"The new paradigm, now emerging, is that all the 'genes' will be known (in the sense of being resident in databases available electronically), and that the starting point of a biological investigation will be theoretical. An individual scientist will begin with a theoretical conjecture, only then turning to experiment to follow or test that hypothesis....

"To use this flood of knowledge [that is resulting from the mapping and sequencing of human genes and the genes of model organisms], which will pour across the computer networks of the world, biologists not only must become computer-literate, but also change their approach to the problem of understanding life.

"The next tenfold increase [5 years from now] in the amount of information in the databases will divide the world into haves and have-nots, unless each of us connects to that information and learns how to sift through it for the parts we need....

"We must hook our individual computers into the worldwide network that gives us access to daily changes in the database and also makes immediate our communications with each other. The programs that display and analyze the material for us must be improved—and we must learn how to use them more effectively. Like the purchased kits [of molecular biological reagents], they will make our life easier, but also like the kits, we must understand enough of how they work to use them effectively."

SOURCE: Reprinted, with permission, from Gilbert (1991), p. 99.


Within molecular biology, the importance of sharing data and the scale of research have increased for activities revolving around gene mapping and sequencing. The widespread use of automated DNA sequencing technology2 is now permitting the nucleotide sequence for many genes and chromosome regions to be determined. This enormous amount of information has brought centralized database archives to the fore as major points of collaboration. The main data produced in genome projects are the sequences of DNA base pairs embodying patterns of great evolutionary, physiological, and medical interest. The discovery of meaningful patterns is complicated: first, the data available to support a particular line of research are often sparse, so that many different organisms (e.g., bacteria, yeast, worms, mice, and humans) must be studied, and second, the available theory of the organization of DNA sequences remains scant, so that hands-on experimentation by scientists is required.

As experimental results, DNA sequences themselves are useful to share directly, since similarities in sequence often imply similarities in function. However, the printed literature is no longer adequate for sharing such data, partly because of the economics of charges for the journal pages needed to print the long sequences, but largely because computer examination of the sequences is far more effective than human examination of the data on a printed page. As a result, it has become standard procedure for investigators to use sequence databases such as GenBank and mapping databases such as the Genome Data Base in their research and to submit new results to be incorporated into the databases.

From a computing-oriented perspective, genome analysis is a data-driven science in which researchers must search massive amounts of data for the specific information they need to interpret their experimental results and plan new experiments. The unique product of a gene is a functional protein (such as an enzyme or a structural complex). Many more genes are known than the corresponding proteins; it takes person-years of effort to purify a protein, to understand its function, or to determine its three-dimensional structure. Such research conducted on the current scale would be nearly impossible without computing and information technology.

Such recent major efforts as the Human Genome Project, which seeks to map3 and sequence all human genes, promise to generate data on a scale unprecedented in the history of molecular biology.

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