Limits...
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.


Reconstructing GRN of human breast epithelium and comparison with LASSO. (A) Prior network based on TFBS information. (B) PPI among TFs. (C) The gold standard network. (D) AUROCs of LASSO and BVS algorithms under different prior settings. (E) AUPRs of LASSO and BVS algorithms under different prior settings. (F) Sensitivity of the BVS algorithm to the confidence parameter (αc). Here, TFBS represents the prior network constructed from TFBS data, TFBS + PPI represents the prior network constructed from both TFBS and PPI information, α = 1, 2, 3, 4 represents the networks inferred from ΓTFBS+PPI with confidence parameters αc = 1, 2, 3, 4, respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Reconstructing GRN of human breast epithelium and comparison with LASSO. (A) Prior network based on TFBS information. (B) PPI among TFs. (C) The gold standard network. (D) AUROCs of LASSO and BVS algorithms under different prior settings. (E) AUPRs of LASSO and BVS algorithms under different prior settings. (F) Sensitivity of the BVS algorithm to the confidence parameter (αc). Here, TFBS represents the prior network constructed from TFBS data, TFBS + PPI represents the prior network constructed from both TFBS and PPI information, α = 1, 2, 3, 4 represents the networks inferred from ΓTFBS+PPI with confidence parameters αc = 1, 2, 3, 4, respectively.

Mentions: Four different prior settings were used for the BVS framework. The parameter settings for the flat and sparse priors were left the same as before. TFBS information were collected from ENCODE (Hughes et al., 2000; Ernst et al., 2010), MEME (Bailey et al., 2009), TRANSFAC (Bryne et al., 2008), and JASPER (Matys et al., 2006) to construct the prior network (ΓTFBS) that contains only direct gene regulations (Figure 4A). This network (ΓTFBS) contains 4963 number of potential gene regulations between 93 TFs and 1317 target genes (Table S6 in Supplementary Material). Information regarding PPI among TFs (Figure 4B) was collected from physical TF binding data published by Ravasi et al. (2010) (Table S7 in Supplementary Material). This information along with the TFBS data were used to construct a second prior network (ΓTFBS+PPI) which contains 16,372 potential regulatory interactions supported by both types of data (Table S8 in Supplementary Material). The confidence parameter (αc) was set to 2 and the restriction parameter (k) were assigned a value of 12 . The above prior settings, when used with the proposed BVS framework led to four different posterior networks that were then used for performance evaluation and comparison purposes.


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

Santra T - Front Bioeng Biotechnol (2014)

Reconstructing GRN of human breast epithelium and comparison with LASSO. (A) Prior network based on TFBS information. (B) PPI among TFs. (C) The gold standard network. (D) AUROCs of LASSO and BVS algorithms under different prior settings. (E) AUPRs of LASSO and BVS algorithms under different prior settings. (F) Sensitivity of the BVS algorithm to the confidence parameter (αc). Here, TFBS represents the prior network constructed from TFBS data, TFBS + PPI represents the prior network constructed from both TFBS and PPI information, α = 1, 2, 3, 4 represents the networks inferred from ΓTFBS+PPI with confidence parameters αc = 1, 2, 3, 4, respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Reconstructing GRN of human breast epithelium and comparison with LASSO. (A) Prior network based on TFBS information. (B) PPI among TFs. (C) The gold standard network. (D) AUROCs of LASSO and BVS algorithms under different prior settings. (E) AUPRs of LASSO and BVS algorithms under different prior settings. (F) Sensitivity of the BVS algorithm to the confidence parameter (αc). Here, TFBS represents the prior network constructed from TFBS data, TFBS + PPI represents the prior network constructed from both TFBS and PPI information, α = 1, 2, 3, 4 represents the networks inferred from ΓTFBS+PPI with confidence parameters αc = 1, 2, 3, 4, respectively.
Mentions: Four different prior settings were used for the BVS framework. The parameter settings for the flat and sparse priors were left the same as before. TFBS information were collected from ENCODE (Hughes et al., 2000; Ernst et al., 2010), MEME (Bailey et al., 2009), TRANSFAC (Bryne et al., 2008), and JASPER (Matys et al., 2006) to construct the prior network (ΓTFBS) that contains only direct gene regulations (Figure 4A). This network (ΓTFBS) contains 4963 number of potential gene regulations between 93 TFs and 1317 target genes (Table S6 in Supplementary Material). Information regarding PPI among TFs (Figure 4B) was collected from physical TF binding data published by Ravasi et al. (2010) (Table S7 in Supplementary Material). This information along with the TFBS data were used to construct a second prior network (ΓTFBS+PPI) which contains 16,372 potential regulatory interactions supported by both types of data (Table S8 in Supplementary Material). The confidence parameter (αc) was set to 2 and the restriction parameter (k) were assigned a value of 12 . The above prior settings, when used with the proposed BVS framework led to four different posterior networks that were then used for performance evaluation and comparison purposes.

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.