This paper describes the rapid accumulation of biological data, its various organizational structures, and the role that ontologies play in interoperability.
The abundance of diverse biological data from various sources constitutes a rich source of knowledge, which has the power to advance our understanding of organisms.
Moreover, these signaling maps can also be overlaid with biological data from various high-throughput experimental platforms to obtain an integrated view of biological processes (3, 4).
In many genomics projects and other projects handling large amounts of biological data, various lists containing biological identifiers are produced, corresponding to e.g. sets of genes regulated under different treatments.
Due to the amorphous nature of the accruing biological data, various databases have continually been developed to allow systematization [ 1].
With the advancement of next-generation sequencing technologies, researchers are now able to generate a huge amount of data of various biological features of interest, including ChIP data for multiple transcription factors, histone modifications, nucleosome positioning and RNA-seq.
But with the diversity of available machines and exposure chambers comes a caveat: how to interpret the biological data generated from various exposure systems?
Clinical data complement biological data, enabling detailed descriptions of various healthy and diseased states, progression, and responses to therapies.
High throughput techniques have generated a huge set of biological data, which are deposited in various databases.
This allows the integration of biological data for rapid identification of biologically relevant similarities or differences between various protein functions.
