Edinburgh Data-Intensive Research - Rapid-OMERO

OMERO: flexible, model-driven data management for experimental biology

Chee.Sun.Liew — Thu, 01 Mar 2012 13:52:13 +0000

Arrival of Donald MacDonald and departure of Jos Koetsier

Malcolm.Atkinson — Fri, 30 Sep 2011 13:33:34 +0000

We are delighted to welcome Donald MacDonald who is joining the Rapid-OMERO project developing its story from where Jos Koestier is leaving it.
He brings with him the experience of working in the OMERO team with Professor Jason Swedlow in Dundee, and earlier experience of data mining in Paisley.
He will be exploring how to make best use of data-intensive strategies to distill useful measurements from the microscopy images, exploiting the data-intensive technology at Edinburgh. He will also be making the data-intensive methods far more accessible to microscopists, biologists and biochemists, by continuing the development of portals using RAPID and integrating them with the OMERO research environment. Before he does this he is getting married this Sunday! We wish him and his wife Annette many many happy years together.

Jos was the prime mover behind RAPID and he leaves a legacy of well-developed software and many applications of the approach. We wish him every success in his new role in Melbourne working with Professor Richard Sinnott.

Location:

Informatics Forum

Dates:

30 Sep 2011 to 4 Oct 2011

Research topics:

Efficient distributed systems

Effective algorithms

Data-intensive computing

Collaborative environments

Projects:

Rapid-OMERO

Intuitive Large-scale Image Processing for Biologists

Jano.van.Hemert — Mon, 31 May 2010 14:05:17 +0000

Modern cell and developmental biology and the now-established domain of systems biology use quantitative imaging methods to measure the location, dynamics and interaction of molecules in fixed and living cells, and at increasingly high spatial and temporal resolution. Quantitative imaging depends on the development, delivery, and use of sophisticated image processing and analysis algorithms. The availability of these data analysis tools is commonly cited as a major bottleneck in scientific discovery. Previously, the absence of common interfaces and defined standards for data structures hindered the sharing of new analysis methods and the open, shared access to image datasets. Moreover the sheer computational cost of running complex algorithms on large datasets demands access to compute facilities that, while existing, are not accessible via standardised, intuitive tools for most bench biologists.

This project combines developments in the OMERO application developed by the Open Microscopy Consortium led by Prof J Swedlow and the Rapid portlet development tool developed in Dr J I van Hemert's laboratory at the UK's National e-Science Centre. The resource generated will be a service with an intuitive user interface that enables bench biologists to access high performance computing resources for processing and analysing their multi-dimensional images of cells and tissues. We do not propose to develop a single stand-alone resource under this project but to provide a vital service for bench biologists, based on world-leading work performed in the UK that uses common, standardised interfaces and established principles in usability to provide access to cutting-edge image analysis methods for bench biologists. The resource will be released as a component of the open-source OMERO software suite that is currently either in testing or in daily use at most imaging sites in the UK and over 1200 sites worldwide. A stable version of Rapid will be bundled in these releases under the same license. We will build this service on top of the Edinburgh Compute Data Facility and the National Grid Service to provide the underlying e-Infrastructure.

Acronym:

Rapid-OMERO

Value:

£780956

Dates:

1 July 2010 to 31 December 2012

Partners:

Swedlow Lab, Wellcome Trust Centre for Gene Regulation & Expression, University of Dundee, UK

Projects:

Rapid-OMERO

Research topics:

Biological/Medical Image Processing

Collaborative environments

Data-intensive computing

Project Status:

In progress