A Statistical Computing Framework for Genomic Data

Dana-Farber Cancer Institute, Boston MA

Investigators

Linked publications & trials

Abstract

DESCRIPTION (provided by applicant): We will design and deploy software infrastructure that bridges diverse processes and resources in biotechnology and information science. In the process we will design and implement significant improvements in the R programming language to support the development and use of the innovative software tools we develop. In the domain of biotechnology we will design software enabling the integration of experimental data and experimental metadata (e.g. MIAME). We will develop tools to integrate biological metadata with experimental data. These tools will be designed to allow other developers to have access to all methodology and will be designed to simplify interactions with other projects through a well-defined class system. We will develop software infrastructure for visualization, combining data from multiple sources and accessing information, programmatically from the WWW. In the domain of information technology, tools for creating, structuring and harvesting annotation resources and databases of published literature will be developed. Interactive tools for linking experimental results to annotation/literature resources in real time will be provided. We will explore the development and deployment of Web services. The software architecture will address the highly dynamic nature of the annotation and scientific literature, and will cope with multiple data sources of varying degrees of quality. Indexing of techniques with respect to error rates, resolutions and capabilities will be supported. Guidance and infrastructure that will enable the use of the software tools in a graphical manner will be developed and deployed. Additionally we will use the tools described above to develop new computational methods for genomic data, with particular attention to visualization, computational inference, multiple comparisons, and specialized analytic methods for microarray data.