Bioinformatics and Computational Biology Graduate Program, University of Minnesota Twin Cities, Minneapolis, MN, USA.
Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, MN, USA.
Nat Protoc. 2019 Feb;14(2):415-440. doi: 10.1038/s41596-018-0099-1.
The construction of genome-wide mutant collections has enabled high-throughput, high-dimensional quantitative characterization of gene and chemical function, particularly via genetic and chemical-genetic interaction experiments. As the throughput of such experiments increases with improvements in sequencing technology and sample multiplexing, appropriate tools must be developed to handle the large volume of data produced. Here, we describe how to apply our approach to high-throughput, fitness-based profiling of pooled mutant yeast collections using the BEAN-counter software pipeline (Barcoded Experiment Analysis for Next-generation sequencing) for analysis. The software has also successfully processed data from Schizosaccharomyces pombe, Escherichia coli, and Zymomonas mobilis mutant collections. We provide general recommendations for the design of large-scale, multiplexed barcode sequencing experiments. The procedure outlined here was used to score interactions for ~4 million chemical-by-mutant combinations in our recently published chemical-genetic interaction screen of nearly 14,000 chemical compounds across seven diverse compound collections. Here we selected a representative subset of these data on which to demonstrate our analysis pipeline. BEAN-counter is open source, written in Python, and freely available for academic use. Users should be proficient at the command line; advanced users who wish to analyze larger datasets with hundreds or more conditions should also be familiar with concepts in analysis of high-throughput biological data. BEAN-counter encapsulates the knowledge we have accumulated from, and successfully applied to, our multiplexed, pooled barcode sequencing experiments. This protocol will be useful to those interested in generating their own high-dimensional, quantitative characterizations of gene or chemical function in a high-throughput manner.
基因组规模的突变体文库的构建使高通量、高维的基因和化学功能的定量特征成为可能，特别是通过遗传和化学遗传相互作用实验。随着测序技术和样品多路复用的改进，这些实验的通量增加，必须开发适当的工具来处理产生的大量数据。在这里，我们描述了如何应用我们的方法，使用 BEAN-counter 软件管道（用于下一代测序的带条码的实验分析）对基于适合度的 pooled 突变酵母文库进行高通量、高吞吐量的分析。该软件还成功处理了来自酿酒酵母、大肠杆菌和运动发酵单胞菌突变体文库的数据。我们为大规模、多路复用条码测序实验的设计提供了一般建议。本文概述的程序用于对我们最近发表的近 14000 种化学化合物在七个不同化合物文库中的化学遗传相互作用筛选中约 400 万个化学物质 - 突变体组合的相互作用进行评分。在这里，我们选择了这些数据的一个代表性子集来演示我们的分析管道。BEAN-counter 是开源的，用 Python 编写，可免费用于学术用途。用户应该熟练掌握命令行；希望分析具有数百个或更多条件的更大数据集的高级用户还应该熟悉高通量生物数据分析的概念。BEAN-counter 封装了我们从多路复用、pooled 条码测序实验中积累的知识，并成功地应用于这些实验。对于那些有兴趣以高通量方式生成自己的基因或化学功能的高维、定量特征的人来说，这个方案将非常有用。
