Detecting functional modules in the yeast protein–protein interaction network

Chen, Jingchun; Yuan, Bo

doi:10.1093/bioinformatics/btl370

Cited by 384 publications

(255 citation statements)

References 33 publications

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“…The data integration approach was used in some early works on predicting protein-protein interactions (10,11) and more recently by Qiu and Noble (12), but these studies focus only on predicting pairs of proteins in the same complex and not on reconstructing entire complexes. Many recent studies (13)(14)(15)(16)(17)(18)(19)(20)(21) have successfully integrated multiple types of data to predict functional linkage between proteins, constructing a graph whose pairwise affinity score summarizes the information from different sources of data. However, because the data integration is not trained toward predicting complexes, the high affinity pairs contain transient binding partners and even protein pairs that never interact directly but merely function in the same pathways.…”

mentioning

confidence: 99%

A Complex-based Reconstruction of the Saccharomyces cerevisiae Interactome

Wang

Kakaradov

Collins

et al. 2009

Molecular & Cellular Proteomics

100

117

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Most cellular processes are performed by proteomic units that interact with each other. These units are often stoichiometrically stable complexes comprised of several proteins. To obtain a faithful view of the protein interactome we must view it in terms of these basic units (complexes and proteins) and the interactions between them. This study makes two contributions toward this goal. First, it provides a new algorithm for reconstruction of stable complexes from a variety of heterogeneous biological assays; our approach combines state-of-the-art machine learning methods with a novel hierarchical clustering algorithm that allows clusters to overlap. We demonstrate that our approach constructs over 40% more known complexes than other recent methods and that the complexes it produces are more biologically coherent even compared with the reference set. We provide experimental support for some of our novel predictions, identifying both a new complex involved in nutrient starvation and a new component of the eisosome complex. Second, we provide a high accuracy algorithm for the novel problem of predicting transient interactions involving complexes. We show that our complex level network, which we call ComplexNet, provides novel insights regarding the protein-protein interaction network. In particular, we reinterpret the finding that "hubs" in the network are enriched for being essential, showing instead that essential proteins tend to be clustered together in essential complexes and that these essential complexes tend to be large. Biological processes exhibit a hierarchical structure in which the basic working units, proteins, physically associate to form stoichiometrically stable complexes. Complexes interact with individual proteins or other complexes to form functional modules and pathways that carry out most cellular processes. Such higher level interactions are more transient than those within complexes and are highly dependent on temporal and spatial context. The function of each protein or complex depends on its interaction partners. Therefore, a faithful reconstruction of the entire set of complexes in the cell is essential to identifying the function of individual proteins and complexes as well as serving as a building block for understanding the higher level organization of the cell, such as the interactions of complexes and proteins within cellular pathways. Here we describe a novel method for reconstruction of complexes from a variety of biological assays and a method for predicting the network of interactions relating these core cellular units (complexes and proteins).Our reconstruction effort focuses on the yeast Saccharomyces cerevisiae. Yeast serves as the prototypical case study for the reconstruction of protein-protein interaction networks. Moreover the yeast complexes often have conserved orthologs in other organisms, including human, and are of interest in their own right. Several studies (1-4) using a variety of assays have generated high throughput data that directly measure protein-protein inte...

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mentioning

confidence: 99%

A Complex-based Reconstruction of the Saccharomyces cerevisiae Interactome

Wang

Kakaradov

Collins

et al. 2009

Molecular & Cellular Proteomics

100

117

View full text Add to dashboard Cite

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“…The Girvan-Newman algorithm has also been modified so that shortest paths are computed on weighted networks. In one approach, instead of counting the total number of shortest paths through an edge, the total number of "non-redundant" shortest paths through an edge are counted by considering paths that do not share an endpoint [16]. Edge weights are also considered by this method; in this case, weights correspond to dissimilarities between endpoints, rather than similarities or edge reliabilities.…”

Section: |mentioning

confidence: 99%

“…Network-based hierarchical clustering Apply Girvan-Newman (GN) algorithm, building a hierarchical clustering by removing edges with highest edge-betweenness [27]; extend the GN algorithm to weighted graphs and modify to consider non-redundant paths [16]; extend the GN algorithm to additionally consider local measure (edge commonality) [75]; perform agglomerative clustering in the reverse order of the GN edge removal [50].…”

Section: Local Graph Clusteringmentioning

confidence: 99%

Protein Function Prediction via Analysis of Interactomes

Nabieva

Singh

2008

Prediction of Protein Structures, Functions, and Interactions

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“…Expression data have been clustered (Prelić et al 2006;Segal et al, 2004), or transformed to a graph and analyzed with topological methods (Chen and Yuan, 2006;Voy et al, 2006). Similarly, protein protein interaction (PPI) data have been transformed into an association matrix and clustered (Rives and Galitski, 2003), or analyzed with topological meth ods (Pereira Leal et al, 2004;Spirin and Mirny, 2003).…”

Section: Introductionmentioning

confidence: 99%

Extracting Functional Modules from Biological Pathways

Zhao

2007

Nature Precedings

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It has been proposed that functional modules are the fundamental units of cellular function (Hartwell et al., 1999). Methods to identify these modules have thus far relied on gene expression data or protein protein interaction (PPI) data, but have a few limitations. We propose a new method, using biological pathway data to identify functional modules, that can potentially overcome these limitations. We also construct a network of these modules using functionally relevant PPI data. This network displays the flow and integration of information between modules and can be used to map cellular function.

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Detecting functional modules in the yeast protein–protein interaction network

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 384 publications

References 33 publications

A Complex-based Reconstruction of the Saccharomyces cerevisiae Interactome

A Complex-based Reconstruction of the Saccharomyces cerevisiae Interactome

Protein Function Prediction via Analysis of Interactomes

Extracting Functional Modules from Biological Pathways

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