Databases and Systems

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screens to identify direct or indirect interactions based on phenotypes or gene
expression patterns. It is thus important for FlyBase to recognize and support data
representations and reports based on relationships among gene products in addition
to those relationships based on chromosomal location. Some ways of addressing this
need can be addressed now; others present substantial technical hurdles.
The FlyBase architecture supports the curation of different versions of a gene
product -RNAs or polypeptides or molecular complexes – as different data objects,
so that annotations can be attached to the appropriate objects. This is an essential
part of an organism-specific data model, since much of the regulation of cellular
function boils down to gene products that can be toggled between alternative states
based on allosteric interactions, subunit modifications, or differential subunit
interactions.
Describing the interactions and the pathways is an even larger and more difficult
task. Much of the available physical interaction data involves in vitro assays, usually
in heterologous systems. These data are often hints or suggestions of possible
interactions rather than readily verifiable ones. Genetic interaction data have their
own set of pitfalls. While the individual observations can be represented, our ability
to compile them into computed pathways is impaired by the inherent limitations of
the current data sets. Thus, we need to capture and represent data in a manner that
reflects the current state of knowledge, but that will be of value once better standards
and methods are available. This represents a considerable challenge at the strategic
and computational levels.
Another aspect of the problem are those of spatial pattern: descriptions of
anatomical phenotypes and gene expression patterns. Were rigorous representations
of spatial pattern possible, these could be used in combination with interaction data
to distinguish among possible interactions. (For example, two proteins that are shown
to physically interact but which are never expressed in the same tissues are unlikely
to interact in a biologically meaningful way). FlyBase has developed an extensive
ontology of anatomical parts, and using this vocabulary, phenotypes and expression
patterns are captured. Either the authors or the curators, however, end up throwing
away a great deal of data in turning two or three dimensional spatial information into
text. Similarly, dependence on text terms to support user queries places inherent
limitations on the depth of questions that can be answered. Ultimately, it will be
important for tools to be developed that can effectively capture quantitative spatial
information. Only in this way can these data can be directly queried without
imposing a strong filter on the data set through its conversion into much coarser
textual objects. This is obviously a major long term issue which is already receiving
attention, and we can expect that it will continue to be an important area for
computational research.