酵母培养物的全基因组定量适应性分析（QFA）

Genome-Wide Quantitative Fitness Analysis (QFA) of Yeast Cultures.

作者信息

Holstein Eva-Maria, Lawless Conor, Banks Peter, Lydall David

机构信息

Institute for Cell & Molecular Biosciences, Medical School, Newcastle University, Framlington Place, Newcastle upon Tyne, NE2 4HH, UK.

出版信息

Methods Mol Biol. 2018;1672:575-597. doi: 10.1007/978-1-4939-7306-4_38.

DOI:10.1007/978-1-4939-7306-4_38

PMID:29043649

Abstract

We provide a detailed protocol for robot-assisted, genome-wide measurement of fitness in the model yeast Saccharomyces cerevisiae using Quantitative Fitness Analysis (QFA). We first describe how we construct thousands of double or triple mutant yeast strains in parallel using Synthetic Genetic Array (SGA) procedures. Strains are inoculated onto solid agar surfaces by liquid spotting followed by repeated photography of agar plates. Growth curves are constructed and the fitness of each strain is estimated. Robot-assisted QFA, can be used to identify genetic interactions and chemical sensitivity/resistance in genome-wide experiments, but QFA can also be used in smaller scale, manual workflows.

摘要

我们提供了一份详细的实验方案，用于使用定量适应性分析（QFA）在模式酵母酿酒酵母中进行机器人辅助的全基因组适应性测量。我们首先描述如何使用合成遗传阵列（SGA）程序并行构建数千个双突变或三突变酵母菌株。通过液体点样将菌株接种到固体琼脂表面，随后对琼脂平板进行重复拍照。构建生长曲线并估计每个菌株的适应性。机器人辅助的QFA可用于在全基因组实验中识别遗传相互作用和化学敏感性/抗性，但QFA也可用于规模较小的手动工作流程。