Information systems approaches to support discovery in agricultural genomics

Abstract

Reductionist approaches to biological questions have provided important insights to genetic mechanisms and genome structures. These approaches have also led to the engineering of technologies that generate data at rates that far exceed our ability to comprehend their meaning. If biologists are going to address the fundamental questions concerning the complexity that underlies growth, development and phenotypic variability, then there is an unprecedented need to acquire, understand, manipulate, and exploit high-value genomic information. Further, for genomics to deliver the promise of using biological processes to make agricultural systems more efficient, sustainable, and environmentally friendly, various types of biological data will need to be integrated to reveal the functional relationships between DNA, RNA, proteins, environment and phenotypes. Such integrative approaches will rely heavily on development of information systems (IS) and data analysis methods that will require the same rigor and resources that have been applied to development of laboratory experimental protocols. In addition, software and process engineering will need to be applied to information systems development much in the same way as engineering principles have been applied to data production in biological laboratories, resulting in a shift from cottage industry to industrial scale biological data factories.