基于高含量双RNAi筛选微扰数据的遗传相互作用识别和预测集成分析管道

Zheng Yin, Fuhai Li, Stephen T. C. Wong
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引用次数: 0

摘要

在本文中,我们描述了一个集成的数据分析管道,用于识别和预测基于细胞对单因子和多因子扰动的反应的遗传相互作用。该管道是在全基因组单rnai筛选和较小规模双rnai筛选果蝇KC-167细胞系的背景下开发的,目的是重建调节细胞形状变化的分子途径。XSEDE框架下的TACC (Texas Advanced Computing Center)从其Stampede系统中分配了100,000个服务单元(service units, su),用于利用果蝇细胞的荧光图像进行图像量化和信号通路建模,最近有报道称进行了全基因组范围的单RNAi筛选[1]。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Integrated Analytic Pipeline for Identifying and Predicting Genetic Interactions based on Perturbation Data from High Content Double RNAi Screening
In this paper, we describe an integrated data analysis pipeline for identifying and predicting genetic interactions based on cellular responses to perturbations of single- and multiple-agents. This pipeline was developed in the context of genome wide single-RNAi screens and smaller scale double-RNAi screens using Drosophila KC-167 cell lines, with the aim to reconstruct the molecular pathways regulating changes in cell shape. The TACC (Texas Advanced Computing Center) under XSEDE framework allocated 100,000 service unites (SUs) from its Stampede system to facilitate image quantification and signaling pathway modeling using fluorescence images of Drosophila cells, and recently a kinome-wide single RNAi screening has been reported [1].
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