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MBAT: A scalable informatics system for unifying digital atlasing workflows

Daren Lee, Seth Ruffins, Queenie Ng, Nikhil Sane, Steve Anderson and Arthur Toga*

Author Affiliations

UCLA Laboratory of NeuroImaging, David Geffin School of Medicine, Los Angeles, CA USA

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BMC Bioinformatics 2010, 11:608  doi:10.1186/1471-2105-11-608

Published: 22 December 2010

Abstract

Background

Digital atlases provide a common semantic and spatial coordinate system that can be leveraged to compare, contrast, and correlate data from disparate sources. As the quality and amount of biological data continues to advance and grow, searching, referencing, and comparing this data with a researcher's own data is essential. However, the integration process is cumbersome and time-consuming due to misaligned data, implicitly defined associations, and incompatible data sources. This work addressing these challenges by providing a unified and adaptable environment to accelerate the workflow to gather, align, and analyze the data.

Results

The MouseBIRN Atlasing Toolkit (MBAT) project was developed as a cross-platform, free open-source application that unifies and accelerates the digital atlas workflow. A tiered, plug-in architecture was designed for the neuroinformatics and genomics goals of the project to provide a modular and extensible design. MBAT provides the ability to use a single query to search and retrieve data from multiple data sources, align image data using the user's preferred registration method, composite data from multiple sources in a common space, and link relevant informatics information to the current view of the data or atlas. The workspaces leverage tool plug-ins to extend and allow future extensions of the basic workspace functionality. A wide variety of tool plug-ins were developed that integrate pre-existing as well as newly created technology into each workspace. Novel atlasing features were also developed, such as supporting multiple label sets, dynamic selection and grouping of labels, and synchronized, context-driven display of ontological data.

Conclusions

MBAT empowers researchers to discover correlations among disparate data by providing a unified environment for bringing together distributed reference resources, a user's image data, and biological atlases into the same spatial or semantic context. Through its extensible tiered plug-in architecture, MBAT allows researchers to customize all platform components to quickly achieve personalized workflows.