Abstract
BackgroundKnowledge of interaction types in biological networks is important for understanding the functional organization of the cell. Currently information-based approaches are widely used for inferring gene regulatory interactions from genomics data, such as gene expression profiles; however, these approaches do not provide evidence about the regulation type (positive or negative sign) of the interaction.ResultsThis paper describes a novel algorithm, “Signing of Regulatory Networks” (SIREN), which can infer the regulatory type of interactions in a known gene regulatory network (GRN) given corresponding genome-wide gene expression data. To assess our new approach, we applied it to three different benchmark gene regulatory networks, including Escherichia coli, prostate cancer, and an in silico constructed network. Our new method has approximately 68, 70, and 100 percent accuracy, respectively, for these networks. To showcase the utility of SIREN algorithm, we used it to predict previously unknown regulation types for 454 interactions related to the prostate cancer GRN.ConclusionsSIREN is an efficient algorithm with low computational complexity; hence, it is applicable to large biological networks. It can serve as a complementary approach for a wide range of network reconstruction methods that do not provide information about the interaction type.Electronic supplementary materialThe online version of this article (doi:10.1186/s13015-015-0054-4) contains supplementary material, which is available to authorized users.
Highlights
Knowledge of interaction types in biological networks is important for understanding the functional organization of the cell
Comparison of signing of regulatory networks (SIREN) with a baseline method based on Pearson coefficient correlation (PCC) revealed that it has a greater performance on biological gene regulatory networks (GRNs)
Consistent with previous studies [27], we found the number of bins does not affect the SIREN performance remarkably as long as it is within a reasonable range (Figure 1a) and using spline order greater than three does not improve the quality of prediction significantly (Figure 1b), but rather it increases the computational cost of the algorithm
Summary
This paper describes a novel algorithm, “Signing of Regulatory Networks” (SIREN), which can infer the regulatory type of interactions in a known gene regulatory network (GRN) given corresponding genome-wide gene expression data. To assess our new approach, we applied it to three different benchmark gene regulatory networks, including Escherichia coli, prostate cancer, and an in silico constructed network. Our new method has approximately 68, 70, and 100 percent accuracy, respectively, for these networks. To showcase the utility of SIREN algorithm, we used it to predict previously unknown regulation types for 454 interactions related to the prostate cancer GRN
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