LabKey Server

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LabKey Server
Developer(s)LabKey
Stable release
19.2[1] / July 22, 2019; 2 months ago (2019-07-22)
Written inJava
Operating systemCross-platform
LicenseApache License 2.0
Websitehttp://www.labkey.com

LabKey Server is a software suite available for scientists to integrate, analyze, and share biomedical research data. The platform provides a secure data repository that allows web-based querying, reporting, and collaborating across a range of data sources. Specific scientific applications and workflows can be added on top of the basic platform and leverage a data processing pipeline.

License[edit]

LabKey licenses LabKey Server and its documentation for free under the Apache License.[2]

Languages and extensibility[edit]

The base platform is written in Java. It can be extended through the addition of Java-based modules or simple, file-based modules written in HTML, XML and JavaScript.[3] The platform can also be extended using LabKey Server's Java, JavaScript, R, Python, Perl and SAS client libraries.[4]

History[edit]

LabKey Server, originally known as the Computational Proteomics Analysis System (CPAS), was developed at the Fred Hutchinson Cancer Research Center to manage high volumes of data generated at the Fred Hutch Computational Proteomics Lab. In 2005, a small team spun out of the Hutch and began operating independently as LabKey Software after contributors realized that the software could be beneficial to the broader scientific community.[5][6][7]

Core Components[edit]

LabKey Server provides a secure data repository for all types of biomedical data, including mass spectrometry, flow cytometry, microarray, microplate, ELISpot, ELISA, NAb and observational study information. A customizable data processing pipeline allows the upload and processing of the large data files common in biomedical research.

The platform also provides domain-specific support for several areas of research, including:

  • Observational Studies. Supports management of longitudinal, large-scale studies of participants, subjects or animals over time. Allows the integration of clinical data with assay results.
  • Proteomics. Allows the processing of high-throughput mass spectrometry data using tools such as the X! Tandem search engine, the Trans-Proteomic Pipeline, Mascot and Sequest. Certified as "Silver-Level Compliant Data Service" with the caBIG standard.
  • Flow Cytometry. Supports automated quality control, centralized data management and web-based data sharing. Integrates with FlowJo.

Zika Open Research Portal[edit]

In 2016, LabKey and Professor Dave O'Connor of the University of Wisconsin–Madison launched the Zika Open Research Portal [1] using LabKey Server. The portal provides direct access to experiment data being produced by members of the Zika Experimental Science Team (ZEST). The portal received attention from the scientific community for being the first platform of its kind to share real-time research data. [8][9]

Open Source Software[edit]

Labkey is licensed in a variety of manners. Source-code is provided for a core set of features with the Community Edition, and there are also Premium Editions available [10].

Users[edit]

Users range from individual labs to large research consortia. In 2017, the program's users included the following:[11]

Publications[edit]

  • Nelson, Elizabeth K; Piehler, Britt; Eckels, Josh; Rauch, Adam; Bellew, Matthew; Hussey, Peter; Ramsay, Sarah; Nathe, Cory; Lum, Karl; Krouse, Kevin; Stearns, David; Connolly, Brian; Skillman, Tom; Igra, Mark (2011). "LabKey Server: An open source platform for scientific data integration, analysis and collaboration". BMC Bioinformatics. 12: 71. doi:10.1186/1471-2105-12-71. PMC 3062597. PMID 21385461.
  • Rauch, Adam; Bellew, Matthew; Eng, Jimmy; Fitzgibbon, Matthew; Holzman, Ted; Hussey, Peter; Igra, Mark; MacLean, Brendan; et al. (2006). "Computational Proteomics Analysis System (CPAS): An Extensible, Open-Source Analytic System for Evaluating and Publishing Proteomic Data and High Throughput Biological Experiments". Journal of Proteome Research. 5 (1): 112–21. doi:10.1021/pr0503533. PMID 16396501.
  • Shulman, Nicholas; Bellew, Matthew; Snelling, George; Carter, Donald; Huang, Yunda; Li, Hongli; Self, Steven G.; McElrath, M. Juliana; De Rosa, Stephen C. (2008). "Development of an automated analysis system for data from flow cytometric intracellular cytokine staining assays from clinical vaccine trials". Cytometry Part A. 73A (9): 847. doi:10.1002/cyto.a.20600. PMC 2591089.
  • "The Best of Both Worlds: Integrating a Java Web Application with SAS Using the SAS/SHARE Driver for JDBC" (PDF).

References[edit]

External links[edit]