07-11, 11:25–11:55 (US/Pacific), Room 316
Image analysis is ubiquitous across many areas of biomedical research, resulting in terabytes of image data that must be hosted by both research institutions and data repositories for sharing and reproducibility. Common solutions for data hosting are required to improve interoperability and accessibility of bioimage data, while maintaining the flexibility to address each institution's unique requirements regarding sharing and infrastructure. OMERO is an open-source solution for image data management which can be customized and hosted by individual institutions. OMERO runs a server-based application with web browser and command line options for accessing and viewing image data, based on the widely used OME data model for microscopy data. Multiple OMERO deployments might be used to provide core delivery, facilitate internal research, or serve as a public data repository. The omero-cli-transfer package facilitates data transfer between these OMERO instances and provides new methods for importing datasets. Another open-source package, ezomero, improves the usability of OMERO in a research environment by providing easier access to OMERO's Python interface. Along with existing OMERO plugins built for other analysis and viewing software, this positions OMERO to be a hub for image storage, analysis, and sharing.
In this talk, we will discuss two Python-native packages dedicated to expanding the OME/OMERO[1] ecosystem for imaging data management: ezomero and omero-cli-transfer.
ezomero[2] is a convenience-layer library in Python that abstracts the OME-provided Python API into functions that are easier to use, more intuitive and that use Python built-in or Scientific Python types as inputs and outputs. This library facilitates writing cleaner, more maintainable code by providing a simpler API to interface with an OMERO server.
omero-cli-transfer[3] is a pip package that provides serialization/deserialization of OMERO entities. It exposes a simple command-line interface that generates "data packages", files that include raw imaging data and an XML file describing metadata stored in OMERO (both part of the OME data model and not). This "data package" can be "unpacked" on a destination OMERO server, recovering all entities that existed in the source server: images, annotations, regions of interest and so on. It also provides a plugin interface for other serialization standards, such as ARC[4][5].
[1] Allan, Chris, et al. "OMERO: flexible, model-driven data management for experimental biology." Nature methods 9.3 (2012): 245-253.
[2] Martins Ratamero, Erick, et al. "Easing OMERO adoption with ezomero." bioRxiv (2023): 2023-06.
[3] https://github.com/ome/omero-cli-transfer
[4] Dumschott, Kathryn, et al. "DataPLANT–Harnessing the Power of Ontologies for FAIR Research Data Management." Tage 2023: 89.
[5] https://github.com/cmohl2013/omero-arc
I manage the Imaging Applications team at The Jackson Laboratory, working in the Research IT department and providing support on imaging data analysis and management to 60+ research groups. My main area expertise is in microscopy data analysis and management, with my previous life being the data person at the CAMDU core facility at Warwick Medical School. I have a PhD in Analytical Science, developing computational models of bacterial cell division, from the University of Warwick.