Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.
SIB Swiss Institute of Bioinformatics, Basel, Switzerland.
Bioinformatics. 2018 Jul 1;34(13):i519-i527. doi: 10.1093/bioinformatics/bty240.
Pathway reconstruction has proven to be an indispensable tool for analyzing the molecular mechanisms of signal transduction underlying cell function. Nested effects models (NEMs) are a class of probabilistic graphical models designed to reconstruct signalling pathways from high-dimensional observations resulting from perturbation experiments, such as RNA interference (RNAi). NEMs assume that the short interfering RNAs (siRNAs) designed to knockdown specific genes are always on-target. However, it has been shown that most siRNAs exhibit strong off-target effects, which further confound the data, resulting in unreliable reconstruction of networks by NEMs.
Here, we present an extension of NEMs called probabilistic combinatorial nested effects models (pc-NEMs), which capitalize on the ancillary siRNA off-target effects for network reconstruction from combinatorial gene knockdown data. Our model employs an adaptive simulated annealing search algorithm for simultaneous inference of network structure and error rates inherent to the data. Evaluation of pc-NEMs on simulated data with varying number of phenotypic effects and noise levels as well as real data demonstrates improved reconstruction compared to classical NEMs. Application to Bartonella henselae infection RNAi screening data yielded an eight node network largely in agreement with previous works, and revealed novel binary interactions of direct impact between established components.
The software used for the analysis is freely available as an R package at https://github.com/cbg-ethz/pcNEM.git.
Supplementary data are available at Bioinformatics online.
途径重建已被证明是分析细胞功能信号转导分子机制的不可或缺的工具。嵌套效应模型（NEM）是一类概率图形模型，旨在从 RNA 干扰（RNAi）等扰动实验产生的高维观测结果中重建信号通路。NEM 假设设计用于敲低特定基因的短干扰 RNA（siRNA）总是针对目标。然而，已经表明大多数 siRNA 表现出强烈的脱靶效应，这进一步混淆了数据，导致 NEM 对网络的重建不可靠。
在这里，我们提出了一种称为概率组合嵌套效应模型（pc-NEM）的 NEM 扩展，该模型利用辅助 siRNA 脱靶效应从组合基因敲低数据中进行网络重建。我们的模型采用自适应模拟退火搜索算法，用于同时推断数据固有的网络结构和误差率。对具有不同数量表型效应和噪声水平的模拟数据以及真实数据的 pc-NEM 评估表明，与经典 NEM 相比，重建得到了改善。应用于 Bartonella henselae 感染 RNAi 筛选数据的结果产生了一个八个节点的网络，与以前的工作基本一致，并揭示了先前建立的组件之间直接影响的新的二进制相互作用。
用于分析的软件可作为 R 包在 https://github.com/cbg-ethz/pcNEM.git 上免费获得。
补充数据可在生物信息学在线获得。
