Evaluation of clustering algorithms for protein-protein interaction networks View Full Text


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Article Info

DATE

2006-11-06

AUTHORS

Sylvain Brohée, Jacques van Helden

ABSTRACT

BACKGROUND: Protein interactions are crucial components of all cellular processes. Recently, high-throughput methods have been developed to obtain a global description of the interactome (the whole network of protein interactions for a given organism). In 2002, the yeast interactome was estimated to contain up to 80,000 potential interactions. This estimate is based on the integration of data sets obtained by various methods (mass spectrometry, two-hybrid methods, genetic studies). High-throughput methods are known, however, to yield a non-negligible rate of false positives, and to miss a fraction of existing interactions. The interactome can be represented as a graph where nodes correspond with proteins and edges with pairwise interactions. In recent years clustering methods have been developed and applied in order to extract relevant modules from such graphs. These algorithms require the specification of parameters that may drastically affect the results. In this paper we present a comparative assessment of four algorithms: Markov Clustering (MCL), Restricted Neighborhood Search Clustering (RNSC), Super Paramagnetic Clustering (SPC), and Molecular Complex Detection (MCODE). RESULTS: A test graph was built on the basis of 220 complexes annotated in the MIPS database. To evaluate the robustness to false positives and false negatives, we derived 41 altered graphs by randomly removing edges from or adding edges to the test graph in various proportions. Each clustering algorithm was applied to these graphs with various parameter settings, and the clusters were compared with the annotated complexes. We analyzed the sensitivity of the algorithms to the parameters and determined their optimal parameter values. We also evaluated their robustness to alterations of the test graph. We then applied the four algorithms to six graphs obtained from high-throughput experiments and compared the resulting clusters with the annotated complexes. CONCLUSION: This analysis shows that MCL is remarkably robust to graph alterations. In the tests of robustness, RNSC is more sensitive to edge deletion but less sensitive to the use of suboptimal parameter values. The other two algorithms are clearly weaker under most conditions. The analysis of high-throughput data supports the superiority of MCL for the extraction of complexes from interaction networks. More... »

PAGES

488-488

References to SciGraph publications

  • 2004-07-21. Modular decomposition of protein-protein interaction networks in GENOME BIOLOGY
  • 2006-03-22. Global landscape of protein complexes in the yeast Saccharomyces cerevisiae in NATURE
  • 2002-01. Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry in NATURE
  • 2002-01. Functional organization of the yeast proteome by systematic analysis of protein complexes in NATURE
  • 2001-05. Lethality and centrality in protein networks in NATURE
  • 2003-12-15. Functional classification of proteins for the prediction of cellular function from a protein-protein interaction network in GENOME BIOLOGY
  • 2004-07-13. Clustering proteins from interaction networks for the prediction of cellular functions in BMC BIOINFORMATICS
  • 2003-05-12. Global protein function prediction from protein-protein interaction networks in NATURE BIOTECHNOLOGY
  • 2005-07-01. Effect of sampling on topology predictions of protein-protein interaction networks in NATURE BIOTECHNOLOGY
  • 2003-01-13. An automated method for finding molecular complexes in large protein interaction networks in BMC BIOINFORMATICS
  • 2002-05-08. Comparative assessment of large-scale data sets of protein–protein interactions in NATURE
  • 2004-11-01. How biologically relevant are interaction-based modules in protein networks? in GENOME BIOLOGY
  • 2005-03-01. The Use of Edge-Betweenness Clustering to Investigate Biological Function in Protein Interaction Networks in BMC BIOINFORMATICS
  • 2000-02. A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae in NATURE
  • 2004-04-30. Transcriptional regulation of protein complexes in yeast in GENOME BIOLOGY
  • 2006-04-14. Development and implementation of an algorithm for detection of protein complexes in large interaction networks in BMC BIOINFORMATICS
  • 2006-01-22. Proteome survey reveals modularity of the yeast cell machinery in NATURE
  • 2003-02-27. The GRID: The General Repository for Interaction Datasets in GENOME BIOLOGY
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/1471-2105-7-488

    DOI

    http://dx.doi.org/10.1186/1471-2105-7-488

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1007199178

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/17087821


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    88 interaction networks
    89 interactome
    90 method
    91 module
    92 most conditions
    93 negatives
    94 network
    95 nodes
    96 non-negligible rate
    97 optimal parameter values
    98 order
    99 pairwise interactions
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    102 parameter values
    103 parameters
    104 positives
    105 potential interactions
    106 process
    107 proportion
    108 protein
    109 protein interactions
    110 protein-protein interaction network
    111 rate
    112 recent years
    113 relevant modules
    114 results
    115 robustness
    116 sensitivity
    117 set
    118 setting
    119 specification
    120 specification of parameters
    121 suboptimal parameter values
    122 such graphs
    123 superiority
    124 superiority of MCL
    125 test
    126 test graphs
    127 tests of robustness
    128 use
    129 values
    130 years
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