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Knowledge of the perturbation design is essential for accurate gene regulatory network inference
Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, Box 1031, S-17121 Solna, Sweden..
Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, Box 1031, S-17121 Solna, Sweden..
NYU, Ctr Dev Genet, New York, NY USA..
Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neurooncology and neurodegeneration.ORCID iD: 0000-0003-1758-1262
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2022 (English)In: Scientific Reports, E-ISSN 2045-2322, Vol. 12, article id 16531Article in journal (Refereed) Published
Abstract [en]

The gene regulatory network (GRN) of a cell executes genetic programs in response to environmental and internal cues. Two distinct classes of methods are used to infer regulatory interactions from gene expression: those that only use observed changes in gene expression, and those that use both the observed changes and the perturbation design, i.e. the targets used to cause the changes in gene expression. Considering that the GRN by definition converts input cues to changes in gene expression, it may be conjectured that the latter methods would yield more accurate inferences but this has not previously been investigated. To address this question, we evaluated a number of popular GRN inference methods that either use the perturbation design or not. For the evaluation we used targeted perturbation knockdown gene expression datasets with varying noise levels generated by two different packages, GeneNetWeaver and GeneSpider. The accuracy was evaluated on each dataset using a variety of measures. The results show that on all datasets, methods using the perturbation design matrix consistently and significantly outperform methods not using it. This was also found to be the case on a smaller experimental dataset from E. coli. Targeted gene perturbations combined with inference methods that use the perturbation design are indispensable for accurate GRN inference.

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Springer Nature, 2022. Vol. 12, article id 16531
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Bioinformatics and Systems Biology
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URN: urn:nbn:se:uu:diva-487635DOI: 10.1038/s41598-022-19005-xISI: 000865282300021PubMedID: 36192495OAI: oai:DiVA.org:uu-487635DiVA, id: diva2:1707382
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Swedish Foundation for Strategic ResearchAvailable from: 2022-10-31 Created: 2022-10-31 Last updated: 2022-10-31Bibliographically approved

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Nelander, Sven

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