Genetics management and analysis tools: GeMS

DESCRIPTION (provided by applicant): During Phase I, this project successfully implemented a Genetics Management System (GeMS) in partner labs for testing and requirements gathering. During Phase II, the project will expand these efforts towards additional labs and will implement new functionalities into GeMS, increasing the data-managing and data sharing capabilities of GeMS. Our Phase I accomplishments include 1) a revision of the overall architecture for GeMS, 2) establishing a functional software development methodology, 3) the installation, requirements gathering, and testing of GeMS in five laboratories, 4) the identification of a number of specific needs from our partner labs for future implementation, 5) an accounting of cost savings from the use of GeMS, 6) the development of new analytical tools that meet needs of the relevant area of biomedical research, 7) substantial new programming completed implementing several GeMS core components and new service modules, and 8) the identification of additional academic laboratories for new deployments. The overall goal of this Phase II application is to provide methods for researchers with improved ability to generate, organize, analyze, and share data and metadata. To this end, we propose to accomplish the following specific aims, 1) To improve existing tools and implement new solutions for data analysis, 2) To extend GeMS to support new data models, 3) To improve the functionality of GeMS towards interaction with other systems, and 4) To expand the number of GeMS deployments. From these efforts we propose to engage the development and application of new biocomputing tools or technologies for a particular area(s) of scientific opportunity in biomedical research." In that regard, we have built and propose to further develop tools for data acquisition, archiving, querying, retrieval, visualization, integration, and management, web-based linkage tools for data sharing and tools for electronic communication, and new analytical tools for interpretation of complex sequence trace data. In Phase I we formed a cross-disciplinary collaboration with state of the art software development methods to produce products that are relevant to this scientific area and are evolvable, scalable, extensible, and interoperable computational resources. This project spans the interface of biomedical research and biomedical computational science and technology. It combines senior software engineers from industry, software engineers with biomedical backgrounds, and an active biomedical user laboratory defining requirements, forming unique cross-disciplinary collaborations between state of the art computer science applications and state of the art genomic/genetic methods and approaches.