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Development of a bioinformatics pipeline for quantitative trait loci (QTL) analysis in ethanol-producing industrial yeast

Grant number: 22/13111-4
Support Opportunities:Scholarships in Brazil - Scientific Initiation
Effective date (Start): December 01, 2022
Effective date (End): November 30, 2023
Field of knowledge:Biological Sciences - Genetics - Quantitative Genetics
Principal Investigator:Marcelo Falsarella Carazzolle
Grantee:Eduardo Menoti Silva
Host Institution: Instituto de Biologia (IB). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil

Abstract

Brazil is a world reference in renewable energy. In terms of fuels, the country has reached high levels of bioethanol production since the creation of the National Alcohol Program (Proálcool) in 1975, becoming the world's second largest biofuel producer. But, whether in Brazil or in other countries, the yeast Saccharomyces cerevisiae assumes a central role in the ethanol production chain. There is today a great demand from the sugar-alcohol and biotechnology industry for yeasts that are both productive and robust to the extremely stressful environment of fermentation vats, a trend that accompanies the growing importance of renewable fuels in the face of the climate crisis. Brazilian industrial yeasts possess a diversity of robustness phenotypes, due to the high selective pressure they are subjected to in the industrial environment of ethanol plants. Prospecting these phenotypes and identifying the associated genetic bases are fundamental to meet these demands of the productive sector. QTL (quantitative trait loci) analyses are one of the most used approaches in yeast to map the genomic origin of quantitative traits. But depending on the phenotype, the window of candidate genes generated by QTL analyses can be complex or imprecise, making experimental validation on the bench difficult due to the considerable number of candidates. In front of this, we propose the creation of a bioinformatics pipeline starting from public data of more than 1000 genomes from different isolates of Saccharomyces cerevisiae, all phenotyped in more than 30 traits, to be used as an additional step in the QTL analyses and make a more fine-grained targeting of the bench validation, thus maximizing the results. With this, the project has the potential both to reduce the experimental costs of validation and to save working time, besides being a publishable computational tool with applications in other areas of interest in yeast.

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