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Global transcription network incorporating distal regulator binding reveals selective cooperation of cancer drivers and risk genes.

Kim K, Yang W, Lee KS, Bang H, Jang K, Kim SC, Yang JO, Park S, Park K, Choi JK - Nucleic Acids Res. (2015)

Bottom Line: Here, we developed a Bayesian probabilistic model and computational method for global causal network construction with breast cancer as a model.Whereas physical regulator binding was well supported by gene expression causality in general, distal elements in intragenic regions or loci distant from the target gene exhibited particularly strong functional effects.Modeling the action of long-range enhancers was critical in recovering true biological interactions with increased coverage and specificity overall and unraveling regulatory complexity underlying tumor subclasses and drug responses in particular.

View Article: PubMed Central - PubMed

Affiliation: Department of Bio and Brain Engineering, KAIST, Daejeon 305-701, Republic of Korea.

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Quantitative prior evaluation. (A) Evaluation of four different test networks built on four different prior subsets. (Left) Distribution of the F1 scores for edges in a key breast cancer subnetwork as calculated by interrogating a manually curated and peer-reviewed pathway database. (Right) The average gene expression correlation of a node with other nodes at a varying network distance. (B) Ratio of the outdegree to indegree of TF nodes in the tested subnetworks. (C) Global network performance of four partial prior models. Convergence patterns were observed in 10 independent GA runs that used each prior subset by tracing the number of recovered edges according to the number of GA generations (left) and by tracing the fitness score according to the number of edges (right).
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Figure 2: Quantitative prior evaluation. (A) Evaluation of four different test networks built on four different prior subsets. (Left) Distribution of the F1 scores for edges in a key breast cancer subnetwork as calculated by interrogating a manually curated and peer-reviewed pathway database. (Right) The average gene expression correlation of a node with other nodes at a varying network distance. (B) Ratio of the outdegree to indegree of TF nodes in the tested subnetworks. (C) Global network performance of four partial prior models. Convergence patterns were observed in 10 independent GA runs that used each prior subset by tracing the number of recovered edges according to the number of GA generations (left) and by tracing the fitness score according to the number of edges (right).

Mentions: We computed the relative fraction of true positive links as the F1 score for each node by querying a manually curated functional interaction database and known TF-target relationship databases (see Methods). The distribution of the F1 scores was compared among the four test networks. As shown in the left panel of Figure 2A and Supplementary Figure S4A, the complete TF prior model led to a network with the largest number of high-F1 links and lowest number of low-F1 links, indicating that modeling long-range TF interactions is essential for accurately recovering true functional interactions. Moreover, the TF prior models were less dependent on gene expression patterns than the prior model as assessed by expression correlations between distant nodes in the network (right panel of FigureĀ 2A). By contrast, the eQTL priors as well as the random priors did not add a substantial amount of additional information beyond the expression patterns. Prior information also appeared to affect network topology. The relative distribution of the outdegree and indegree of TFs in the tested sub-network differed depending on which prior subset was used (Figure 2B).


Global transcription network incorporating distal regulator binding reveals selective cooperation of cancer drivers and risk genes.

Kim K, Yang W, Lee KS, Bang H, Jang K, Kim SC, Yang JO, Park S, Park K, Choi JK - Nucleic Acids Res. (2015)

Quantitative prior evaluation. (A) Evaluation of four different test networks built on four different prior subsets. (Left) Distribution of the F1 scores for edges in a key breast cancer subnetwork as calculated by interrogating a manually curated and peer-reviewed pathway database. (Right) The average gene expression correlation of a node with other nodes at a varying network distance. (B) Ratio of the outdegree to indegree of TF nodes in the tested subnetworks. (C) Global network performance of four partial prior models. Convergence patterns were observed in 10 independent GA runs that used each prior subset by tracing the number of recovered edges according to the number of GA generations (left) and by tracing the fitness score according to the number of edges (right).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4499150&req=5

Figure 2: Quantitative prior evaluation. (A) Evaluation of four different test networks built on four different prior subsets. (Left) Distribution of the F1 scores for edges in a key breast cancer subnetwork as calculated by interrogating a manually curated and peer-reviewed pathway database. (Right) The average gene expression correlation of a node with other nodes at a varying network distance. (B) Ratio of the outdegree to indegree of TF nodes in the tested subnetworks. (C) Global network performance of four partial prior models. Convergence patterns were observed in 10 independent GA runs that used each prior subset by tracing the number of recovered edges according to the number of GA generations (left) and by tracing the fitness score according to the number of edges (right).
Mentions: We computed the relative fraction of true positive links as the F1 score for each node by querying a manually curated functional interaction database and known TF-target relationship databases (see Methods). The distribution of the F1 scores was compared among the four test networks. As shown in the left panel of Figure 2A and Supplementary Figure S4A, the complete TF prior model led to a network with the largest number of high-F1 links and lowest number of low-F1 links, indicating that modeling long-range TF interactions is essential for accurately recovering true functional interactions. Moreover, the TF prior models were less dependent on gene expression patterns than the prior model as assessed by expression correlations between distant nodes in the network (right panel of FigureĀ 2A). By contrast, the eQTL priors as well as the random priors did not add a substantial amount of additional information beyond the expression patterns. Prior information also appeared to affect network topology. The relative distribution of the outdegree and indegree of TFs in the tested sub-network differed depending on which prior subset was used (Figure 2B).

Bottom Line: Here, we developed a Bayesian probabilistic model and computational method for global causal network construction with breast cancer as a model.Whereas physical regulator binding was well supported by gene expression causality in general, distal elements in intragenic regions or loci distant from the target gene exhibited particularly strong functional effects.Modeling the action of long-range enhancers was critical in recovering true biological interactions with increased coverage and specificity overall and unraveling regulatory complexity underlying tumor subclasses and drug responses in particular.

View Article: PubMed Central - PubMed

Affiliation: Department of Bio and Brain Engineering, KAIST, Daejeon 305-701, Republic of Korea.

Show MeSH
Related in: MedlinePlus