FACETS: multi-faceted functional decomposition of protein interaction networks
Motivation: The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein–protein interaction (PPI) network using graph theoretic analysis. Despite the recent progress, systems level a...
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Oxford University Press
2012
|
| Online Access: | http://hdl.handle.net/1721.1/75409 https://orcid.org/0000-0001-7387-3572 |
| _version_ | 1826194268689006592 |
|---|---|
| author | Seah, Boon-Siew Bhowmick, Sourav S. Dewey, C. Forbes |
| author2 | Massachusetts Institute of Technology. Department of Biological Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Biological Engineering Seah, Boon-Siew Bhowmick, Sourav S. Dewey, C. Forbes |
| author_sort | Seah, Boon-Siew |
| collection | MIT |
| description | Motivation: The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein–protein interaction (PPI) network using graph theoretic analysis. Despite the recent progress, systems level analysis of high-throughput PPIs remains a daunting task because of the amount of data they present. In this article, we propose a novel PPI network decomposition algorithm called FACETS in order to make sense of the deluge of interaction data using Gene Ontology (GO) annotations. FACETS finds not just a single functional decomposition of the PPI network, but a multi-faceted atlas of functional decompositions that portray alternative perspectives of the functional landscape of the underlying PPI network. Each facet in the atlas represents a distinct interpretation of how the network can be functionally decomposed and organized. Our algorithm maximizes interpretative value of the atlas by optimizing inter-facet orthogonality and intra-facet cluster modularity.
Results: We tested our algorithm on the global networks from IntAct, and compared it with gold standard datasets from MIPS and KEGG. We demonstrated the performance of FACETS. We also performed a case study that illustrates the utility of our approach. |
| first_indexed | 2024-09-23T09:53:27Z |
| format | Article |
| id | mit-1721.1/75409 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T09:53:27Z |
| publishDate | 2012 |
| publisher | Oxford University Press |
| record_format | dspace |
| spelling | mit-1721.1/754092021-09-09T17:02:26Z FACETS: multi-faceted functional decomposition of protein interaction networks Seah, Boon-Siew Bhowmick, Sourav S. Dewey, C. Forbes Massachusetts Institute of Technology. Department of Biological Engineering Dewey, C. Forbes Motivation: The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein–protein interaction (PPI) network using graph theoretic analysis. Despite the recent progress, systems level analysis of high-throughput PPIs remains a daunting task because of the amount of data they present. In this article, we propose a novel PPI network decomposition algorithm called FACETS in order to make sense of the deluge of interaction data using Gene Ontology (GO) annotations. FACETS finds not just a single functional decomposition of the PPI network, but a multi-faceted atlas of functional decompositions that portray alternative perspectives of the functional landscape of the underlying PPI network. Each facet in the atlas represents a distinct interpretation of how the network can be functionally decomposed and organized. Our algorithm maximizes interpretative value of the atlas by optimizing inter-facet orthogonality and intra-facet cluster modularity. Results: We tested our algorithm on the global networks from IntAct, and compared it with gold standard datasets from MIPS and KEGG. We demonstrated the performance of FACETS. We also performed a case study that illustrates the utility of our approach. 2012-12-12T16:03:06Z 2012-12-12T16:03:06Z 2012-08 2012-06 Article http://purl.org/eprint/type/JournalArticle 1367-4803 1460-2059 http://hdl.handle.net/1721.1/75409 Seah, B.-S., S. S. Bhowmick, and C. Forbes Dewey. “FACETS: Multi-faceted Functional Decomposition of Protein Interaction Networks.” Bioinformatics 28.20 (2012): 2624–2631. https://orcid.org/0000-0001-7387-3572 en_US http://dx.doi.org/10.1093/bioinformatics/bts469 Bioinformatics Creative Commons Attribution Non-Commercial http://creativecommons.org/licenses/by-nc/2.5 application/pdf Oxford University Press Oxford |
| spellingShingle | Seah, Boon-Siew Bhowmick, Sourav S. Dewey, C. Forbes FACETS: multi-faceted functional decomposition of protein interaction networks |
| title | FACETS: multi-faceted functional decomposition of protein interaction networks |
| title_full | FACETS: multi-faceted functional decomposition of protein interaction networks |
| title_fullStr | FACETS: multi-faceted functional decomposition of protein interaction networks |
| title_full_unstemmed | FACETS: multi-faceted functional decomposition of protein interaction networks |
| title_short | FACETS: multi-faceted functional decomposition of protein interaction networks |
| title_sort | facets multi faceted functional decomposition of protein interaction networks |
| url | http://hdl.handle.net/1721.1/75409 https://orcid.org/0000-0001-7387-3572 |
| work_keys_str_mv | AT seahboonsiew facetsmultifacetedfunctionaldecompositionofproteininteractionnetworks AT bhowmicksouravs facetsmultifacetedfunctionaldecompositionofproteininteractionnetworks AT deweycforbes facetsmultifacetedfunctionaldecompositionofproteininteractionnetworks |