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Modular biological function is most effectively captured by combining molecular interaction data types.

Ames RM, Macpherson JI, Pinney JW, Lovell SC, Robertson DL - PLoS ONE (2013)

Bottom Line: Furthermore, the different annotation types of GO are not predominantly associated with one of the interaction data types.Collectively our results demonstrate that successful capture of functional relationships by network data depends on both the specific biological function being characterised and the type of network data being used.Combining interaction subnetworks across data types is therefore essential for fully understanding the complex and emergent nature of biological function.

View Article: PubMed Central - PubMed

Affiliation: Computational and Evolutionary Biology, Faculty of Life Sciences, The University of Manchester, Manchester, United Kingdom. ryan.ames@manchester.ac.uk

ABSTRACT
Large-scale molecular interaction data sets have the potential to provide a comprehensive, system-wide understanding of biological function. Although individual molecules can be promiscuous in terms of their contribution to function, molecular functions emerge from the specific interactions of molecules giving rise to modular organisation. As functions often derive from a range of mechanisms, we demonstrate that they are best studied using networks derived from different sources. Implementing a graph partitioning algorithm we identify subnetworks in yeast protein-protein interaction (PPI), genetic interaction and gene co-regulation networks. Among these subnetworks we identify cohesive subgraphs that we expect to represent functional modules in the different data types. We demonstrate significant overlap between the subgraphs generated from the different data types and show these overlaps can represent related functions as represented by the Gene Ontology (GO). Next, we investigate the correspondence between our subgraphs and the Gene Ontology. This revealed varying degrees of coverage of the biological process, molecular function and cellular component ontologies, dependent on the data type. For example, subgraphs from the PPI show enrichment for 84%, 58% and 93% of annotated GO terms, respectively. Integrating the interaction data into a combined network increases the coverage of GO. Furthermore, the different annotation types of GO are not predominantly associated with one of the interaction data types. Collectively our results demonstrate that successful capture of functional relationships by network data depends on both the specific biological function being characterised and the type of network data being used. We identify functions that require integrated information to be accurately represented, demonstrating the limitations of individual data types. Combining interaction subnetworks across data types is therefore essential for fully understanding the complex and emergent nature of biological function.

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GLASS visualisation of enriched GO terms.Each cell represents a GO term and is coloured blue, red, yellow or green if one or more subgraphs are enriched for that GO term in the PPI, genetic, co-regulation or combined networks, respectively. The intensity of each coloured cell shows the best MCC of the subgraphs with enrichment for that term. Grey coloured cells are those GO terms which have only one or no associated genes in that network.
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pone-0062670-g004: GLASS visualisation of enriched GO terms.Each cell represents a GO term and is coloured blue, red, yellow or green if one or more subgraphs are enriched for that GO term in the PPI, genetic, co-regulation or combined networks, respectively. The intensity of each coloured cell shows the best MCC of the subgraphs with enrichment for that term. Grey coloured cells are those GO terms which have only one or no associated genes in that network.

Mentions: To further investigate the capture of distinct functional categories by network subgraphs, we visualised gene ontology terms using a Voronoi tree-mapping approach (Figure 4). In the tree maps, each cell represents a GO term, where terms of similar functions are grouped together. The maps from different networks are directly comparable with the equivalently positioned cells in each tile representing the same GO terms. The intensity of cell shading indicates the accuracy with which the GO term is captured by a network, using MCC score as the accuracy measure. Importantly, the Voronoi maps (Figure 4) highlight the disparity between the ability of network subgraphs to capture certain types of functional data. Cellular component annotation appears to be the easiest type of biological function to capture, using any type of network data. Conversely molecular function is more difficult to capture. This tree-mapping approach also highlights that certain functional areas within each ontology can be either successfully captured, or are difficult to capture, using the network data. In contrast, some areas are clearly shaded in all maps from the same ontology. The ability therefore of different networks to capture functional relationships, is related both to the type of data used to create the network, and also the specific function in question.


Modular biological function is most effectively captured by combining molecular interaction data types.

Ames RM, Macpherson JI, Pinney JW, Lovell SC, Robertson DL - PLoS ONE (2013)

GLASS visualisation of enriched GO terms.Each cell represents a GO term and is coloured blue, red, yellow or green if one or more subgraphs are enriched for that GO term in the PPI, genetic, co-regulation or combined networks, respectively. The intensity of each coloured cell shows the best MCC of the subgraphs with enrichment for that term. Grey coloured cells are those GO terms which have only one or no associated genes in that network.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3643936&req=5

pone-0062670-g004: GLASS visualisation of enriched GO terms.Each cell represents a GO term and is coloured blue, red, yellow or green if one or more subgraphs are enriched for that GO term in the PPI, genetic, co-regulation or combined networks, respectively. The intensity of each coloured cell shows the best MCC of the subgraphs with enrichment for that term. Grey coloured cells are those GO terms which have only one or no associated genes in that network.
Mentions: To further investigate the capture of distinct functional categories by network subgraphs, we visualised gene ontology terms using a Voronoi tree-mapping approach (Figure 4). In the tree maps, each cell represents a GO term, where terms of similar functions are grouped together. The maps from different networks are directly comparable with the equivalently positioned cells in each tile representing the same GO terms. The intensity of cell shading indicates the accuracy with which the GO term is captured by a network, using MCC score as the accuracy measure. Importantly, the Voronoi maps (Figure 4) highlight the disparity between the ability of network subgraphs to capture certain types of functional data. Cellular component annotation appears to be the easiest type of biological function to capture, using any type of network data. Conversely molecular function is more difficult to capture. This tree-mapping approach also highlights that certain functional areas within each ontology can be either successfully captured, or are difficult to capture, using the network data. In contrast, some areas are clearly shaded in all maps from the same ontology. The ability therefore of different networks to capture functional relationships, is related both to the type of data used to create the network, and also the specific function in question.

Bottom Line: Furthermore, the different annotation types of GO are not predominantly associated with one of the interaction data types.Collectively our results demonstrate that successful capture of functional relationships by network data depends on both the specific biological function being characterised and the type of network data being used.Combining interaction subnetworks across data types is therefore essential for fully understanding the complex and emergent nature of biological function.

View Article: PubMed Central - PubMed

Affiliation: Computational and Evolutionary Biology, Faculty of Life Sciences, The University of Manchester, Manchester, United Kingdom. ryan.ames@manchester.ac.uk

ABSTRACT
Large-scale molecular interaction data sets have the potential to provide a comprehensive, system-wide understanding of biological function. Although individual molecules can be promiscuous in terms of their contribution to function, molecular functions emerge from the specific interactions of molecules giving rise to modular organisation. As functions often derive from a range of mechanisms, we demonstrate that they are best studied using networks derived from different sources. Implementing a graph partitioning algorithm we identify subnetworks in yeast protein-protein interaction (PPI), genetic interaction and gene co-regulation networks. Among these subnetworks we identify cohesive subgraphs that we expect to represent functional modules in the different data types. We demonstrate significant overlap between the subgraphs generated from the different data types and show these overlaps can represent related functions as represented by the Gene Ontology (GO). Next, we investigate the correspondence between our subgraphs and the Gene Ontology. This revealed varying degrees of coverage of the biological process, molecular function and cellular component ontologies, dependent on the data type. For example, subgraphs from the PPI show enrichment for 84%, 58% and 93% of annotated GO terms, respectively. Integrating the interaction data into a combined network increases the coverage of GO. Furthermore, the different annotation types of GO are not predominantly associated with one of the interaction data types. Collectively our results demonstrate that successful capture of functional relationships by network data depends on both the specific biological function being characterised and the type of network data being used. We identify functions that require integrated information to be accurately represented, demonstrating the limitations of individual data types. Combining interaction subnetworks across data types is therefore essential for fully understanding the complex and emergent nature of biological function.

Show MeSH
Related in: MedlinePlus