Data repositories are important tools in cancer research. They provide safe and sustainable locations to store data, provide access to input data for meta-analyses, and allow researchers to collaborate and share information across a common resource.
The problem is that repositories are often not flexible enough to store data that do not conform to known standards. Genomics, for example, benefits from community genomics standards groups and open industry standards. Many other fields of study are without such standards, yet generate significant amounts of data.
The Center for Strategic Scientific Initiatives Data Coordinating Center (CSSI DCC) stores and manages access to data generated in support of cancer research funded or supported by the CSSI. Frederick National Lab, under the leadership of Andrew Quong, developed the CSSI DCC Portal, a repository for CSSI DCC data and also emerging data types without a standard approach to data storage. The data currently in the DCC conform to the standard Investigation-Study-Assay tab-delimited format (ISA-TAB) format, which describes a scientific investigation, its study or studies, and each study's assay(s).
The DCC's goal is to use the CSSI DCC Portal to store emerging data types in addition to ISA-TAB. Facilitating this is the DCC's design approach, which follows FAIR principles for scientific data management and stewardship--Findable, Accessible, Interoperable, and Reusable, applied metadata standards, and involved interactions with the academic research community to seek out and apply their best practices.
The CSSI DCC Portal is powerful and serves the following purposes:
- Provides a common location and web access to data from disparate data types including gene expression results from Next Generation Sequencing, microarray experiments, histopathological images, metabolomics data and proteomics data, allowing for easy access by multiple collaborators and researchers located at different geographic locations. Is flexible enough to handle new and unspecified data types.
- Stores the data in one common location so that you can make biological insights that would otherwise be missed by having data in multiple locations.
- Applies the information gained from one study to multiple studies and projects.
- Allows you to search the metadata from each study to identify datasets of interest.
- Develops data storage and data mining modules that can be applied across studies, avoiding duplication of effort and saving costs.
- Develops and/or adopts common vocabularies, data standards, and ontologies for data representation, storage, and comparison.
See Neo4j - Rapidly Prototyping a Semantic Graph for more information about technologies used in CSSI DCC.
(Do you want to say something about the response to the Portal thus far or future uses of it?)
