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Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

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Cloud-enabled Biodepot workflow builder integrates image processing using Fiji with reproducible data analysis using

Ling-Hong Hung¹, Evan Straw^2,3, Shishir Reddy¹

¹School of Engineering and Technology, University of Washington Tacoma, Box 358426, Tacoma, 98402, WA, USA.

Scientific Reports

|September 2, 2022

Summary

This summary is machine-generated.

The Biodepot-workflow-builder platform enables reproducible biomedical image analysis by supporting interactive graphics within software containers. This simplifies cloud-based processing of large datasets and integrates various analysis tools.

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Published on: December 16, 2014

Area of Science:

Biomedical image analysis
Computational biology
Bioinformatics

Background:

Modern biomedical image analysis workflows face reproducibility challenges due to complex computational tasks.
Large, multi-dimensional image datasets exceed local processing capabilities.
Existing omics analysis tools lack support for interactive graphical elements common in image analysis.

Purpose of the Study:

To present a containerized and cloud-enabled platform, Biodepot-workflow-builder, for advanced biomedical image analysis.
To extend software container capabilities to support interactive graphics for image processing.
To simplify the execution of complex, multi-step image analysis workflows on cloud resources.

Main Methods:

Developed a modular platform integrating software containers with support for interactive graphics.
Extended the platform for biomedical image analysis, incorporating the Fiji image processing suite.
Demonstrated workflow integration with interactive ImageJ macros in Jupyter notebooks.
Automated and simplified cloud setup for computationally intensive tasks like 3D digital pathology stitching using BigStitcher.

Main Results:

Successfully integrated interactive graphical elements within software containers for image analysis.
Enabled single-click execution of multi-step workflows via a form-based graphical interface.
Facilitated interactive modification of image processing steps and parameters by users.
Simplified the cloud deployment of complex image analysis tasks, such as large-scale dataset stitching.

Conclusions:

The Biodepot-workflow-builder platform enhances reproducibility in biomedical image analysis.
It provides simplified access to scalable cloud computing resources for large datasets.
The platform supports seamless integration across diverse applications, including Jupyter notebooks and Fiji, for comprehensive image analysis.