Sharing biomedical data with impunity and ease.

BIOLOGICAL DATA is increasingly being shared between databases using a variety of different data formats. While some of these formats have been developed almost exclusively for communicating between viewers and databases (e.g., AGAVE and GAME) and others for sharing annotation as well as viewing (e.g., DAS), many formats are also being used to exchange data between databases (e.g., EMBL, ASN.1, and specialized XML DTDs). The appeal of a data exchange format is that it is a way of serving data in a uniform, flexible, and easily parseable form. Data exchange formats are also largely self-describing and hence easy to understand. However, agreeing to use a specific exchange format does not solve the data exchange problem by itself. Exporters must map their data into the exchange format, and importers of data must again map from the exchange format into their local format (or more generally, model). Thus data exchange is inextricably tied up with writing mappings (or transformations) between data formats. Several problems are associated with writing mappings between data formats. First, it is an inherently difficult problem. The writer of the mapping must understand both how the data is being represented in the exchange format and how they are representing it in their own model. Second, semantic information is frequently not captured in a data exchange format. For example, information about keys, foreign keys and constraints is often omitted. Clearly, constructing a mapping must be guided by an understanding of the semantics of the data since otherwise the mapping may cause run-time constraint violations. As an example of these problems, we having recently been conducting an experiment involving exchanging microarray gene expression data using a developing standard called MAGE-OM/ML (Spellman et al., 2002). The semantics of MAGE is specified using UML modeling tools (MAGE-OM), however the exchange is effected using an XML representation of the standard. Prior to this standardization effort, a relational database called RAD (On-line data) had been developed at the Penn Center for Bioinformatics to store gene expression data as well as its associated sample annotation data. When the MAGE-ML standard is finalized, data will be imported from collaborators and exported from RAD using this format. However, each of these data representations—RAD and MAGE—has been developed independently. In our experiment, the following problems emerged: 1. The data exported by RAD into MAGE-ML through some transformation may fail to validate against the constraints of MAGE-OM. Example: Gene expression annotation in MAGE-ML requires information about the process of sample preparation. The annotation interface in RADv2 required only information about the end result of the sample preparation, with an (optional) free-text description of the process. Since in MAGE-ML the biomaterial can only exist if the biosource is present (and so on down the process), the sample information in RADv2 was inconsistent with MAGE-ML. RAD was therefore modified so that RADv3 is consistent with MAGEML, and a new annotation interface is under development to force the process to be captured. 2. The data imported by RAD through some transformation from MAGE-ML may violate integrity constraints in RAD. If the MAGE-ML data is consistent with respect to the constraints of MAGE-OM, then there must be some inconsistency between MAGE-OM and the constraints expressed in RAD.