When a complex biological event is to be explored, a data-driven
approach is widely accepted as a powerful alternative to a conventional
hypothesis-driven one (Smalheiser, 2002
). However, the data-driven
approach cannot be achieved without high-quality genomic data.
In this regard, DNA microarray technology has opened the way
to the comprehensive collection of large amounts of data regarding
genome-wide gene expression profiles, and is now recognized
as a standard tool for the characterization of biological systems
at the mRNA level. The description of biological systems with
tens of thousands of different mRNA expression levels is highly
sensitive to the state of those systems, and has enabled us
to discover a number of mRNA biomarkers for various biological
events (Abbas et al.
; Komor et al.
; Su et al.
Zheng et al.
). However, the discovery of such biomarkers
does not necessarily address the molecular mechanisms underlying
the observed biological events. This is mainly because mRNA
levels do not always have direct relevance to biological phenomena
in general; mRNA is a template of protein synthesis and is not
a functional element in itself in most cases. In this respect,
protein profiles must have a greater relevance to biological
events than mRNA profiles because proteins govern them directly.
Thus, genome-wide characterization of the biological system
at the protein level is widely considered to be the next goal
to achieve, although this in itself is challenging due to technological
limitations faced at present (Cutler, 2003
). The low availability
of quantitative proteomic data is one of bottlenecks when analyzing
a biological system from an ‘omics’ viewpoint. To
address this problem, we have recently reported an approach
for generating quantitative proteomic maps based on two-dimensional
gel electrophoresis (2-DE) and have attempted to expand the
protein profiling data (Kimura et al.
As more mRNA and protein profile data has accumulated, the demand
for a cross-referencing database has increased significantly.
We have therefore developed a web-based platform for sharing
specialized immune cell mRNA and protein profile data. For this
study, all of the profile data was newly obtained following
well-controlled protocols. Affymetrix GeneChip DNA microarray
technology was utilized for obtaining mRNA profiles and 2-DE
for quantitative protein profiling. In order to integrate the
‘omics’ data obtained for various immune cells,
we annotated each sample with controlled vocabularies and implemented
a relational database that included the quantitative mRNA and
protein profiling data. The Reference genomics Database of Immune
Cells (RefDIC) offers a web-based query interface and user-friendly
data visualizing facilities, and also allows all of the raw
data to be downloaded. RefDIC could serve as a solid reference
for the transcriptome and proteome of immune cells, and hence
could greatly facilitate the identification of immunologically
important genes and proteins that are involved in various immune
responses, through cross-referencing quantitative mRNA and protein