Abstract
The LONI Pipeline is a graphical environment for construction, validation and execution of advanced neuroimaging data analysis protocols (Rex et al., 2003). It enables automated data format conversion, allows Grid utilization, facilitates data provenance, and provides a significant library of computational tools. There are two main advantages of the LONI Pipeline over other graphical analysis workflow architectures. It is built as a distributed Grid computing environment and permits efficient tool integration, protocol validation and broad resource distribution. To integrate existing data and computational tools within the LONI Pipeline environment, no modification of the resources themselves is required. The LONI Pipeline provides several types of process submissions based on the underlying server hardware infrastructure. Only workflow instructions and references to data, executable scripts and binary instructions are stored within the LONI Pipeline environment. This makes it portable, computationally efficient, distributed and independent of the individual binary processes involved in pipeline data-analysis workflows. We have expanded the LONI Pipeline (V.4.2) to include server-to-server (peer-to-peer) communication and a 3-tier failover infrastructure (Grid hardware, Sun Grid Engine/Distributed Resource Management Application API middleware, and the Pipeline server). Additionally, the LONI Pipeline provides three layers of background-server executions for all users/sites/systems. These new LONI Pipeline features facilitate resource-interoperability, decentralized computing, construction and validation of efficient and robust neuroimaging data-analysis workflows. Using brain imaging data from the Alzheimer's Disease Neuroimaging Initiative (Mueller et al., 2005), we demonstrate integration of disparate resources, graphical construction of complex neuroimaging analysis protocols and distributed parallel computing. The LONI Pipeline, its features, specifications, documentation and usage are available online (http://Pipeline.loni.ucla.edu).
Highlights
Modern tools for image processing employ large amounts of heterogeneous data, diverse computational resources and distributed web-services (Toga and Thompson, 2007)
Interactive workflow environments for automated data analysis are critical in many research studies involving complex computations and large datasets (Kawas et al, 2006; Myers et al, 2006; Oinn et al, 2005; Taylor et al, 2006)
There are three distinct necessities that underlie the importance of such graphical frameworks for management of novel analysis strategies – high data volume and complexity, sophisticated study protocols and demands for distributed computational resources
Summary
Modern tools for image processing employ large amounts of heterogeneous data, diverse computational resources and distributed web-services (Toga and Thompson, 2007). Efficient analysis protocols combine diverse data, software tools and network infrastructure to obtain, analyze and disseminate results. Construction of such analysis protocols are significantly enhanced by a graphical workflow interface that provides high-level manipulation of the analysis sequence while hiding many of its technical details. In this manuscript, we discuss the challenges of development, maintenance and dissemination of integrated resources including data, software tools and web-services, as platform-independent, agile and scalable frameworks. We demonstrate the development and utilization of the LONI Pipeline environment for combining of user computational and biological expertise with disparate resources and Grid infrastructures. Version 4 of the LONI Pipeline, extends the previous implementation of this environment (Rex et al, 2003)
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