Metadata management for high content screening in OMERO

Simon Li, Sébastien Besson, Colin Blackburn, Mark Carroll, Richard K. Ferguson, Helen Flynn, Kenneth Gillen, Roger Leigh, Dominik Lindner, Melissa Linkert, William J. Moore, Balaji Ramalingam, Emil Rozbicki, Gabriella Rustici, Aleksandra Tarkowska, Petr Walczysko, Eleanor Williams, Chris Allan, Jean Marie Burel, Josh MooreJason R. Swedlow

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)
187 Downloads (Pure)


High content screening (HCS) experiments create a classic data management challenge-multiple, large sets of heterogeneous structured and unstructured data, that must be integrated and linked to produce a set of "final" results. These different data include images, reagents, protocols, analytic output, and phenotypes, all of which must be stored, linked and made accessible for users, scientists, collaborators and where appropriate the wider community. The OME Consortium has built several open source tools for managing, linking and sharing these different types of data. The OME Data Model is a metadata specification that supports the image data and metadata recorded in HCS experiments. Bio-Formats is a Java library that reads recorded image data and metadata and includes support for several HCS screening systems. OMERO is an enterprise data management application that integrates image data, experimental and analytic metadata and makes them accessible for visualization, mining, sharing and downstream analysis. We discuss how Bio-Formats and OMERO handle these different data types, and how they can be used to integrate, link and share HCS experiments in facilities and public data repositories. OME specifications and software are open source and are available at.

Original languageEnglish
Pages (from-to)27-32
Number of pages6
Early online date22 Oct 2015
Publication statusPublished - 1 Mar 2016


  • Data management
  • HCS
  • Metadata
  • Screening


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