Bioinformatics applied to Bioenergy: Probabilistic annotation of cane sugar the metabolome

Abstract

The pressing need to decrease consumption of fossil fuels and reduce emissions of greenhouse gases have increased the interest in the production of biofuels, especially ethanol. Understanding the mechanisms of sucrose accumulation in sugar cane is central to the rationalization of breeding programs using traditional methods of selection or genetic engineering, in order to reach a sustainable production of ethanol. Genetic mechanisms of sucrose accumulation in sugar cane have been studied from many angles. However, these studies focused mainly on the characterization of sugar levels and the enzymes directly involved in the production of sucrose in an attempt to identify key control points in the system. The sheer volume of information obtained introduced questions that can not be answered only by analyzing omics data separately, requiring a system biology approach, integrating information from genome, proteome and metabolome. The increasing potential for generating metabolic profiles makes the ability of data screening and reliable comparative analysis a strictly necessary condition for the success of metabolomics in sugar cane. Therefore, it is necessary to develop new tools for the effective handling of information in silico. In this project, we intend to bring to the field of metabolomics, the same kind of revolution experienced by the field of proteomics, with the introduction of probabilistic tools for accurate annotation of metabolic measurements in high-throughput mass spectrometry. This will be achieved by probabilistic method for the assignment of empirical formulas to peaks of mass, which allows the identification of metabolic pathways through the relationships between the metabolites.