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Open AccessJournal ArticleDOI

Local modeling of global interactome networks

TLDR
The local modeling methodology proposed by Scholtens and Gentleman (2004) is applied to two publicly available datasets and it is formally shown that accurate local interactome models require both Y2H and AP-MS data, even in idealized situations.
Abstract
Motivation: Systems biology requires accurate models of protein complexes, including physical interactions that assemble and regulate these molecular machines. Yeast two-hybrid (Y2H) and affinity--purification/mass-spectrometry (AP--MS) technologies measure different protein--protein relationships, and issues of completeness, sensitivity and specificity fuel debate over which is best for high-throughput 'interactome' data collection. Static graphs currently used to model Y2H and AP--MS data neglect dynamic and spatial aspects of macromolecular complexes and pleiotropic protein function. Results: We apply the local modeling methodology proposed by Scholtens and Gentleman (2004) to two publicly available datasets and demonstrate its uses, interpretation and limitations. Specifically, we use this technology to address four major issues pertaining to protein--protein networks. (1) We motivate the need to move from static global interactome graphs to local protein complex models. (2) We formally show that accurate local interactome models require both Y2H and AP--MS data, even in idealized situations. (3) We briefly discuss experimental design issues and how bait selection affects interpretability of results. (4) We point to the implications of local modeling for systems biology including functional annotation, new complex prediction, pathway interactivity and coordination with gene-expression data. Availability: The local modeling algorithm and all protein complex estimates reported here can be found in the R package apComplex, available at http://www.bioconductor.org Contact: dscholtens@northwestern.edu Supplementary information: http://daisy.prevmed.northwestern.edu/~denise/pubs/LocalModeling

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Network-based prediction of protein function

TL;DR: The current computational approaches for theFunctional annotation of proteins are described, including direct methods, which propagate functional information through the network, and module‐assisted methods, who infer functional modules within the network and use those for the annotation task.
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The model organism as a system: integrating 'omics' data sets.

TL;DR: Researchers are rising to the challenge by using omics data integration to address fundamental biological questions that would increase the understanding of systems as a whole.
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iRefIndex: A consolidated protein interaction database with provenance

TL;DR: A unifying index that would facilitate searching for redundant interaction data and that would group together redundant interactionData while recording the methods used to perform this grouping is created.
References
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TL;DR: In this article, the authors discuss their experience designing and implementing a statistical computing language, which combines what they felt were useful features from two existing computer languages, and they feel that the new language provides advantages in the areas of portability, computational efficiency, memory management, and scope.
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Lethality and centrality in protein networks

TL;DR: It is demonstrated that the phenotypic consequence of a single gene deletion in the yeast Saccharomyces cerevisiae is affected to a large extent by the topological position of its protein product in the complex hierarchical web of molecular interactions.
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A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae

TL;DR: Examination of large-scale yeast two-hybrid screens reveals interactions that place functionally unclassified proteins in a biological context, interactions between proteins involved in the same biological function, and interactions that link biological functions together into larger cellular processes.
Journal ArticleDOI

An automated method for finding molecular complexes in large protein interaction networks.

TL;DR: A novel graph theoretic clustering algorithm, "Molecular Complex Detection" (MCODE), that detects densely connected regions in large protein-protein interaction networks that may represent molecular complexes is described.
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