Perturbations revealed to be indispensable for Gene Regulatory Networks
A research group led by SciLifeLab researcher Erik Sonnhammer (SU) have shown that knowledge of the perturbation design is essential for accurate Gene Regulatory Network (GRN) inference. Using this knowledge significantly enhances the ability to reconstruct the correct GRN.
GRN inference is tremendously important for understanding biological processes. Two distinct classes of methods exist 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., which genes were perturbed (typically knocked down) to cause the changes in gene expression.
Which of these approaches is more accurate? For the first time, this question has been systematically addressed in a recent study published in Nature Scientific Reports and led by SciLifeLab researcher Erik Sonnhammer (Stockholm University).
Several popular GRN inference methods that either use the perturbation design or not were evaluated and their accuracy was evaluated on multiple datasets using a variety of measures. The results show that methods using the perturbation design matrix consistently outperform methods not using it on all datasets.
This study shows that targeted gene perturbations combined with inference methods that use the perturbation design are indispensable for accurate GRN inference.
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