Synthetic Genetic Array Analysis.
Synthetic Genetic Array Analysis.
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DOI：
10.1101/pdb.prot088807
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发表时间：
2016-04-01
影响因子：
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通讯作者：
Boone, Charles
中科院分区：
文献类型：
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作者：
Kuzmin, Elena;Costanzo, Michael;Boone, Charles
遗传相互作用研究已被用于表征未知基因，分配成员的途径和复杂的，并建立一个全面的真核细胞的功能图谱。合成遗传阵列（SGA）方法使酵母遗传分析自动化，并能够系统地绘制遗传相互作用。在其最简单的形式中，SGA由一系列复制品固定步骤组成，这些步骤使得能够通过自动交配和减数分裂重组构建单倍体双突变体。使用该方法，可以将携带查询突变（例如非必需基因的缺失等位基因或必需基因的条件性温度敏感等位基因）的菌株与酵母突变体的输入阵列（例如约5000个活缺失突变体的完整集合）杂交。基于从菌落大小测量得到的细胞适合度的估计，可以针对遗传相互作用对所得到的双突变体的输出阵列进行评分。SGA评分方法可用于分析大规模数据集，而小规模数据集可使用SGAtools进行分析，SGAtools是一个简单的基于Web的界面，包括量化遗传相互作用的所有必要分析步骤。
Genetic interaction studies have been used to characterize unknown genes, assign membership in pathway and complex, and build a comprehensive functional map of a eukaryotic cell. Synthetic genetic array (SGA) methodology automates yeast genetic analysis and enables systematic mapping of genetic interactions. In its simplest form, SGA consists of a series of replica pinning steps that enable construction of haploid double mutants through automated mating and meiotic recombination. Using this method, a strain carrying a query mutation, such as a deletion allele of a nonessential gene or a conditional temperature-sensitive allele of an essential gene, can be crossed to an input array of yeast mutants, such as the complete set of approximately 5000 viable deletion mutants. The resulting output array of double mutants can be scored for genetic interactions based on estimates of cellular fitness derived from colony-size measurements. The SGA score method can be used to analyze large-scale data sets, whereas small-scale data sets can be analyzed using SGAtools, a simple web-based interface that includes all the necessary analysis steps for quantifying genetic interactions.