4.2.1 Desiderata

If researcher A wants to use a database kept and maintained by researcher B, the “quick and dirty” solution is for researcher A to write a program that will translate data from one format into another. For example, many laboratories have used programs written in Perl to read, parse, extract, and transform data from one form into another for particular applications.3 Depending on the nature of the data involved and the structure of the source databases, writing such a program may require intensive coding.

Although such a fix is expedient, it is not scalable. That is, point-to-point solutions are not sustainable in a large community in which it is assumed that everyone wants to share data with everyone else. More formally, if there are N data sources to be integrated, and point-to-point solutions must be developed, N (N − 1)/2 translation programs must be written. If one data source changes (as is highly likely), N − 1 programs must be updated.

A more desirable approach to data integration is scalable. That is, a change in one database should not necessitate a change on the part of every research group that wants to use those data. A number of approaches are discussed below, but in general, Chung and Wooley argue that robust data integration systems must be able to

  1. Access and retrieve relevant data from a broad range of disparate data sources;

  2. Transform the retrieved data into a common data model for data integration;

  3. Provide a rich common data model for abstracting retrieved data and presenting integrated data objects to the end-user applications;

  4. Provide a high-level expressive language to compose complex queries across multiple data sources and to facilitate data manipulation, transformation, and integration tasks; and

  5. Manage query optimization and other complex issues.

Sections 4.2.2, 4.2.4, 4.2.5, 4.2.6, and 4.2.8 address a number of different approaches to dealing with the data integration problem. These approaches are not, in general, mutually exclusive, and they may be usable in combination to improve the effectiveness of a data integration solution.

Finally, biological databases are always changing, so integration is necessarily an ongoing task. Not only are new data being integrated within the existing database structure (a structure established on the basis of an existing intellectual paradigm), but biology is a field that changes quickly—thus requiring structural changes in the databases that store data. In other words, biology does not have some “classical core framework” that is reliably constant. Thus, biological paradigms must be redesigned from time to time (on the scale of every decade or so) to keep up with advances, which means that no “gold standards” to organize data are built into biology. Furthermore, as biology expands its attention to encompass complexes of entities and events as well as individual entities and events, more coherent approaches to describing new phenomena will become necessary—approaches that bring some commonality and consistency to data representations of different biological entities—so that relationships between different phenomena can be elucidated.

As one example, consider the potential impact of “-omic” biology, biology that is characterized by a search for data completeness—the complete sequence of the human genome, a complete catalog of proteins in the human body, the sequencing of all genomes in a given ecosystem, and so on. The possibility of such completeness is unprecedented in the history of the life sciences and will almost certainly require substantial revisions to the relevant intellectual frameworks.

3  

The Perl programming language provides powerful and easy-to-use capabilities to search and manipulate text files. Because of these strengths, Perl is a major component of much bioinformatics programming. At the same time, Perl is regarded by many computer scientists as an unsafe language in which it is easy to make programs do dangerous things. In addition, many regard the syntax and structure of most Perl programs to be of a nature that is hard to understand much after the fact.



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