ArticleBioinformatics (Oxford, England)2009
How and when should interactome-derived clusters be used to predict functional modules and protein function?
Article in Bioinformatics (Oxford, England), 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers.
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66 citing papers in PubMed.
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- Next-generation Interactomics: Considerations for the Use of Co-elution to Measure Protein Interaction Networks.Molecular & cellular proteomics : MCP · 2020Review
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- Network propagation: a universal amplifier of genetic associations.Nature reviews. Genetics · 2017Review
- Integration of over 9,000 mass spectrometry experiments builds a global map of human protein complexes.Molecular systems biology · 2017Article
- Bacterial protein meta-interactomes predict cross-species interactions and protein function.BMC bioinformatics · 2017Article
- RECoN: Rice Environment Coexpression Network for Systems Level Analysis of Abiotic-Stress Response.Frontiers in plant science · 2017Article
- Protein complex prediction for large protein protein interaction networks with the Core&Peel method.BMC bioinformatics · 2016Article
- A two-layer integration framework for protein complex detection.BMC bioinformatics · 2016Article
- Identification of potential biomarkers to differentially diagnose solid pseudopapillary tumors and pancreatic malignancies via a gene regulatory network.Journal of translational medicine · 2015Article
- Genome-Wide Detection and Analysis of Multifunctional Genes.PLoS computational biology · 2015Article
- A comprehensive analysis of candidate genes and pathways in pancreatic cancer.Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine · 2015Article
6 more citing papers are in PubMed but not listed here.
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Abstract
motivationClustering of protein-protein interaction networks is one of the most common approaches for predicting functional modules, protein complexes and protein functions. But, how well does clustering perform at these tasks?
resultsWe develop a general framework to assess how well computationally derived clusters in physical interactomes overlap functional modules derived via the Gene Ontology (GO). Using this framework, we evaluate six diverse network clustering algorithms using Saccharomyces cerevisiae and show that (i) the performances of these algorithms can differ substantially when run on the same network and (ii) their relative performances change depending upon the topological characteristics of the network under consideration. For the specific task of function prediction in S.cerevisiae, we demonstrate that, surprisingly, a simple non-clustering guilt-by-association approach outperforms widely used clustering-based approaches that annotate a protein with the overrepresented biological process and cellular component terms in its cluster; this is true over the range of clustering algorithms considered. Further analysis parameterizes performance based on the number of annotated proteins, and suggests when clustering approaches should be used for interactome functional analyses. Overall our results suggest a re-examination of when and how clustering approaches should be applied to physical interactomes, and establishes guidelines by which novel clustering approaches for biological networks should be justified and evaluated with respect to functional analysis. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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