Integrative computational approaches reveal the Saccharomyces cerevisiae pheno-metabolomic profile.

Genome sequencing is essential to understand individual variation and to study the mechanisms that explain relations between genotype and phenotype. The accumulated knowledge from large-scale genome sequencing projects of Saccharomyces cerevisiae isolates is being used to study the mechanisms that explain such relations. In our previous work we undertook genetic and phenotypic characterization of 172 S. cerevisiae strains from different geographical origins and technological groups, using 11 polymorphic microsatellites and 30 phenotypic tests with biotechnological relevance. We confirm the strength of these associations by construction and cross-validation of computational models that can estimate a strain technological group and phenotype from microsatellite allelic combinations as tools for preliminary yeast strain selection. From these results a subset of the most diverse strains were chosen, and whole-genome sequencing was performed in order to obtain an holistic view of the genome variability associated with the differences observed previously.

基因组测序是解析个体遗传变异、阐明基因型（genotype）与表型（phenotype）之间关联机制的关键技术手段。目前，针对酿酒酵母（Saccharomyces cerevisiae）分离株开展的大规模基因组测序项目所积累的研究数据，正被用于阐释上述基因型与表型间的关联机制。在本团队此前的研究中，我们针对来自不同地理来源与技术应用类群的172株酿酒酵母菌株，采用11个多态性微卫星（microsatellite）标记与30项具备生物技术应用价值的表型检测实验，完成了遗传与表型特征分析。本研究通过构建并交叉验证可基于微卫星等位基因组合预测菌株技术类群与表型的计算模型，验证了上述关联的可靠性，该模型可作为酿酒酵母菌株初步筛选的辅助工具。基于上述研究结果，我们选取了其中多样性最高的菌株子集开展全基因组测序，以全面解析与此前观测到的表型差异相关的基因组变异特征。