Data repositories are important tools in cancer research, providing safe and sustainable locations to store data, providing access to input data for meta-analyses, and allowing researchers to collaborate and share information across a common resource.
The problem is that scientists are generating data...or 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 that develop standard programmatic interfaces for managing, describing, and annotating genomic data (attribution: https://gdc.cancer.gov/about-data/data-standards). Emerging data types such as...do not yet have data storage standards.
The Center for Strategic Scientific Initiatives Data Coordinating Center (CSSI DCC), supported by the Frederick National Lab, stores and manages access to data generated in support of cancer research. 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 store emerging data types in addition to those that comply with ISA-TAB. The DCC was designed according to the guiding principles of FAIR: Findable, Accessible, Interoperable, and Reusable, metadata standards such as ISA-TAB, and best practices of the cancer research community,
The CSSI DCC team, led by FNL's Andrew Quong, developed the CSSI DCC Portal, the repository for CSSI DCC data. It 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.
