Over the last decade, advances in high-throughput technologies
have resulted in a flood of new biological data. Here, individual
samples can extend up into terabyte size. While potential applications
are broad, ranging from biotechnology to medical applications, the
analysis of these datasets poses massive challenges.
In order to make use of the produced terabytes of data, these datasets need to
be integrated, need to be mapped onto existing biological knowledge,
and need to be explored by experts.
We present UniPAX and BiNA, a scalable system for the integration and analysis
of high-throughput data (genomics, transcriptomics, proteomics, and
metabolomics) in a network context. A central data warehouse holds the core
dataset. A flexible middleware can execute custom queries on this dataset and
communicate with our visual analytics tool BiNA, the Biological Network
Analyzer. We demonstrate how the combination of these tools permits an efficient
analysis of large-scale datasets for medical applications.