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Uncovering packaging features of co-regulated modules based on human protein interaction and transcriptional regulatory networks.

Chen L, Wang H, Zhang L, Li W, Wang Q, Shang Y, He Y, He W, Li X, Tai J, Li X - BMC Bioinformatics (2010)

Bottom Line: Here, we weighted edges of a human protein interaction network and a transcriptional regulatory network to construct an integrated network, and introduce a probabilistic model and a bipartite graph framework to exploit human co-regulated modules and uncover their specific features in packaging different biological entities (genes, protein complexes or metabolic pathways).Finally, we identified 96 human co-regulated modules based on this method, and evaluate its effectiveness by comparing it with four other methods.Therefore, we focussed on an example co-regulated module and found that it could integrate a number of cancer-related genes.

View Article: PubMed Central - HTML - PubMed

Affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, China. chenlina_2004@yahoo.com.cn

ABSTRACT

Background: Network co-regulated modules are believed to have the functionality of packaging multiple biological entities, and can thus be assumed to coordinate many biological functions in their network neighbouring regions.

Results: Here, we weighted edges of a human protein interaction network and a transcriptional regulatory network to construct an integrated network, and introduce a probabilistic model and a bipartite graph framework to exploit human co-regulated modules and uncover their specific features in packaging different biological entities (genes, protein complexes or metabolic pathways). Finally, we identified 96 human co-regulated modules based on this method, and evaluate its effectiveness by comparing it with four other methods.

Conclusions: Dysfunctions in co-regulated interactions often occur in the development of cancer. Therefore, we focussed on an example co-regulated module and found that it could integrate a number of cancer-related genes. This was extended to causal dysfunctions of some complexes maintained by several physically interacting proteins, thus coordinating several metabolic pathways that directly underlie cancer.

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Functional consistency analysis of 96 co-regulated modules vs. random control modules (30% nodes replacement). Horizontal axis symbols represent three branches of GO (BP: biological process, MF: molecular function, CC: cellular component). Vertical axis symbols represent the corresponding percentages with different Hit-rates or Miss-rates for all modules.
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Figure 2: Functional consistency analysis of 96 co-regulated modules vs. random control modules (30% nodes replacement). Horizontal axis symbols represent three branches of GO (BP: biological process, MF: molecular function, CC: cellular component). Vertical axis symbols represent the corresponding percentages with different Hit-rates or Miss-rates for all modules.

Mentions: For a given module M, ( i = 1, 2,..., t, where t represents the number of GO terms for which the module M enriched) is the intersection gene set of module M and its enriched GO term i, and /M/ is the size of M. A higher Hit-rate indicated that more genes in module M convey a centralized biological function; a lower Miss-rate provided additional confirmation of our deduction. We binned the Hit-rates and Miss-rates in grades of 10%, and compared the Hit-rates and Miss-rates between our predicted modules and their controls (30% nodes replacement) (Figure 2). In the GO: biological process (BP) branch, 50 investigated modules in the real team had a Hit-rate above 90%, and 79 had a Miss-rate below 10%, while 17 modules in the control team had a Hit-rate above 90%, and 38 had a Miss-rate below 10%. The same observations for higher Hit-rate and lower Miss-rate were seen when analyzing the functional consistency of our investigated modules in the molecular function (MF) and cellular component (CC) categories. These results suggested that our method was capable of finding co-regulated modules with strong biological relevance. Similar results were found for the 10% and 20% node replacements (data not shown).


Uncovering packaging features of co-regulated modules based on human protein interaction and transcriptional regulatory networks.

Chen L, Wang H, Zhang L, Li W, Wang Q, Shang Y, He Y, He W, Li X, Tai J, Li X - BMC Bioinformatics (2010)

Functional consistency analysis of 96 co-regulated modules vs. random control modules (30% nodes replacement). Horizontal axis symbols represent three branches of GO (BP: biological process, MF: molecular function, CC: cellular component). Vertical axis symbols represent the corresponding percentages with different Hit-rates or Miss-rates for all modules.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2914056&req=5

Figure 2: Functional consistency analysis of 96 co-regulated modules vs. random control modules (30% nodes replacement). Horizontal axis symbols represent three branches of GO (BP: biological process, MF: molecular function, CC: cellular component). Vertical axis symbols represent the corresponding percentages with different Hit-rates or Miss-rates for all modules.
Mentions: For a given module M, ( i = 1, 2,..., t, where t represents the number of GO terms for which the module M enriched) is the intersection gene set of module M and its enriched GO term i, and /M/ is the size of M. A higher Hit-rate indicated that more genes in module M convey a centralized biological function; a lower Miss-rate provided additional confirmation of our deduction. We binned the Hit-rates and Miss-rates in grades of 10%, and compared the Hit-rates and Miss-rates between our predicted modules and their controls (30% nodes replacement) (Figure 2). In the GO: biological process (BP) branch, 50 investigated modules in the real team had a Hit-rate above 90%, and 79 had a Miss-rate below 10%, while 17 modules in the control team had a Hit-rate above 90%, and 38 had a Miss-rate below 10%. The same observations for higher Hit-rate and lower Miss-rate were seen when analyzing the functional consistency of our investigated modules in the molecular function (MF) and cellular component (CC) categories. These results suggested that our method was capable of finding co-regulated modules with strong biological relevance. Similar results were found for the 10% and 20% node replacements (data not shown).

Bottom Line: Here, we weighted edges of a human protein interaction network and a transcriptional regulatory network to construct an integrated network, and introduce a probabilistic model and a bipartite graph framework to exploit human co-regulated modules and uncover their specific features in packaging different biological entities (genes, protein complexes or metabolic pathways).Finally, we identified 96 human co-regulated modules based on this method, and evaluate its effectiveness by comparing it with four other methods.Therefore, we focussed on an example co-regulated module and found that it could integrate a number of cancer-related genes.

View Article: PubMed Central - HTML - PubMed

Affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, China. chenlina_2004@yahoo.com.cn

ABSTRACT

Background: Network co-regulated modules are believed to have the functionality of packaging multiple biological entities, and can thus be assumed to coordinate many biological functions in their network neighbouring regions.

Results: Here, we weighted edges of a human protein interaction network and a transcriptional regulatory network to construct an integrated network, and introduce a probabilistic model and a bipartite graph framework to exploit human co-regulated modules and uncover their specific features in packaging different biological entities (genes, protein complexes or metabolic pathways). Finally, we identified 96 human co-regulated modules based on this method, and evaluate its effectiveness by comparing it with four other methods.

Conclusions: Dysfunctions in co-regulated interactions often occur in the development of cancer. Therefore, we focussed on an example co-regulated module and found that it could integrate a number of cancer-related genes. This was extended to causal dysfunctions of some complexes maintained by several physically interacting proteins, thus coordinating several metabolic pathways that directly underlie cancer.

Show MeSH
Related in: MedlinePlus