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Improved pathway reconstruction from RNA interference screens by exploiting off-target effects.

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Srivatsa Sumana, Kuipers Jack, Schmich Fabian, Eicher Simone, Emmenlauer Mario, Dehio Christoph, Beerenwinkel Niko,
Project Bacterial Type IV Secretion (T4S): Cellular, Molecular, and Evolutionary Basis of the Subversion of Host Cell Functions by Translocated Effector Proteins
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Original article (peer-reviewed)

Journal Bioinformatics (Oxford, England)
Volume (Issue) 34(13)
Page(s) i519 - i527
Title of proceedings Bioinformatics (Oxford, England)
DOI 10.1093/bioinformatics/bty240

Open Access

URL https://edoc.unibas.ch/66848/
Type of Open Access Green OA Embargo (Freely available via Repository after an embargo)

Abstract

MOTIVATION: 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. RESULTS: 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. AVAILABILITY AND IMPLEMENTATION: The software used for the analysis is freely available as an R package at https://github.com/cbg-ethz/pcNEM.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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