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

Srivatsa, Sumana and Kuipers, Jack and Schmich, Fabian and Eicher, Simone and Emmenlauer, Mario and Dehio, Christoph and Beerenwinkel, Niko. (2018) Improved pathway reconstruction from RNA interference screens by exploiting off-target effects. Bioinformatics, 34 (13). i519-i527.

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Official URL: https://edoc.unibas.ch/66848/

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Abstract

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.
Faculties and Departments:05 Faculty of Science
05 Faculty of Science > Departement Biozentrum > Infection Biology > Molecular Microbiology (Dehio)
UniBasel Contributors:Dehio, Christoph
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:Oxford University Press
ISSN:1367-4803
e-ISSN:1460-2059
Note:Publication type according to Uni Basel Research Database: Journal article
Language:English
Identification Number:
Last Modified:02 Jul 2019 13:19
Deposited On:02 Jul 2019 13:19

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