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MIPS: analysis and annotation of proteins from whole genomes in 2005 (2006)

by H W Mewes
Venue:Nucleic Acids Res
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Proteome survey reveals modularity of the yeast cell machinery.

by Anne-Claude Gavin , Patrick Aloy , Paola Grandi , Roland Krause , Markus Boesche , Martina Marzioch , Christina Rau , Lars Juhl Jensen , Sonja Bastuck , Birgit Dümpelfeld , Angela Edelmann , Marie-Anne Heurtier , Verena Hoffman , Christian Hoefert , Karin Klein , Manuela Hudak , Anne-Marie Michon , Malgorzata Schelder , Markus Schirle , Marita Remor , Tatjana Rudi , Sean Hooper , Andreas Bauer , Tewis Bouwmeester , Georg Casari , Gerard Drewes , Gitte Neubauer , Jens M Rick , Bernhard Kuster , Robert B Russell , Giulio Superti-Furga - Nature , 2006
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Abstract - Cited by 519 (3 self) - Add to MetaCart
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BioGRID: a General Repository for Interaction Datasets

by Chris Stark, Bobby-joe Breitkreutz, Teresa Reguly, Lorrie Boucher, Ashton Breitkreutz, Mike Tyers , 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 ..."
Abstract - Cited by 424 (1 self) - Add to MetaCart
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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...ion of system level attributes and benchmarking of high throughput (HTP) interaction studies. A number of interaction databases, including BIND (6), DIP (7), HPRD (8), IntAct (9), MINT (10), and MIPS =-=(11)-=-, provide a variety of datasets and analysis tools. We have developed a biological General Repository for Interaction Datasets (BioGRID) to house and distribute comprehensive collections of physical a...

Mixed membership stochastic block models for relational data with application to protein-protein interactions

by Edoardo M. Airoldi, David M. Blei, Stephen E. Fienberg, Eric P. Xing, Tommi Jaakkola - 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 ..."
Abstract - Cited by 378 (52 self) - Add to MetaCart
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.
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...ate and escape detection. 12The MIPS database was created in 1998 based on evidence derived from a variety of experimental techniques and does not include information from high-throughput data sets (=-=Mewes et al., 2004-=-). It contains about 8000 protein complex associations in yeast. We analyze a subset of this collection containing 871 proteins, the interactions amongst which were hand-curated. In Table 1 we summari...

Global landscape of protein complexes in the yeast Saccharomyces cerevisiae.

by Nevan J Krogan , Gerard Cagney , Haiyuan Yu , Gouqing Zhong , Xinghua Guo , Alexandr Ignatchenko , Joyce Li , Shuye Pu , Nira Datta , Aaron P Tikuisis , Thanuja Punna , José M Peregrín-Alvarez , Michael Shales , Xin Zhang , Michael Davey , Mark D Robinson , Alberto Paccanaro , James E Bray , Anthony Sheung , Bryan Beattie , Dawn P Richards , Veronica Canadien , Atanas Lalev , Frank Mena , Peter Wong , Andrei Starostine , Myra M Canete , James Vlasblom , Samuel Wu , Chris Orsi , Sean R Collins , Shamanta Chandran , Robin Haw , Jennifer J Rilstone , Kiran Gandi , Natalie J Thompson , Gabe Musso , Peter St Onge , Shaun Ghanny , Mandy H Y Lam , Gareth Butland , Amin M Altaf-Ul , Shigehiko Kanaya , Ali Shilatifard , Erin O'shea , Jonathan S Weissman , C James Ingles , Timothy R Hughes , John Parkinson , Mark Gerstein , Shoshana J Wodak , Andrew Emili , Jack F Greenblatt - 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 ..."
Abstract - Cited by 296 (9 self) - Add to MetaCart
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

by Christian Von Mering, Lars J. Jensen, Berend Snel, Sean D. Hooper, Mathilde Foglierini, Nelly Jouffre, Martijn A. Huynen, Peer Bork - Database Issue , 2005
"... associations, integrated and transferred across organisms ..."
Abstract - Cited by 143 (16 self) - Add to MetaCart
associations, integrated and transferred across organisms
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... d). A purification is counted twice when one of the partners is the bait (a and d). Raw quality is: Q = logf(N together N total)/[(N alone1 + 1) (Nalone2 + 1)]}. are directly imported from databases =-=(2,5,17)-=-, and given a uniform confidence score per dataset. Another important source of protein association information is the published literature (18,19). We systematically extract associations from PubMed,...

Toward a comprehensive atlas of the physical interactome of Saccharomyces cerevisiae

by Sean R. Collins, K Patrick Kemmeren, F Xue-chu Zhao, G Jack F. Greenblatt, Forrest Spencer, G Frank C. P. Holstege, E Jonathan S. Weissman, Nevan J. Krogan A - 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 ..."
Abstract - Cited by 137 (1 self) - Add to MetaCart
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
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...used to calculate the total number of proteins in each GOslim category. MIPS and SGD Complexes—MIPS complexes were obtained from the MIPS database on March 7, 2006 using the FunCat scheme version 2.0 =-=(15)-=-. SGD complexes were extracted from the SGD database using the GO cellular component annotations. GO annotations containing the words “complex,” “subunit,” “ribosome,” “proteasome,” “nucleosome,” “rep...

Efficient algorithms for detecting signaling pathways in protein interaction networks

by Jacob Scott, Trey Ideker, Richard M. Karp, Roded Sharan - 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 ..."
Abstract - Cited by 107 (3 self) - Add to MetaCart
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
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...,...,β3 are the parameters of the distribution. Given training data, one can optimize the distribution parameters so as to maximize the likelihood of the data. As positive examples, we used the MIPS (=-=Mewes et al., 2004-=-) interaction data, which is an accepted gold standard for yeast interactions. Motivated by the large fraction of false positives in interaction data, we considered observed interactions chosen at ran...

The European Bioinformatics Institute’s data resources. Nucleic Acids Res 2010;38:D17–25

by Catherine Brooksbank , Graham Cameron , Janet Thornton
"... ABSTRACT The wide uptake of next-generation sequencing and other ultra-high throughput technologies by life scientists with a diverse range of interests, ..."
Abstract - Cited by 91 (7 self) - Add to MetaCart
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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...ember of the International MolecularInteraction Exchange (IMEx) consortium, a collaborative group of providers of molecular interaction data [other members are BIND (29), DIP (30), MINT (31) and MIPS =-=(32)-=-] that will begin to exchange data on a regular basis as of January 1. The IMEx Consortium is also campaigning for published molecular interaction data to be placed in the public domain in a standardi...

MIPS Arabidopsis thaliana database (MAtDB): an integrated biological knowledge resource based on the first complete plant genome

by Heiko Schoof, Rebecca Ernst, Vladimir Nazarov, Lukas Pfeifer, Hans-werner Mewes, Klaus F. X. Mayer - Nucleic Acids Res , 2002
"... integrated biological knowledge resource for plant genomics ..."
Abstract - Cited by 81 (7 self) - Add to MetaCart
integrated biological knowledge resource for plant genomics
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... tables that summarize general, functional, structural or comparative features (4). New features include precomputed FASTA homology scores provided by the SIMAP database [see Mewes et al., this issue =-=(16)-=-] or protein domain detection using SESAM (17). To be more ¯exible with respect to extensions of the data model, a new graphical interface, dbBrowser, was developed. For more demanding graphical explo...

GMD@CSB.DB: the Golm Metabolome Database

by Joachim Kopka, Nicolas Schauer, Stephan Krueger, Claudia Birkemeyer, Björn Usadel, Eveline Bergmüller, Peter Dörmann, Wolfram Weckwerth, Yves Gibon, Mark Stitt, Lothar Willmitzer, Alisdair R. Fernie, Dirk Steinhauser - 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 ..."
Abstract - Cited by 70 (16 self) - Add to MetaCart
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
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...2004) or MetaCyc (Krieger et al., 2004) harbour information concerning metabolic pathways, chemical reactions including inventories of the genes and enzymes involved. Genomic databases, such as MIPS (=-=Mewes et al., 2004-=-), TAIR (Rhee et al., 2003) and TIGR (Quackenbush et al., 2000), provide public access to protein sequences based on whole genome analyses, maps of protein–protein interactions, protein localization a...

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