Frontiers in Genomic Assay Technologies: The Grand Challenges in Enabling Data-Intensive Biological Research
We live in astonishing times, not least when we consider the recent rate of progress in science. We are experiencing a revolution, underpinned and empowered by Moore’s famous law and the computational power enabled by that law (Mollick, 2006), that has largely eliminated the traditional sci-entific bottle-neck associated with data acquisition. Biologists now find themselves a situation familiar to any of us that has, over the last decade or so, replaced their conventional film camera with its digital counterpart. The biologist now has the ability to take technological “snapshots” of almost any attribute of a living organism, whether it be DNA sequence, protein iden-tity, 3-, 4-, and 5-dimensional distributions of macromolecules, composition of small molecules, and ions, identity of mutations, the mapping of phenotypes to genotypes, and so-on, and this has provided an unprec-edented amount of data for analysis. This situation, which extends across all fields of experimental science, has been canonized as the emergence of a “Fourth Paradigm” in scientific research, termed Data-Intensive Scientific Discovery, the first, second, and third paradigms representing conven-tional empirical observation, abstraction and theory, and computational simula-tion, respectively (Gray, 2009). As you will see below, biologists now face much of the same grand challenges as experienced by the recreational photographer.The purpose of this article is to sum-marize grand challenges confronted at the frontier of genomic assay technology, and to provide some talking points as to the purpose of this new journal. Several grand challenges are relatively easy to define. At least three are directly associated with our recent and unprecedented ability to gener -ate biological data in digital form. Given this ability, immediate challenges are: (1) How does one design the most meaning ful experiments to generate the most useful data? (2) How does one handle the resultant data stream? and (3) What is the best way to archive data, and to harvest and integrate this archived data?A fourth grand challenge naturally arises from the process of technology innova-tion, being associated with the identifi-cation of new ways to analyze biological organisms and systems, and the develop-ment of the associated technologies and experimental platforms.A final grand challenge arises from the concept of stepping back and focusing on the source materials used for generating biological knowledge, the families, genera, and species, discovered and undiscovered, representing life on this planet, and asking the question as to whether we know all pos-sible ways in which evolution can give rise to successful life-forms based on detailed understanding and study of only a very lim-ited subset of these life-forms.