Abstract
Reconstruction of gene regulatory networks (GRNs) from experimental data is a fundamental challenge in systems biology. A number of computational approaches have been developed to infer GRNs from mRNA expression profiles. However, expression profiles alone are proving to be insufficient for inferring GRN topologies with reasonable accuracy. Recently, it has been shown that integration of external data sources (such as gene and protein sequence information, gene ontology data, protein–protein interactions) with mRNA expression profiles may increase the reliability of the inference process. Here, I propose a new approach that incorporates transcription factor binding sites (TFBS) and physical protein interactions (PPI) among transcription factors (TFs) in a Bayesian variable selection (BVS) algorithm which can infer GRNs from mRNA expression profiles subjected to genetic perturbations. Using real experimental data, I show that the integration of TFBS and PPI data with mRNA expression profiles leads to significantly more accurate networks than those inferred from expression profiles alone. Additionally, the performance of the proposed algorithm is compared with a series of least absolute shrinkage and selection operator (LASSO) regression-based network inference methods that can also incorporate prior knowledge in the inference framework. The results of this comparison suggest that BVS can outperform LASSO regression-based method in some circumstances.
Highlights
Understanding how genes regulate each other to orchestrate cellular phenotypes is a fundamental challenge of Biology
The AUROC values calculated under different prior settings were compared using Mann–Whitney U test (Mann and Whitney, 1947) to assess the effects of different network priors on the accuracy of the proposed Bayesian variable selection (BVS) algorithm. These results suggest that the BVS framework that incorporates both the transcription factor binding sites (TFBS) and physical protein interactions (PPI) data performed better than those which incorporate no prior information (p = 0.99 × 10−6), only TFBS information (p = 2.05 × 10−4) as prior knowledge, and the sparse prior (p = 2.4 × 10−6)
The main hypothesis behind this approach was that integrating protein interactions among transcription factors (TFs) with TFBS data increases the predictive power of the inference process, especially in a variable selection setting
Summary
Understanding how genes regulate each other to orchestrate cellular phenotypes is a fundamental challenge of Biology. An implementation of this method to infer a liver-specific GRN is discussed in Section “Inferring LiverSpecific Gene Regulatory Network from Perturbation Response Data.” I compared the performance of the proposed BVS algorithm with our previous work.
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