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Bisque Image Analysis Environment

Associating phenotypes with genotypes is one of the grand challenges facing biological scientists, and high-throughput imaging is essential for enabling genome-scale phenotyping efforts. Automated image acquisition is already creating vast amounts of data, and super resolution microscopy and multi-channel imaging are pushing the boundaries of data storage and computational capabilities. Although many laboratories have high-throughput image acquisition setups, they typically lack the accompanying and requisite high-throughput data management and scalable analysis platform.

 

Bisque (Bio-Image Semantic Query User Environment) is a web-based system that provides support for secure image storage, analysis, and data management capabilities, while giving users control over sharing of images and analysis results. The Bisque system is integrated with iPlant’s authentication system, Data Store, and computation infrastructure for scalability and ready access to a large set of downstream analysis options. For developers wanting to integrate existing applications or create new applications, Bisque provides a rich set of custom visualization and image handling routines and APIs (Application Programming Interfaces) for building scalable web-based image analysis applications.

To try Bisque now, use your iPlant credentials to log in at
http://bisque.iplantcollaborative.org/.

Features

  • Enables high-throughput image analysis
  • Imports and stores extremely large images and datasets (5D images; 100+ GB)
  • Displays 20K x 20K pixel images using a standard web browser
  • Uses features from popular large scale photo sharing sites and high resolution aerial imagery (google maps) to automatically tile and display large images
  • Supports over 100+ biological image formats and movies
  • Overlay results to validate findings without altering original image
  • Compare different analysis methods
  • Share results, images, and annotations with collaborators via a secure link
  • Automatic and manual metadata tagging
  • Allows manual annotations (text and graphical)
  • Developers can easily publish novel analysis methods and make them web accessible
  • Developers can produce interactive plots and visualizations using the built-in API
  • Is free and open-source

The Bisque system includes analysis algorithms useful to plant scientists as well as the broader biological research community.

Image Analysis Algorithms

  • Seed size (Edgar Spalding group, University of Wisconsin)
  • Root tip tracking (Edgar Spalding group, University of Wisconsin)
  • Pollen tube tracking (Ravi Palanivelu group, University of Arizona)
  • Other general biological image analysis algorithms
  • To recommend image analysis method for integration into Bisque, please contact support@iplantcollaborative.org.

For further questions on how you can use iPlant hosted bisque for your research projects please contact info@iplantcollaborative.org

Development Team

Name Role Institution
B.S. Manjunath
Bisque project lead
University of California, Santa Barbara
Edgar Spalding iPlant imaging algorithms University of Wisconsin
Nirav Merchant iPlant Project Lead iPlant Collaborative, The University of Arizona
Kris Kvilekval Lead Developer University of California, Santa Barbara
Dmitry Fedorov Lead Developer University of California, Santa Barbara
Utkarsh Gaur Developer University of California, Santa Barbara
Nathan Miller Lead analysis tool developer University of Wisconsin
Logan Johnson Analysis tool developer University of Wisconsin