Discovery of uncharacterized cellular systems by genome-wide analysis of functional linkages

Date, Shailesh V.; Marcotte, Edward M.

doi:10.1038/nbt861

Cited by 196 publications

(165 citation statements)

References 44 publications

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“…We used the same transcription regulation data as described above under "Biological Coherence of Predicted Complexes." For any pair of proteins, we compute the mutual information between the profile vectors of the two proteins using the method described Date and Marcotte (48).…”

Section: Constructing a Reference List Of Positive And Negative Complmentioning

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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Section: Constructing a Reference List Of Positive And Negative Complmentioning

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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“…For both methods, we analyzed 424 bacterial genome sequences (31 archeaebacteria and 393 eubacteria, downloaded from NCBI in December 2006). C. elegans protein sequences were aligned to protein sequences encoded by the 424 bacterial genomes, using the program BLASTP with default settings (Altschul et al 1997) and alignment scores analyzed as in Date and Marcotte (2003) with the modification of using discretized BLASTP E-values when calculating mutual information between phylogenetic profiles. We used bins of equal numbers of E-values rather than equal intervals of E-values, accounting for the nonuniform E-value distribution.…”

Section: Inferring Functional Associations From Phylogenetic Profilesmentioning

confidence: 99%

Predicting genetic modifier loci using functional gene networks

et al. 2010

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Most phenotypes are genetically complex, with contributions from mutations in many different genes. Mutations in more than one gene can combine synergistically to cause phenotypic change, and systematic studies in model organisms show that these genetic interactions are pervasive. However, in human association studies such nonadditive genetic interactions are very difficult to identify because of a lack of statistical power-simply put, the number of potential interactions is too vast. One approach to resolve this is to predict candidate modifier interactions between loci, and then to specifically test these for associations with the phenotype. Here, we describe a general method for predicting genetic interactions based on the use of integrated functional gene networks. We show that in both Saccharomyces cerevisiae and Caenorhabditis elegans a single high-coverage, high-quality functional network can successfully predict genetic modifiers for the majority of genes. For C. elegans we also describe the construction of a new, improved, and expanded functional network, WormNet 2.Using this network we demonstrate how it is possible to rapidly expand the number of modifier loci known for a gene, predicting and validating new genetic interactions for each of three signal transduction genes. We propose that this approach, termed network-guided modifier screening, provides a general strategy for predicting genetic interactions. This work thus suggests that a high-quality integrated human gene network will provide a powerful resource for modifier locus discovery in many different diseases.

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“…Computational clustering of genes according to their connectivities [39][40][41][42][43] shows that these features may correspond to known cellular machines (e.g., the ribosome, the proteasome, etc.) 44,45 but may also be totally unexplored [46][47][48][49] . The network as a stand-alone description of gene function is not directly interpretable by us.…”

Section: Low High Mediummentioning

confidence: 99%

“…Processes can thus be defined at different levels of complexity, which results directly in a hierarchical organization of subnetworks, networks, supernetworks. The networks thus implicitly capture the hierarchical organization of biological processes in the cell, and this hierarchy can be explored by clustering the genes according to their network connections, the genes' precise functions being reflected in their memberships in different clusters [39][40][41][42][43][44][45][46][47][48][49] . Third, however one defines a process in the full network, these processes are highly interlinked.…”

Section: Properties and Limitations Of Networkmentioning

confidence: 99%

A probabilistic view of gene function

2004

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Cells are controlled by the complex and dynamic actions of thousands of genes. With the sequencing of many genomes, the key problem has shifted from identifying genes to knowing what the genes do; we need a framework for expressing that knowledge. Even the most rigorous attempts to construct ontological frameworks describing gene function (e.g., the Gene Ontology project) ultimately rely on manual curation and are thus labor-intensive and subjective. But an alternative exists: the field of functional genomics is piecing together networks of gene interactions, and although these data are currently incomplete and error-prone, they provide a glimpse of a new, probabilistic view of gene function. We outline such a framework, which revolves around a statistical description of gene interactions derived from large, systematically compiled data sets. In this probabilistic view, pleiotropy is implicit, all data have errors and the definition of gene function is an iterative process that ultimately converges on the correct functions. The relationships between the genes are defined by the data, not by hand. Even this comprehensive view fails to capture key aspects of gene function, not least their dynamics in time and space, showing that there are limitations to the model that must ultimately be addressed.The necessity for a logical framework A primary issue in biology is describing the global organization of genes into systems and understanding the coordination of these systems in the cell. The sequencing of complete genomes has yielded the full parts lists of several organisms, and we now have powerful techniques to sample different aspects of gene function at a genome-wide level. Together, these advances hold great promise in leading us closer to a complete description of the molecular biology of the cell. For us to reach this goal, however, we need some coherent framework in which to express what we learn about gene function through the accumulation of data. What do we mean by 'gene function'? More importantly, what should we mean by this? What are the key properties of genes that we should measure to adequately describe their function in the cell? If we have all the measurements, how should we integrate them into a complete description of the molecular machineries that are the basis of a cell? Our framework for thinking about gene function inevitably colors the questions we ask and the conclusions we draw; therefore, defining a rational framework is not merely an exercise in abstraction but a key tool in understanding how an organism works.Great effort (e.g., refs. 1-8) has already gone into defining precisely what we mean by the functions of genes, as well as how we should record this information. These summaries help us by identifying common features of genes with similar functions and giving clues to the organization and control of processes in the cell. They may also help us to see whether this organization can explain observed biological properties and guide us toward new processes. The functional framework itsel...

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Discovery of uncharacterized cellular systems by genome-wide analysis of functional linkages

Cited by 196 publications

References 44 publications

A Complex-based Reconstruction of the Saccharomyces cerevisiae Interactome

A Complex-based Reconstruction of the Saccharomyces cerevisiae Interactome

Predicting genetic modifier loci using functional gene networks

A probabilistic view of gene function

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