Intuitive Large-scale Image Processing for Biologists

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.

Rapid is a tool aimed at quickly designing and delivering user interfaces for applications that need access to remote compute resources. This need may arise because of the large amount of computing required or simply because the applications must execute on specific platforms. It enables these applications by generating a customised interface allowing a computational task to be performed without referring to the terminology of the underlying computational infrastructure. Its aim is to make submitting remote compute jobs as easy as booking a flight or ordering a book on the Internet. Rapid provides a XML specification that is a light-weight extension of both the Job Submission Description Language (JSDL) and the eXtended Hyper-Text Markup Language (XHTML). It provides a description of the User Interface (UI), the resources available and the task-flow. We will extend the specification to add methods specifically dealing with OMERO servers, staging and uploading data back to a server. We also will extend the specification of the UI to allow more dynamic behaviour than is currently available in Rapid. OMERO will be updated to allow reading of task descriptions in the JSDL specification and using the OMERO.Rapid job submission/UI API and be able to submit these tasks to remote computing resources. For image processing, the community requires a shared resource for processing and analysis algorithms written against the OMERO API. OMERO already uses a central repository for user comments, bug tracking, upgrade checks, and submission of user files that are either useful examples or causing users problems. This site will be updated to hold a repository of OMERO.Rapid Tasks that can be through the OMERO API and then run on remote resources using Rapid's facilities. We will provide a full schema for the specification of OMERO.Rapid Tasks that will be published and maintained alongside the well-established OME-XML.

Our goal is to change the way bench biologists use imaging as a scientific discovery and assay tool. As image datasets grow in size and complexity and analysis and processing tools grow in sophistication, the need for general access to high performance computing will only grow. To date, experimental biologists have rarely made use of central computing resources because interfaces have been designed for scientists familiar with command line interfaces. The need for access to these resources certainly exists-iterative deconvolution is often used in live cell imaging, requiring many hours to days to process a single timelapse sequence. New high resolution imaging methods like PALM, STORM and 3D Structured Illumination require substantial processing of raw data just to deliver the first image a biologist can use. Our existing projects, OME and Rapid, have already made important contributions to the data management and processing challenges of life scientists. In this project, we will build the tools that provide links from easy-to-use, biologist-friendly interfaces to remotely located, high performance computing resources. We will also build a repository of standard compute-intensive processing tasks that any biologist can access and run on the resource of their choice. Our projects have established track records of producing useful, usable tools for bench biologists. The output of this project will be available for installation and usage by anyone. Given our established record of installations, we can confidently predict that many hundreds to thousands of scientists worldwide will use the output of this project, and in so doing, gain access to a repository of image processing tools and the resources to process large datasets or run computationally challenging algorithms from their desktop. Thus, our work can be expected to be used by most cell biologists in the UK, and very likely throughout the world.