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POINeT: protein interactome with sub-network analysis and hub prioritization.

Lee SA, Chan CH, Chen TC, Yang CY, Huang KC, Tsai CH, Lai JM, Wang FS, Kao CY, Huang CY - BMC Bioinformatics (2009)

Bottom Line: Expansion of all PPIs from a set of given queries often results in a complex PPI network lacking spatiotemporal consideration.Moreover, the reliability of available PPI resources, which consist of low- and high-throughput data, for network construction remains a significant challenge.The functionalities provided by POINeT are highly improved compared to previous version of POINT.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Clinical Medicine, National Yang-Ming University, Taipei, Taiwan, ROC. d93922005@ntu.edu.tw

ABSTRACT

Background: Protein-protein interactions (PPIs) are critical to every aspect of biological processes. Expansion of all PPIs from a set of given queries often results in a complex PPI network lacking spatiotemporal consideration. Moreover, the reliability of available PPI resources, which consist of low- and high-throughput data, for network construction remains a significant challenge. Even though a number of software tools are available to facilitate PPI network analysis, an integrated tool is crucial to alleviate the burden on querying across multiple web servers and software tools.

Results: We have constructed an integrated web service, POINeT, to simplify the process of PPI searching, analysis, and visualization. POINeT merges PPI and tissue-specific expression data from multiple resources. The tissue-specific PPIs and the numbers of research papers supporting the PPIs can be filtered with user-adjustable threshold values and are dynamically updated in the viewer. The network constructed in POINeT can be readily analyzed with, for example, the built-in centrality calculation module and an integrated network viewer. Nodes in global networks can also be ranked and filtered using various network analysis formulas, i.e., centralities. To prioritize the sub-network, we developed a ranking filtered method (S3) to uncover potential novel mediators in the midbody network. Several examples are provided to illustrate the functionality of POINeT. The network constructed from four schizophrenia risk markers suggests that EXOC4 might be a novel marker for this disease. Finally, a liver-specific PPI network has been filtered with adult and fetal liver expression profiles.

Conclusion: The functionalities provided by POINeT are highly improved compared to previous version of POINT. POINeT enables the identification and ranking of potential novel genes involved in a sub-network. Combining with tissue-specific gene expression profiles, PPIs specific to selected tissues can be revealed. The straightforward interface of POINeT makes PPI search and analysis just a few clicks away. The modular design permits further functional enhancement without hampering the simplicity. POINeT is available at (http://poinet.bioinformatics.tw/).

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Connections between the schizophrenia risk genes DTNBP1 and NRG1. (A) DLG4 and EXOC4 are positioned on the path between DTNBP1 and NRG1. Without DLG4 and EXOC4, the links between DTNBP1 and NRG1 would be broken. The gene expression patterns of the nodes in the temporal lobe are labeled with differential levels of grey, where darker shades denote higher expression levels. This figure is generated using CytoScape. The same network in two brain tissues, (B) Prefrontal Cortex and (C) Temporal Lobe, reveal the presences of interactions among DTNBP1, DLG4 and EXOC4. (D) Whereas in adipocyte (which is not related to brain and schizophrenia), most of the interactions are missing.
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Figure 3: Connections between the schizophrenia risk genes DTNBP1 and NRG1. (A) DLG4 and EXOC4 are positioned on the path between DTNBP1 and NRG1. Without DLG4 and EXOC4, the links between DTNBP1 and NRG1 would be broken. The gene expression patterns of the nodes in the temporal lobe are labeled with differential levels of grey, where darker shades denote higher expression levels. This figure is generated using CytoScape. The same network in two brain tissues, (B) Prefrontal Cortex and (C) Temporal Lobe, reveal the presences of interactions among DTNBP1, DLG4 and EXOC4. (D) Whereas in adipocyte (which is not related to brain and schizophrenia), most of the interactions are missing.

Mentions: Using these four genes as queries, there are interesting links between DTNBP1 and NRG1 (Figure 3A). DTNBP1 and NRG1 are both involved in fully connected cliques. Two nodes lie between DTNBP1 and NRG1; these are DLG4 and EXOC4. The interactions among DTNBP1, DLG4 and EXOC4 are present in various brain tissues, such as prefrontal cortex and temporal lobe, which are known to be related to schizophrenia etiology (Figure 3B and 3C). Most of the interactions in this sub-network are missing in other un-related tissues, such as adipocyte (Figure 3D). DLG4 is known to be involved in nicotine dependence [44]. There is no known association between DLG4 and schizophrenia in the literature; notwithstanding this, because there are constant controversial debates on the genetic factors contributing to schizophrenia, DLG4 is greatly deserving of further investigation. Similarly, EXOC4 is known to be involved in the exocyst complex, which is critical for the release of neurotransmitters [45]; at present its functional involvement in schizophrenia is unknown. The roles of DLG4 and EXOC4 in schizophrenia remain to be explored, and the two genes might serve as putative risk markers with potential for further studies.


POINeT: protein interactome with sub-network analysis and hub prioritization.

Lee SA, Chan CH, Chen TC, Yang CY, Huang KC, Tsai CH, Lai JM, Wang FS, Kao CY, Huang CY - BMC Bioinformatics (2009)

Connections between the schizophrenia risk genes DTNBP1 and NRG1. (A) DLG4 and EXOC4 are positioned on the path between DTNBP1 and NRG1. Without DLG4 and EXOC4, the links between DTNBP1 and NRG1 would be broken. The gene expression patterns of the nodes in the temporal lobe are labeled with differential levels of grey, where darker shades denote higher expression levels. This figure is generated using CytoScape. The same network in two brain tissues, (B) Prefrontal Cortex and (C) Temporal Lobe, reveal the presences of interactions among DTNBP1, DLG4 and EXOC4. (D) Whereas in adipocyte (which is not related to brain and schizophrenia), most of the interactions are missing.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2683814&req=5

Figure 3: Connections between the schizophrenia risk genes DTNBP1 and NRG1. (A) DLG4 and EXOC4 are positioned on the path between DTNBP1 and NRG1. Without DLG4 and EXOC4, the links between DTNBP1 and NRG1 would be broken. The gene expression patterns of the nodes in the temporal lobe are labeled with differential levels of grey, where darker shades denote higher expression levels. This figure is generated using CytoScape. The same network in two brain tissues, (B) Prefrontal Cortex and (C) Temporal Lobe, reveal the presences of interactions among DTNBP1, DLG4 and EXOC4. (D) Whereas in adipocyte (which is not related to brain and schizophrenia), most of the interactions are missing.
Mentions: Using these four genes as queries, there are interesting links between DTNBP1 and NRG1 (Figure 3A). DTNBP1 and NRG1 are both involved in fully connected cliques. Two nodes lie between DTNBP1 and NRG1; these are DLG4 and EXOC4. The interactions among DTNBP1, DLG4 and EXOC4 are present in various brain tissues, such as prefrontal cortex and temporal lobe, which are known to be related to schizophrenia etiology (Figure 3B and 3C). Most of the interactions in this sub-network are missing in other un-related tissues, such as adipocyte (Figure 3D). DLG4 is known to be involved in nicotine dependence [44]. There is no known association between DLG4 and schizophrenia in the literature; notwithstanding this, because there are constant controversial debates on the genetic factors contributing to schizophrenia, DLG4 is greatly deserving of further investigation. Similarly, EXOC4 is known to be involved in the exocyst complex, which is critical for the release of neurotransmitters [45]; at present its functional involvement in schizophrenia is unknown. The roles of DLG4 and EXOC4 in schizophrenia remain to be explored, and the two genes might serve as putative risk markers with potential for further studies.

Bottom Line: Expansion of all PPIs from a set of given queries often results in a complex PPI network lacking spatiotemporal consideration.Moreover, the reliability of available PPI resources, which consist of low- and high-throughput data, for network construction remains a significant challenge.The functionalities provided by POINeT are highly improved compared to previous version of POINT.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Clinical Medicine, National Yang-Ming University, Taipei, Taiwan, ROC. d93922005@ntu.edu.tw

ABSTRACT

Background: Protein-protein interactions (PPIs) are critical to every aspect of biological processes. Expansion of all PPIs from a set of given queries often results in a complex PPI network lacking spatiotemporal consideration. Moreover, the reliability of available PPI resources, which consist of low- and high-throughput data, for network construction remains a significant challenge. Even though a number of software tools are available to facilitate PPI network analysis, an integrated tool is crucial to alleviate the burden on querying across multiple web servers and software tools.

Results: We have constructed an integrated web service, POINeT, to simplify the process of PPI searching, analysis, and visualization. POINeT merges PPI and tissue-specific expression data from multiple resources. The tissue-specific PPIs and the numbers of research papers supporting the PPIs can be filtered with user-adjustable threshold values and are dynamically updated in the viewer. The network constructed in POINeT can be readily analyzed with, for example, the built-in centrality calculation module and an integrated network viewer. Nodes in global networks can also be ranked and filtered using various network analysis formulas, i.e., centralities. To prioritize the sub-network, we developed a ranking filtered method (S3) to uncover potential novel mediators in the midbody network. Several examples are provided to illustrate the functionality of POINeT. The network constructed from four schizophrenia risk markers suggests that EXOC4 might be a novel marker for this disease. Finally, a liver-specific PPI network has been filtered with adult and fetal liver expression profiles.

Conclusion: The functionalities provided by POINeT are highly improved compared to previous version of POINT. POINeT enables the identification and ranking of potential novel genes involved in a sub-network. Combining with tissue-specific gene expression profiles, PPIs specific to selected tissues can be revealed. The straightforward interface of POINeT makes PPI search and analysis just a few clicks away. The modular design permits further functional enhancement without hampering the simplicity. POINeT is available at (http://poinet.bioinformatics.tw/).

Show MeSH
Related in: MedlinePlus