The Open Microscopy Environment: A Collaborative Data Modeling and Software Development Project for Biological Image Informatics

The transition of a microscope’s output from an “image,” recorded on paper or film, to digitally recorded “data” has created new demands for storage, analysis and visualization that are not adequately met in current software packages. The Open Microscopy Environment (OME) Consortium is dedicated to developing open available tools to meet this challenge. We have developed and released the OME data model that provides a thorough description of the image data acquisition, structure and analysis results. An XML representation of the OME data model provides convenient standardized file formats known as OME-XML and OME-TIFF. In addition, OME has built two software tools, the OME and OME Remote Objects (OMERO) servers that enable visualization, management and analysis of multidimensional image data in structures that enable remote access. The OME server provides a flexible data model and an interface into complex analysis workflows. The OMERO server and clients provide image data visualization and management. A major goal for the next year is the provision of well-developed libraries and documentation to support the OME file formats, and enhanced functionality in our OME and OMERO applications to provide complete solutions for imaging in cell biology.

[1]  J. Lippincott-Schwartz,et al.  Studying protein dynamics in living cells , 2001, Nature Reviews Molecular Cell Biology.

[2]  Ilya G. Goldberg,et al.  Open microscopy environment , 2005, 2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05).

[3]  A. Coulson,et al.  A functional genomic analysis of cell morphology using RNA interference , 2003, Journal of biology.

[4]  C. Conrad,et al.  Automatic identification of subcellular phenotypes on human cell arrays. , 2004, Genome research.

[5]  R. Tsien,et al.  The Fluorescent Toolbox for Assessing Protein Location and Function , 2006, Science.

[6]  F. Wouters,et al.  Imaging biochemistry inside cells. , 2001, Trends in cell biology.

[7]  Ilya G. Goldberg,et al.  Modelling data across labs, genomes, space and time , 2006, Nature Cell Biology.

[8]  J. Swedlow,et al.  Deconvolution in optical microscopy , 1996 .

[9]  David A Schiffmann,et al.  Open microscopy environment and findspots: integrating image informatics with quantitative multidimensional image analysis. , 2006, BioTechniques.

[10]  A. Poustka,et al.  Systematic subcellular localization of novel proteins identified by large‐scale cDNA sequencing , 2000, EMBO reports.

[11]  R. Eils,et al.  Computational imaging in cell biology , 2003, The Journal of cell biology.

[12]  Kalen Delaney,et al.  Oracle 10g , 2006 .

[13]  P. Jansson Deconvolution of images and spectra , 1997 .

[14]  Erik Brauner,et al.  Informatics and Quantitative Analysis in Biological Imaging , 2003, Science.

[15]  Douglas A. Creager,et al.  The Open Microscopy Environment (OME) Data Model and XML file: open tools for informatics and quantitative analysis in biological imaging , 2005, Genome Biology.

[16]  Jason R Swedlow,et al.  To 5D and Beyond: Quantitative Fluorescence Microscopy in the Postgenomic Era , 2002, Traffic.

[17]  Tom Misteli,et al.  Kinetic modelling approaches to in vivo imaging , 2001, Nature Reviews Molecular Cell Biology.

[18]  Jason R. Swedlow,et al.  Cajal Body dynamics and association with chromatin are ATP-dependent , 2002, Nature Cell Biology.

[19]  T. Mitchison,et al.  Phenotypic screening of small molecule libraries by high throughput cell imaging. , 2003, Combinatorial chemistry & high throughput screening.