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

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 molec...

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Autores principales:	Ryan M Ames, Jamie I Macpherson, John W Pinney, Simon C Lovell, David L Robertson
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7720d5fa451f47b68fd9e7e753877bf3
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spelling	oai:doaj.org-article:7720d5fa451f47b68fd9e7e753877bf32021-11-18T07:46:56ZModular biological function is most effectively captured by combining molecular interaction data types.1932-620310.1371/journal.pone.0062670https://doaj.org/article/7720d5fa451f47b68fd9e7e753877bf32013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23658761/?tool=EBIhttps://doaj.org/toc/1932-6203Large-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.Ryan M AmesJamie I MacphersonJohn W PinneySimon C LovellDavid L RobertsonPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 5, p e62670 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ryan M Ames Jamie I Macpherson John W Pinney Simon C Lovell David L Robertson Modular biological function is most effectively captured by combining molecular interaction data types.
description	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.
format	article
author	Ryan M Ames Jamie I Macpherson John W Pinney Simon C Lovell David L Robertson
author_facet	Ryan M Ames Jamie I Macpherson John W Pinney Simon C Lovell David L Robertson
author_sort	Ryan M Ames
title	Modular biological function is most effectively captured by combining molecular interaction data types.
title_short	Modular biological function is most effectively captured by combining molecular interaction data types.
title_full	Modular biological function is most effectively captured by combining molecular interaction data types.
title_fullStr	Modular biological function is most effectively captured by combining molecular interaction data types.
title_full_unstemmed	Modular biological function is most effectively captured by combining molecular interaction data types.
title_sort	modular biological function is most effectively captured by combining molecular interaction data types.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/7720d5fa451f47b68fd9e7e753877bf3
work_keys_str_mv	AT ryanmames modularbiologicalfunctionismosteffectivelycapturedbycombiningmolecularinteractiondatatypes AT jamieimacpherson modularbiologicalfunctionismosteffectivelycapturedbycombiningmolecularinteractiondatatypes AT johnwpinney modularbiologicalfunctionismosteffectivelycapturedbycombiningmolecularinteractiondatatypes AT simonclovell modularbiologicalfunctionismosteffectivelycapturedbycombiningmolecularinteractiondatatypes AT davidlrobertson modularbiologicalfunctionismosteffectivelycapturedbycombiningmolecularinteractiondatatypes
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Modular biological function is most effectively captured by combining molecular interaction data types.

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