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A bayesian framework that integrates heterogeneous data for inferring gene regulatory networks.

Santra T - Front Bioeng Biotechnol (2014)

Bottom Line: Reconstruction of gene regulatory networks (GRNs) from experimental data is a fundamental challenge in systems biology.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.

View Article: PubMed Central - PubMed

Affiliation: Systems Biology Ireland, University College Dublin , Dublin , Ireland.

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.

No MeSH data available.


Constructing prior transcription regulatory network using TFBS and PPI data.
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Figure 1: Constructing prior transcription regulatory network using TFBS and PPI data.

Mentions: Step 3: The above information is then used to build a prior network that contains both direct and indirect regulations supported by external data. Potential direct regulations are identified using TFBS information as described above (see Step 1). Potential indirect regulations are identified based on the assumption that if a TF binds to another TF which targets a certain gene, then the former indirectly regulates the target of the later (Figure 1). Both direct and indirect regulations are incorporated in the prior network as potential transcriptional interactions. The prior network is represented by a weighted adjacency matrix (Γ). The non-zero elements of this matrix represent potential transcriptional regulations supported by TFBS and PPI data. The value of a non-zero element (Γij ≠ 0) represents our confidence on the regulation of a gene (gi) by another (gj). In this study, equal confidence is placed on all potential transcriptional regulations that are supported by TFBS and PPI data, i.e., Γij = αc if gene gi has a TFBS for gj or any of its binding partners. Here, αc is called the confidence parameter. The ith row (Γi) of the prior adjacency matrix (Γ) represents our prior knowledge about the regulatory program of gene gi and is used to formulate the prior distribution of the binary vector Ai in the following manner.(5)P(Ai)∝exp(ΓiTAi):Ai∈ζk=0  otherwise.


A bayesian framework that integrates heterogeneous data for inferring gene regulatory networks.

Santra T - Front Bioeng Biotechnol (2014)

Constructing prior transcription regulatory network using TFBS and PPI data.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4126456&req=5

Figure 1: Constructing prior transcription regulatory network using TFBS and PPI data.
Mentions: Step 3: The above information is then used to build a prior network that contains both direct and indirect regulations supported by external data. Potential direct regulations are identified using TFBS information as described above (see Step 1). Potential indirect regulations are identified based on the assumption that if a TF binds to another TF which targets a certain gene, then the former indirectly regulates the target of the later (Figure 1). Both direct and indirect regulations are incorporated in the prior network as potential transcriptional interactions. The prior network is represented by a weighted adjacency matrix (Γ). The non-zero elements of this matrix represent potential transcriptional regulations supported by TFBS and PPI data. The value of a non-zero element (Γij ≠ 0) represents our confidence on the regulation of a gene (gi) by another (gj). In this study, equal confidence is placed on all potential transcriptional regulations that are supported by TFBS and PPI data, i.e., Γij = αc if gene gi has a TFBS for gj or any of its binding partners. Here, αc is called the confidence parameter. The ith row (Γi) of the prior adjacency matrix (Γ) represents our prior knowledge about the regulatory program of gene gi and is used to formulate the prior distribution of the binary vector Ai in the following manner.(5)P(Ai)∝exp(ΓiTAi):Ai∈ζk=0  otherwise.

Bottom Line: Reconstruction of gene regulatory networks (GRNs) from experimental data is a fundamental challenge in systems biology.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.

View Article: PubMed Central - PubMed

Affiliation: Systems Biology Ireland, University College Dublin , Dublin , Ireland.

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.

No MeSH data available.