Zhao1,2, Luke Bryant2,3, Rebecca Cordell2,3, Michael Wilde2,3, Dahlia Salman5, Dorota Ruszkiewicz5, Wadah Ibrahim1, Amisha Singapuri2, Tim Coats3,4, Erol Gaillard1, Caroline Beardsmore1, Toru Suzuki2,4, Leong Ng2,4, Neil Greening2, Paul Thomas5, Paul Monks3, Christopher Brightling1,2, Salman Siddiqui1,2 and Robert C. Free1,2*
*Correspondence: [email protected] 1 Department of Respiratory Sciences, University of Leicester, Leicester, UK Full list of author information is available at the end of the article
Abstract Background: Data handling in clinical bioinformatics is often inadequate. No freely available tools provide straightforward approaches for consistent, flexible metadata collection and linkage of related experimental data generated locally by vendor software. Results: To address this problem, we created LabPipe, a flexible toolkit which is driven through a local client that runs alongside vendor software and connects to a lightweight server. The toolkit allows re-usable configurations to be defined for experiment metadata and local data collection, and handles metadata entry and linkage of data. LabPipe was piloted in a multi-site clinical breathomics study. Conclusions: LabPipe provided a consistent, controlled approach for handling metadata and experimental data collection, collation and linkage in the exemplar study and was flexible enough to deal effectively with different data handling challenges. Keywords: Metadata, Data management, Biomedical, Breathomics
Background A key challenge in clinical bioinformatics is handling the collation and collection of experimental data sets from multiple sites and research groups. While some vendor software is fully automated and provides an end-to-end system for collecting data and metadata, in many cases this is not the case and software is closed, proprietary and limited with a lack of external connectivity. This means data management is often a manual adhoc process, which leads to an approach that is inadequate, slow and potentially errorridden [1]. While there are open source tools available which improve on this situation [2, 3], our team required a tool which was flexible enough to be able to handle multiple different configurations for metadata entry and experimental data linkage depending on the data being collected and the vendor software being used on local PCs.
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