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206
BioGRID: a General Repository for Interaction Datasets
, 2006
"... Access to unified datasets of protein and genetic interactions is critical for interrogation of gene/ protein function and analysis of global network properties. BioGRID is a freely accessible database of physical and genetic interactions available at ..."
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Cited by 424 (1 self)
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Access to unified datasets of protein and genetic interactions is critical for interrogation of gene/ protein function and analysis of global network properties. BioGRID is a freely accessible database of physical and genetic interactions available at
Mixed membership stochastic block models for relational data with application to protein-protein interactions
- In Proceedings of the International Biometrics Society Annual Meeting
, 2006
"... We develop a model for examining data that consists of pairwise measurements, for example, presence or absence of links between pairs of objects. Examples include protein interactions and gene regulatory networks, collections of author-recipient email, and social networks. Analyzing such data with p ..."
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Cited by 378 (52 self)
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We develop a model for examining data that consists of pairwise measurements, for example, presence or absence of links between pairs of objects. Examples include protein interactions and gene regulatory networks, collections of author-recipient email, and social networks. Analyzing such data with probabilistic models requires special assumptions, since the usual independence or exchangeability assumptions no longer hold. We introduce a class of latent variable models for pairwise measurements: mixed membership stochastic blockmodels. Models in this class combine a global model of dense patches of connectivity (blockmodel) and a local model to instantiate nodespecific variability in the connections (mixed membership). We develop a general variational inference algorithm for fast approximate posterior inference. We demonstrate the advantages of mixed membership stochastic blockmodels with applications to social networks and protein interaction networks.
Global landscape of protein complexes in the yeast Saccharomyces cerevisiae.
- Nature
, 2006
"... Identification of protein-protein interactions often provides insight into protein function, and many cellular processes are performed by stable protein complexes. We used tandem affinity purification to process 4,562 different tagged proteins of the yeast Saccharomyces cerevisiae. Each preparation ..."
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Cited by 296 (9 self)
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Identification of protein-protein interactions often provides insight into protein function, and many cellular processes are performed by stable protein complexes. We used tandem affinity purification to process 4,562 different tagged proteins of the yeast Saccharomyces cerevisiae. Each preparation was analysed by both matrix-assisted laser desorption/ ionization-time of flight mass spectrometry and liquid chromatography tandem mass spectrometry to increase coverage and accuracy. Machine learning was used to integrate the mass spectrometry scores and assign probabilities to the protein-protein interactions. Among 4,087 different proteins identified with high confidence by mass spectrometry from 2,357 successful purifications, our core data set (median precision of 0.69) comprises 7,123 protein-protein interactions involving 2,708 proteins. A Markov clustering algorithm organized these interactions into 547 protein complexes averaging 4.9 subunits per complex, about half of them absent from the MIPS database, as well as 429 additional interactions between pairs of complexes. The data (all of which are available online) will help future studies on individual proteins as well as functional genomics and systems biology.
STRING: known and predicted protein-protein associations, integrated and transferred across organisms
- Database Issue
, 2005
"... associations, integrated and transferred across organisms ..."
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Cited by 143 (16 self)
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associations, integrated and transferred across organisms
Toward a comprehensive atlas of the physical interactome of Saccharomyces cerevisiae
- Mol. Cell. Proteomics
, 2007
"... Defining protein complexes is critical to virtually all aspects of cell biology. Two recent affinity purification/mass spectrometry studies in Saccharomyces cerevisiae have vastly increased the available protein interaction data. The practical utility of such high throughput interaction sets, howeve ..."
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Cited by 137 (1 self)
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Defining protein complexes is critical to virtually all aspects of cell biology. Two recent affinity purification/mass spectrometry studies in Saccharomyces cerevisiae have vastly increased the available protein interaction data. The practical utility of such high throughput interaction sets, however, is substantially decreased by the presence of false positives. Here we created a novel probabilistic metric that takes advantage of the high density of these data, including both the presence and absence of individual associations, to provide a measure of the relative confidence of each potential protein-protein interaction. This analysis largely overcomes the noise inherent in high throughput immunoprecipitation experiments. For example, of the 12,122 binary interactions in the general repository of interaction data (BioGRID) derived from these two
Efficient algorithms for detecting signaling pathways in protein interaction networks
- Journal of Computational Biology
, 2005
"... Abstract. The interpretation of large-scale protein network data depends on our ability to identify significant sub-structures in the data, a computationally intensive task. Here we adapt and extend efficient techniques for finding paths in graphs to the problem of identifying pathways in protein in ..."
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Cited by 107 (3 self)
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Abstract. The interpretation of large-scale protein network data depends on our ability to identify significant sub-structures in the data, a computationally intensive task. Here we adapt and extend efficient techniques for finding paths in graphs to the problem of identifying pathways in protein interaction networks. We present linear-time algorithms for finding paths in networks under several biologically-motivated constraints. We apply our methodology to search for protein pathways in the yeast protein-protein interaction network. We demonstrate that our algorithm is capable of reconstructing known signaling pathways and identifying functionally enriched paths in an unsupervised manner. The algorithm is very efficient, computing optimal paths of length 8 within minutes and paths of length 10 in less than two hours. 1
The European Bioinformatics Institute’s data resources. Nucleic Acids Res 2010;38:D17–25
"... ABSTRACT The wide uptake of next-generation sequencing and other ultra-high throughput technologies by life scientists with a diverse range of interests, ..."
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Cited by 91 (7 self)
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ABSTRACT The wide uptake of next-generation sequencing and other ultra-high throughput technologies by life scientists with a diverse range of interests,
MIPS Arabidopsis thaliana database (MAtDB): an integrated biological knowledge resource based on the first complete plant genome
- Nucleic Acids Res
, 2002
"... integrated biological knowledge resource for plant genomics ..."
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Cited by 81 (7 self)
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integrated biological knowledge resource for plant genomics
GMD@CSB.DB: the Golm Metabolome Database
- Bioinformatics
, 2005
"... Summary: Metabolomics, in particular gas chromatography–mass spectrometry (GC–MS) based metabolite profiling of biological extracts, is rapidly becoming one of the cornerstones of functional genomics and systems biology. Metabolite profiling has profound applications in discovering the mode of actio ..."
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Cited by 70 (16 self)
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Summary: Metabolomics, in particular gas chromatography–mass spectrometry (GC–MS) based metabolite profiling of biological extracts, is rapidly becoming one of the cornerstones of functional genomics and systems biology. Metabolite profiling has profound applications in discovering the mode of action of drugs or herbicides, and in unravelling the effect of altered gene expression on metabolism and organism performance in biotechnological applications. As such the technology needs to be available to many laboratories. For this, an open exchange of information is required, like that already achieved for transcript and protein data. One of the key-steps in metabolite profiling is the unambiguous identification of metabolites in highly complex metabolite preparations from biological samples. Collections of mass spectra, which comprise frequently observed metabolites of either known or unknown exact chemical structure, represent the most
