Functional Data Analysis: Omics for Environmental Risk Assessment

Abstract The genome revolution has radically changed our view of biological systems. The quantitative determination of changes in all the major molecular components of the living cells, the “omics” approach, opened new fields for essentially all life sciences. Omic techniques generate huge datasets, usually at the high Gb/Tb level, that need to be decoded and interpreted to become understandable and manageable. Whereas multivariant data analyses are very efficient in reducing datasets to limited and workable sets of variables, the analysis of the biological results requires a biological interpretation, not only a statistical one. Ideally, the objective is to link omic data to specific, recognizable phenotypes causally related to the observed omic changes/variations (bottom-up approach), as well as to use them to identify the molecular events that triggered those changes (top-down approach). Fortunately, the existence of massive databases with omic information from thousands of previously published data facilitates to obtain this information, without which most of the omic data would be essentially useless. In this chapter, we explore the challenges, limitations, menaces, and future prospects of converting omic datasets in useful pieces of information, with special emphasis on environmental and human health risk assessment.