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Metadata management for high content screening in OMERO

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 outpu...

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Autores principales: Li, Simon, Besson, Sébastien, Blackburn, Colin, Carroll, Mark, Ferguson, Richard K., Flynn, Helen, Gillen, Kenneth, Leigh, Roger, Lindner, Dominik, Linkert, Melissa, Moore, William J., Ramalingam, Balaji, Rozbicki, Emil, Rustici, Gabriella, Tarkowska, Aleksandra, Walczysko, Petr, Williams, Eleanor, Allan, Chris, Burel, Jean-Marie, Moore, Josh, Swedlow, Jason R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Academic Press 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4773399/
https://www.ncbi.nlm.nih.gov/pubmed/26476368
http://dx.doi.org/10.1016/j.ymeth.2015.10.006
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author Li, Simon
Besson, Sébastien
Blackburn, Colin
Carroll, Mark
Ferguson, Richard K.
Flynn, Helen
Gillen, Kenneth
Leigh, Roger
Lindner, Dominik
Linkert, Melissa
Moore, William J.
Ramalingam, Balaji
Rozbicki, Emil
Rustici, Gabriella
Tarkowska, Aleksandra
Walczysko, Petr
Williams, Eleanor
Allan, Chris
Burel, Jean-Marie
Moore, Josh
Swedlow, Jason R.
author_facet Li, Simon
Besson, Sébastien
Blackburn, Colin
Carroll, Mark
Ferguson, Richard K.
Flynn, Helen
Gillen, Kenneth
Leigh, Roger
Lindner, Dominik
Linkert, Melissa
Moore, William J.
Ramalingam, Balaji
Rozbicki, Emil
Rustici, Gabriella
Tarkowska, Aleksandra
Walczysko, Petr
Williams, Eleanor
Allan, Chris
Burel, Jean-Marie
Moore, Josh
Swedlow, Jason R.
author_sort Li, Simon
collection PubMed
description 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 https://www.openmicroscopy.org.
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spelling pubmed-47733992016-03-14 Metadata management for high content screening in OMERO Li, Simon Besson, Sébastien Blackburn, Colin Carroll, Mark Ferguson, Richard K. Flynn, Helen Gillen, Kenneth Leigh, Roger Lindner, Dominik Linkert, Melissa Moore, William J. Ramalingam, Balaji Rozbicki, Emil Rustici, Gabriella Tarkowska, Aleksandra Walczysko, Petr Williams, Eleanor Allan, Chris Burel, Jean-Marie Moore, Josh Swedlow, Jason R. Methods Article 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 https://www.openmicroscopy.org. Academic Press 2016-03-01 /pmc/articles/PMC4773399/ /pubmed/26476368 http://dx.doi.org/10.1016/j.ymeth.2015.10.006 Text en © 2015 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Li, Simon
Besson, Sébastien
Blackburn, Colin
Carroll, Mark
Ferguson, Richard K.
Flynn, Helen
Gillen, Kenneth
Leigh, Roger
Lindner, Dominik
Linkert, Melissa
Moore, William J.
Ramalingam, Balaji
Rozbicki, Emil
Rustici, Gabriella
Tarkowska, Aleksandra
Walczysko, Petr
Williams, Eleanor
Allan, Chris
Burel, Jean-Marie
Moore, Josh
Swedlow, Jason R.
Metadata management for high content screening in OMERO
title Metadata management for high content screening in OMERO
title_full Metadata management for high content screening in OMERO
title_fullStr Metadata management for high content screening in OMERO
title_full_unstemmed Metadata management for high content screening in OMERO
title_short Metadata management for high content screening in OMERO
title_sort metadata management for high content screening in omero
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4773399/
https://www.ncbi.nlm.nih.gov/pubmed/26476368
http://dx.doi.org/10.1016/j.ymeth.2015.10.006
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