Research Center for Molecular Medicine of the Austrian Academy of Sciences (CeMM), Vienna, Austria.
BMC Bioinformatics. 2014 Apr 8;15:98. doi: 10.1186/1471-2105-15-98.
Measuring the impact of combinations of genetic or chemical perturbations on cellular fitness, sometimes referred to as synthetic lethal screening, is a powerful method for obtaining novel insights into gene function and drug action. Especially when performed at large scales, gene-gene or gene-drug interaction screens can reveal complex genetic interactions or drug mechanism of action or even identify novel therapeutics for the treatment of diseases.The result of such large-scale screen results can be represented as a matrix with a numeric score indicating the cellular fitness (e.g. viability or doubling time) for each double perturbation. In a typical screen, the majority of combinations do not impact the cellular fitness. Thus, it is critical to first discern true "hits" from noise. Subsequent data exploration and visualization methods can assist to extract meaningful biological information from the data. However, despite the increasing interest in combination perturbation screens, no user friendly open-source program exists that combines statistical analysis, data exploration tools and visualization.
We developed TOPS (Tool for Combination Perturbation Screen Analysis), a Java and R-based software tool with a simple graphical user interface that allows the user to import, analyze, filter and plot data from double perturbation screens as well as other compatible data. TOPS was designed in a modular fashion to allow the user to add alternative importers for data formats or custom analysis scripts not covered by the original release.We demonstrate the utility of TOPS on two datasets derived from functional genetic screens using different methods. Dataset 1 is a gene-drug interaction screen and is based on Luminex xMAP technology. Dataset 2 is a gene-gene short hairpin (sh)RNAi screen exploring the interactions between deubiquitinating enzymes and a number of prominent oncogenes using massive parallel sequencing (MPS).
TOPS provides the benchtop scientist with a free toolset to analyze, filter and visualize data from functional genomic gene-gene and gene-drug interaction screens with a flexible interface to accommodate different technologies and analysis algorithms in addition to those already provided here. TOPS is freely available for academic and non-academic users and is released as open source.
测量遗传或化学扰动对细胞适应性的影响，有时被称为合成致死筛选，是获得基因功能和药物作用新见解的有力方法。特别是在大规模进行时，基因-基因或基因-药物相互作用筛选可以揭示复杂的遗传相互作用或药物作用机制，甚至可以为疾病的治疗确定新的治疗方法。这种大规模筛选结果可以表示为一个矩阵，其中数值分数表示每个双扰动量的细胞适应性（例如生存力或倍增时间）。在典型的筛选中，大多数组合不会影响细胞适应性。因此，首先从噪声中辨别真正的“命中”至关重要。随后的数据探索和可视化方法可以帮助从数据中提取有意义的生物学信息。然而，尽管对组合扰动筛选的兴趣日益浓厚，但没有用户友好的开源程序将统计分析、数据探索工具和可视化结合在一起。
我们开发了 TOPS（组合扰动筛选分析工具），这是一个基于 Java 和 R 的软件工具，具有简单的图形用户界面，允许用户导入、分析、筛选和绘制来自双扰动量筛选以及其他兼容数据的数据。TOPS 采用模块化设计，允许用户为原始版本未涵盖的数据格式或自定义分析脚本添加替代导入程序。我们使用两种不同的方法在两个源自功能基因组基因-基因和基因-药物相互作用筛选的数据集上演示了 TOPS 的实用性。数据集 1 是一个基因-药物相互作用筛选，基于 Luminex xMAP 技术。数据集 2 是一个基因-基因短发夹（sh）RNAi 筛选，使用大规模平行测序（MPS）探索去泛素化酶与许多突出的癌基因之间的相互作用。
TOPS 为基础科学研究人员提供了一个免费的工具集，用于分析、筛选和可视化功能基因组基因-基因和基因-药物相互作用筛选的数据，具有灵活的界面，可以适应不同的技术和分析算法，除了这里已经提供的算法。TOPS 可供学术和非学术用户免费使用，并作为开源发布。
