Enabling Cloud and Grid Powered Image Phenotyping Martha Narro iPlant Collaborative Adapted from slides by Nirav Merchant

Motivation High throughput imaging is essential for large-scale phenotyping. Affordable robotics for image acquisition creating vast amounts of imaging data. Many laboratories have automated imaging setups, but lack a comparable analysis platform. Super resolution microscopy and multi-channel images are pushing the boundaries of storage and computational capabilities.

Motivation II New, improved analysis algorithms are being published. Biologists struggle to use them. Developers need images to test algorithms. Scientists need to compare algorithms, reproduce results. Metadata is key for managing large datasets. Sharing and collaborating with large image data sets is challenging. ONE SIZE FITS ALL APPROACH DOES NOT WORK

Bisque Image Management, Analysis, Sharing System

Why Bisque? Biologists can Manage images Choose from multiple analysis options Overlay results to validate findings Annotate images Share images, results, annotations via secure link Algorithm developers can Publish new analysis methods, easily make them web accessible Produce interactive plots, visualizations using built in API Integrated with iPlant storage and computation infrastructure for scalability

How does it work? Bisque iPlant Data Store High Bandwidth Transfer iPlant Computational Infrastructure High Bandwidth Transfer

Bisque Features Web application Tiling, zooming, step through image stacks, play as movie Display 20K x 20K pixel images in web browser Handles 100+ image, video formats Import large image sets (≤ 40 GB Bisque), extremely large ones (> 40 GB iPlant Data Store) Scale analyses using distributed computing (connected to XSEDE) and workflow engines (Pegasus, Condor)

Pollen Tube Tracker Analysis Stack of time-lapse images of pollen tubes growing in vitro displaying maximum intensity in each image Tracking by Bisque Source: Ravi Palanivelu, Kobus Barnard

MultiRoot Growth Analysis Time lapse image stack of seeds growing Root tip tracking by Bisque Source: Edgar Spalding

Seed Size Analysis High resolution flat bed scanner image of seeds Edge detection and analysis by Bisque Source: Edgar Spalding

Automated Pollen Identification Imagine some pollen grains Source: Matina Donaldson-Matasci, et al. Imagine the species of the pollen grains has been identified Coming Attraction!

Users Currently iPlant has 5+ groups actively using this infrastructure 3 Graduate courses 2 Summer courses/workshops NSF ADBC Thematic Collections Network (Yale University led)

Users Currently iPlant has 5+ groups actively using this infrastructure 3 Graduate courses 2 Summer courses/workshops NSF ADBC Thematic Collections Network (Yale University led) Welcome, 1 Pollen RCN

Bisque-iPlant Team Bisque (U. California, Santa Barbara) B. S. Manjunath Kris Kvelikval Dmitry Fedorov Phytomorph (U. Wisconsin, Madison) Edgar Spalding Nathan Miller Logan Johnson Nirav Merchant (iPlant; U. Arizona, Tucson)

Main application: bisque.iplantc.org Support: Project Website Useful Links