title: Evolving from bioinformatics in-the-small to bioinformatics in-the-large.
abstract: We argue the significance of a fundamental shift in bioinformatics, from in-the-small to in-the-large. Adopting a large-scale perspective is a way to manage the problems endemic to the world of the small-constellations of incompatible tools for which the effort required to assemble an integrated system exceeds the perceived benefit of the integration. Where bioinformatics in-the-small is about data and tools, bioinformatics in-the-large is about metadata and dependencies. Dependencies represent the complexities of large-scale integration, including the requirements and assumptions governing the composition of tools. The popular make utility is a very effective system for defining and maintaining simple dependencies, and it offers a number of insights about the essence of bioinformatics in-the-large. Keeping an in-the-large perspective has been very useful to us in large bioinformatics projects. We give two fairly different examples, and extract lessons from them showing how it has helped. These examples both suggest the benefit of explicitly defining and managing knowledge flows and knowledge maps (which represent metadata regarding types, flows, and dependencies), and also suggest approaches for developing bioinformatics database systems. Generally, we argue that large-scale engineering principles can be successfully adapted from disciplines such as software engineering and data management, and that having an in-the-large perspective will be a key advantage in the next phase of bioinformatics development.
authors.affiliation: Computer Science Department, University of California, Los Angeles, Los Angeles, California 90095-1596, USA. stott@cs.ucla.edu
authors.forename: D Stott
authors.initials: DS
authors.lastname: Parker
authors.forename: Michael M
authors.initials: MM
authors.lastname: Gorlick
authors.forename: Christopher J
authors.initials: CJ
authors.lastname: Lee
mesh_terms: D019295:Computational Biology; D030541:Databases, Genetic