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Inferring interaction type in gene regulatory networks using co-expression data.

Khosravi P, Gazestani VH, Pirhaji L, Law B, Sadeghi M, Goliaei B, Bader GD - Algorithms Mol Biol (2015)

Bottom Line: 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.Our new method has approximately 68, 70, and 100 percent accuracy, respectively, for these 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.

View Article: PubMed Central - PubMed

Affiliation: School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran ; The Donnelly Centre, University of Toronto, Toronto, Canada.

ABSTRACT

Background: Knowledge 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.

Results: 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.

Conclusions: SIREN 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.

No MeSH data available.


Related in: MedlinePlus

Close relationship between Pearson correlation coefficient and SIREN. Three figures illustrate the relationship between PCC and SIREN results for three GRNs: a in silico GRN, b prostate cancer GRN, and cE. coli high confidence GRN.
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Fig10: Close relationship between Pearson correlation coefficient and SIREN. Three figures illustrate the relationship between PCC and SIREN results for three GRNs: a in silico GRN, b prostate cancer GRN, and cE. coli high confidence GRN.

Mentions: PCC has been widely used to decipher the interaction type based on transcriptome data [55, 56]. We compared SIREN with PCC by applying both to all three GRNs. This comparison revealed that their overall results are similar, suggesting that most regulatory associations in the considered GRNs have a linear or monotonic nature (Figure 10). However, the results of PCC and SIREN are inconsistent for some interactions. For example two genes with an activating relationship from STRING (EGR1 and FGF2) [57] are determined to have an inhibition relationship using PCC while SIREN inferred a positive association. On the other hand MICA and IL10 show an inhibition association [58] by STRING and SIREN; while they have positive association based on PCC. Ultimately, SIREN shows superior performance in all but the in silico network case (Figure 11), indicating that consideration of non-linear relationships in the gene expression data is useful.Figure 10


Inferring interaction type in gene regulatory networks using co-expression data.

Khosravi P, Gazestani VH, Pirhaji L, Law B, Sadeghi M, Goliaei B, Bader GD - Algorithms Mol Biol (2015)

Close relationship between Pearson correlation coefficient and SIREN. Three figures illustrate the relationship between PCC and SIREN results for three GRNs: a in silico GRN, b prostate cancer GRN, and cE. coli high confidence GRN.
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4495944&req=5

Fig10: Close relationship between Pearson correlation coefficient and SIREN. Three figures illustrate the relationship between PCC and SIREN results for three GRNs: a in silico GRN, b prostate cancer GRN, and cE. coli high confidence GRN.
Mentions: PCC has been widely used to decipher the interaction type based on transcriptome data [55, 56]. We compared SIREN with PCC by applying both to all three GRNs. This comparison revealed that their overall results are similar, suggesting that most regulatory associations in the considered GRNs have a linear or monotonic nature (Figure 10). However, the results of PCC and SIREN are inconsistent for some interactions. For example two genes with an activating relationship from STRING (EGR1 and FGF2) [57] are determined to have an inhibition relationship using PCC while SIREN inferred a positive association. On the other hand MICA and IL10 show an inhibition association [58] by STRING and SIREN; while they have positive association based on PCC. Ultimately, SIREN shows superior performance in all but the in silico network case (Figure 11), indicating that consideration of non-linear relationships in the gene expression data is useful.Figure 10

Bottom Line: 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.Our new method has approximately 68, 70, and 100 percent accuracy, respectively, for these 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.

View Article: PubMed Central - PubMed

Affiliation: School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran ; The Donnelly Centre, University of Toronto, Toronto, Canada.

ABSTRACT

Background: Knowledge 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.

Results: 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.

Conclusions: SIREN 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.

No MeSH data available.


Related in: MedlinePlus