Full Text

Scott H. Harrison, Sudipto Saha, Peter Hussey, Xiang Zhang, Jake Chen. A Clinical Proteomics Data Model for Managing Metadata of Mass Spectrometry Experiments. Clinical Proteomic Technologies Initiative for Cancer (CPTAC) Annual Meeting 2007.

Abstract

Mass spectrometry (MS) experimentation for clinical proteomics research presents a special problem of metadata management requiring accuracy in design and long-term sustainability of implementation based on the formalities, costs and times of duration associated with clinical trials. The growth of data and intensive needs for quantifications and analysis leading to discovery and clinical application require significant investment into the storage and processing capacities of computer infrastructure. Yet, the degree of institutional commitment required for managing complex and costly proteomics laboratory resources, combined with cross-disciplinary and cross-validating analyses of data, prioritizes the need for a team-based software system for archival, retrieval and collaboration. The data modeling solution we describe is centered upon use cases experienced by 5 research teams and more than 10 academic research labs that are collaborating together as part of the Clinical Proteomic Technology Assessment for Cancer (CPTAC) project. Dissemination of the solution has involved implementing a developed CPTAC data model within the metadata framework of the widely used Computational Proteomics Analysis System (CPAS), and establishing a portal for data entry at the National Institute of Standards and Technology (NIST). The implementation of our initiative was based on logical data structures geared for flexibility and efficiency in data entry, and is achieving architectural goals for ease of use and customization.