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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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Results from best hits analysis.(A) Number of subgraphs from one network (named outside of the intersection) that are a best hit to a subgraph from another network (named within the intersection). (B) Number of best reciprocal hits between subgraphs from two networks.
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pone-0062670-g001: Results from best hits analysis.(A) Number of subgraphs from one network (named outside of the intersection) that are a best hit to a subgraph from another network (named within the intersection). (B) Number of best reciprocal hits between subgraphs from two networks.

Mentions: In order to ascertain whether novel functional modules can be identified by the integration of data, we determined the extent to which subgraphs from the different data types are congruent. To do this we investigated whether the partitioning of different networks had resulted in the production of pairs of subgraphs from different networks that have significantly intersecting gene sets. We term such pairs “congruent subgraphs”. By comparing subgraphs from the PPI, genetic and co-regulation networks, we identified statistically significant gene intersections and subsequent “best hits” and “best reciprocal hits” between the subgraphs of two networks (see Methods for more details). A best hit represents a significant gene intersection between two subgraphs where one subgraph best matches the other, where best match is determined using the maximal Matthews correlation coefficient (MCC). A best reciprocal hit, again, represents a significant gene intersection, where both subgraphs are the best match to one another. Thus, best reciprocal hits indicate the strongest congruence between subgraphs from different networks. A summary of best hits and best reciprocal hits are given in Figure 1A and B, respectively.


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)

Results from best hits analysis.(A) Number of subgraphs from one network (named outside of the intersection) that are a best hit to a subgraph from another network (named within the intersection). (B) Number of best reciprocal hits between subgraphs from two networks.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0062670-g001: Results from best hits analysis.(A) Number of subgraphs from one network (named outside of the intersection) that are a best hit to a subgraph from another network (named within the intersection). (B) Number of best reciprocal hits between subgraphs from two networks.
Mentions: In order to ascertain whether novel functional modules can be identified by the integration of data, we determined the extent to which subgraphs from the different data types are congruent. To do this we investigated whether the partitioning of different networks had resulted in the production of pairs of subgraphs from different networks that have significantly intersecting gene sets. We term such pairs “congruent subgraphs”. By comparing subgraphs from the PPI, genetic and co-regulation networks, we identified statistically significant gene intersections and subsequent “best hits” and “best reciprocal hits” between the subgraphs of two networks (see Methods for more details). A best hit represents a significant gene intersection between two subgraphs where one subgraph best matches the other, where best match is determined using the maximal Matthews correlation coefficient (MCC). A best reciprocal hit, again, represents a significant gene intersection, where both subgraphs are the best match to one another. Thus, best reciprocal hits indicate the strongest congruence between subgraphs from different networks. A summary of best hits and best reciprocal hits are given in Figure 1A and B, respectively.

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