Database management for life science research: summary report of the workshop on data management for molecular and cell biology at the National Library of Medicine, Bethesda, Maryland, February 2-3, 2003.

Abstract

OVER THE PAST 15 YEARS, we have witnessed a dramatic transformation in the practice of molecular biology. What was once a cottage industry marked by scarce, expensive data obtained largely by the manual efforts of small groups of graduate students, post-docs, and a few technicians has become industrialized (routinely and robustly high throughput) and data-rich, marked by factory scale sequencing organizations (such as the Joint Genome Institute, the Whitehead Institute, and the Institute for Genomic Research). Such sequencing factories rely on extensive automation of both sequencing and sample preparation. Commencing with sequencing, such industrialization is being extended to high-throughput proteomics and metabolomics, for example. While this industrialization of biological research is partly the result of technological improvements in sequencing instrumentation and automated sample preparation, it is also driven by massive increases in public and private investment and dramatic changes in the social organization of molecular biology (e.g., the creation of highly specialized, factory scale organizations for mass genomic sequencing). Such industrialization and the accompanying growth in molecular biology data availability demand similar scale up and specialization in the data management systems that support and exploit this data gathering. To date, the bioinformatics community has largely made do with custom handcrafted data management software or with conventional database management system (DBMS) technology developed for accounting applications. The industrialization of molecular biology has been largely the province of pharmacological, government, and, to a lesser extent, academic molecular biology research. However, it is clear that we stand at the threshold of clinical application of many of these technologies, for example, as clinical laboratory tests for medical applications. Such clinical applications will entail great increases in the laboratory and data management activities to handle tens or hundreds of millions of assays annually in the United States. Similarly, the approaches and data generation output from ever higher levels of biological complexity will be increasingly data intensive and high throughput. Instruments, data, and data management systems are complementary goods; in other words, their joint consumption is much more useful than consuming a single commodity at a time. It is trivial to say that data management systems are much more useful if they contain data. Consider also how limited the utility of genomic sequence data would be if we could only publish it in books and manually compare it. The availability of data management software that permits the rapid searching of large genomic sequence databases for similar sequences greatly enhances the utility of such sequence data. Quick sequence comparisons are