Design and analysis of Bar-seq experiments.
Design and analysis of Bar-seq experiments.
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DOI：
10.1534/g3.113.008565
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发表时间：
2014-01-10
期刊：
影响因子：
--
通讯作者：
Gresham D
中科院分区：
文献类型：
--
作者：
Robinson DG;Chen W;Storey JD;Gresham D
高通量定量DNA测序使得能够对数千个突变体池进行平行表型分析。然而，适当的分析方法和实验设计，最大限度地提高这些方法的效率，同时保持统计功率目前是未知的。在这里，我们使用Bar-seq分析酿酒酵母酵母缺失文库，系统地测试实验设计参数和序列读取深度对实验结果的影响。我们提出了计算方法，通过调整现有的RNA-seq分析方法，有效和准确地估计效应大小及其统计意义。使用模拟实验设计的变化，我们发现生物重复对于Bar-seq数据的统计分析至关重要，而技术重复的价值较小。通过对序列读段进行二次采样，我们发现当使用四倍生物复制时，每个条件下600万个读段可以达到96%的功效，以5%的错误发现率检测到两倍（或更多）的变化。我们的实验设计和计算分析指南使我们能够在单个测序泳道中研究多达30种不同条件下的酵母缺失收集。这些发现与使用高通量定量DNA测序（包括Tn-seq）的各种合并遗传筛选方法相关。
High-throughput quantitative DNA sequencing enables the parallel phenotyping of pools of thousands of mutants. However, the appropriate analytical methods and experimental design that maximize the efficiency of these methods while maintaining statistical power are currently unknown. Here, we have used Bar-seq analysis of the Saccharomyces cerevisiae yeast deletion library to systematically test the effect of experimental design parameters and sequence read depth on experimental results. We present computational methods that efficiently and accurately estimate effect sizes and their statistical significance by adapting existing methods for RNA-seq analysis. Using simulated variation of experimental designs, we found that biological replicates are critical for statistical analysis of Bar-seq data, whereas technical replicates are of less value. By subsampling sequence reads, we found that when using four-fold biological replication, 6 million reads per condition achieved 96% power to detect a two-fold change (or more) at a 5% false discovery rate. Our guidelines for experimental design and computational analysis enables the study of the yeast deletion collection in up to 30 different conditions in a single sequencing lane. These findings are relevant to a variety of pooled genetic screening methods that use high-throughput quantitative DNA sequencing, including Tn-seq.
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影响因子：
12.3
作者：
Robinson MD;Oshlack A
通讯作者：
Oshlack A
影响因子：
3.6
作者：
Brutinel, Evan D.;Gralnick, Jeffrey A.
通讯作者：
Gralnick, Jeffrey A.
影响因子：
3.3
作者：
Gresham, David;Boer, Viktor M.;Botstein, David
通讯作者：
Botstein, David
影响因子：
5.8
作者：
Chen, Lin S.;Storey, John D.
通讯作者：
Storey, John D.
影响因子：
48
作者：
van Opijnen, Tim;Bodi, Kip L.;Camilli, Andrew
通讯作者：
Camilli, Andrew