SUMMARY: To expedite the review of semi-automated probability maps of organelles and other features from 3D electron microscopy data we have developed Probability Map Viewer, a Java-based web application that enables the computation and visualization of probability map generation results in near real-time as the data are being collected from the microscope. Probability Map Viewer allows the user to select one or more voxel classifiers, apply them on a sub-region of an active collection, and visualize the results as overlays on the raw data via any web browser using a personal computer or mobile device. Thus, Probability Map Viewer accelerates and informs the image analysis workflow by providing a tool for experimenting with and optimizing dataset-specific segmentation strategies during imaging. AVAILABILITY AND IMPLEMENTATION: https://github.com/crbs/probabilitymapviewer. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Over the last decade, workflows have been established as a mechanism for scientific developers to create simplified views of complex scientific processes. However, there is a need for a comprehensive system architecture to link scientific developers creating workflows with researchers launching workflows in large scale computing environments. We present the architecture for the CAMERA 2.0 Cyber infrastructure platform that provides a scaffold where workflows can be uploaded into the system, and user interface components for launching and viewing results are automatically generated. In CAMERA 2.0, scientific developers and metagenomics researchers seamlessly collaborate to (i) wrap data-analysis software applications and heterogeneous tools as Resource Oriented Architecture (ROA) components integrating them using scientific workflows; (ii) publish and run scientific workflows via dynamically generated uniform portal interfaces; (iii) map heterogeneous workflow products to provenance and CAMERA semantic database through a transformation component, to save output data resulting from workflow runs based on this mapping; (iv) record and visualize the provenance of all workflow run-related data and processes; and (v) conduct queries across multiple workflow executions and link these workflow executions to each other through data and provenance related to these runs. Furthermore, workflows added to CAMERA also have access to a variety of physical resources for computation and data management. Here, we demonstrate the usability of this framework with some of the developed metagenomics workflows.