ABI Development: BISQUE - Scalable Image Informatics for Quantitative Biology

点击翻译按钮获取中文摘要

The University of California Santa Barbara is awarded a grant to develop a collaborative environment for biological image sciences based on the emerging paradigm of cloud-computing and web-based protocols. Recent advances in microscopy imaging, image processing and computing technologies enable large-scale biological experiments that generate not only large collections of images and video, but also pose new computing and information processing challenges. This project addresses core challenges concerning large scale, unstructured biological images and video collections. These include providing ubiquitous access to images, videos and metadata resources; creating easily accessible image and video analysis, visualizations and work flows; and publishing both data and analysis resources. Streamlining collaborative efforts across teams with online virtual environments will improve scientific productivity, enhance understanding of complex phenomena and allow a growing number of researchers to quantify conditions based on image evidence that so far have remained subjective. The BISQUE (Bio-Image Semantic Query and Environment) open-source platform is being developed keeping these requirements in mind. This project will focus on scalability issues concerning analysis and annotations, computational scalability with distributed computing and cloud enabled services, sophisticated new methods for video and 3D-5D image analysis/visualization, and validation and curation of data and methods. Furthermore, the project will provide individual researchers and laboratories with the means to publish and share data and analysis techniques, thus creating opportunities for cross-fertilization of ideas.The research will lead to new paradigms for management of image data in diverse scientific fields. One specific area that will immediately benefit is quantitative biology, where large-scale quantitative analysis of information will enable new scientific discoveries and reproducible results. The Bisque platform, with its web-enabled tools and seamless support for demanding computations, will allow diverse labs to publish both scientific datasets and analysis in an easy to use form. Bisque will be used in teaching and research by graduate and undergraduate students. There will be opportunities for undergraduates and high-school students for summer research internships. Through workshops, online tutorials, video demos, and the iPlant discovery environment, this project will ensure outreach to a broader spectrum of researchers. For more information and access to the Bisque platform, visit the website at http://www.bioimage.ucsb.edu/bisque.