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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.


Integrating external data to infer liver-specific transcription regulatory network (Tomaru et al., 2009). (A) Prior network constructed from TFBS information. (B) PPI among transcription factors. (C) Network inferred by flat prior (P(Ai) = γ)). (D) Network inferred using sparse prior. (E) Network inferred using prior network constructed from TFBS information only. (F) Network inferred using prior network constructed from TFBS and PPI information. The interactions that occur with high and low posterior probabilities are represented by darker/thicker and lighter/thinner edges, respectively, in (C), (D), (E), and (F). (G) Average ROC curves of the inferred networks. (H) Average PR curves of the inferred networks. (I) mean and standard deviation of the area under the ROC and PR curves of the inferred networks.
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Figure 2: Integrating external data to infer liver-specific transcription regulatory network (Tomaru et al., 2009). (A) Prior network constructed from TFBS information. (B) PPI among transcription factors. (C) Network inferred by flat prior (P(Ai) = γ)). (D) Network inferred using sparse prior. (E) Network inferred using prior network constructed from TFBS information only. (F) Network inferred using prior network constructed from TFBS and PPI information. The interactions that occur with high and low posterior probabilities are represented by darker/thicker and lighter/thinner edges, respectively, in (C), (D), (E), and (F). (G) Average ROC curves of the inferred networks. (H) Average PR curves of the inferred networks. (I) mean and standard deviation of the area under the ROC and PR curves of the inferred networks.

Mentions: In the third setting, only TFBS information was used to construct the prior network (Figure 2A). TFBS information were collected from HTRIDB (Bovolenta et al., 2012), MEME (Bailey et al., 2009), TRANSFAC (Bryne et al., 2008), JASPER (Matys et al., 2006), TRED (Jiang et al., 2007), and SABioscience (www.sabiosciences.com). Here, only those TFBS that were found within a 5000 bp region of the gene promoters were included in the analysis. This resulted in a total of 106 potential transcriptional regulations (excluding autoregulations, see Table S1 in Supplementary Material for details) among the 21 TFs mentioned above. These regulatory interactions were represented by a prior adjacency matrix (ΓTFBS) whose non-zero elements represent potential gene regulations and are assigned a value of αc = 2. The ith row of this matrix (ΓTFBS) represents our prior knowledge on the regulatory program of the ith gene gi, based solely on TFBS information, and was used to formulate the prior distribution of Ai.


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

Santra T - Front Bioeng Biotechnol (2014)

Integrating external data to infer liver-specific transcription regulatory network (Tomaru et al., 2009). (A) Prior network constructed from TFBS information. (B) PPI among transcription factors. (C) Network inferred by flat prior (P(Ai) = γ)). (D) Network inferred using sparse prior. (E) Network inferred using prior network constructed from TFBS information only. (F) Network inferred using prior network constructed from TFBS and PPI information. The interactions that occur with high and low posterior probabilities are represented by darker/thicker and lighter/thinner edges, respectively, in (C), (D), (E), and (F). (G) Average ROC curves of the inferred networks. (H) Average PR curves of the inferred networks. (I) mean and standard deviation of the area under the ROC and PR curves of the inferred networks.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
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getmorefigures.php?uid=PMC4126456&req=5

Figure 2: Integrating external data to infer liver-specific transcription regulatory network (Tomaru et al., 2009). (A) Prior network constructed from TFBS information. (B) PPI among transcription factors. (C) Network inferred by flat prior (P(Ai) = γ)). (D) Network inferred using sparse prior. (E) Network inferred using prior network constructed from TFBS information only. (F) Network inferred using prior network constructed from TFBS and PPI information. The interactions that occur with high and low posterior probabilities are represented by darker/thicker and lighter/thinner edges, respectively, in (C), (D), (E), and (F). (G) Average ROC curves of the inferred networks. (H) Average PR curves of the inferred networks. (I) mean and standard deviation of the area under the ROC and PR curves of the inferred networks.
Mentions: In the third setting, only TFBS information was used to construct the prior network (Figure 2A). TFBS information were collected from HTRIDB (Bovolenta et al., 2012), MEME (Bailey et al., 2009), TRANSFAC (Bryne et al., 2008), JASPER (Matys et al., 2006), TRED (Jiang et al., 2007), and SABioscience (www.sabiosciences.com). Here, only those TFBS that were found within a 5000 bp region of the gene promoters were included in the analysis. This resulted in a total of 106 potential transcriptional regulations (excluding autoregulations, see Table S1 in Supplementary Material for details) among the 21 TFs mentioned above. These regulatory interactions were represented by a prior adjacency matrix (ΓTFBS) whose non-zero elements represent potential gene regulations and are assigned a value of αc = 2. The ith row of this matrix (ΓTFBS) represents our prior knowledge on the regulatory program of the ith gene gi, based solely on TFBS information, and was used to formulate the prior distribution of Ai.

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.