Author
Peter Uetz
Other affiliations: Karlsruhe Institute of Technology, University of Washington, J. Craig Venter Institute ...read more
Bio: Peter Uetz is an academic researcher from Virginia Commonwealth University. The author has contributed to research in topics: Interactome & Protein–protein interaction. The author has an hindex of 46, co-authored 133 publications receiving 13649 citations. Previous affiliations of Peter Uetz include Karlsruhe Institute of Technology & University of Washington.
Papers published on a yearly basis
Papers
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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.
Abstract: Two large-scale yeast two-hybrid screens were undertaken to identify protein-protein interactions between full-length open reading frames predicted from the Saccharomyces cerevisiae genome sequence. In one approach, we constructed a protein array of about 6,000 yeast transformants, with each transformant expressing one of the open reading frames as a fusion to an activation domain. This array was screened by a simple and automated procedure for 192 yeast proteins, with positive responses identified by their positions in the array. In a second approach, we pooled cells expressing one of about 6,000 activation domain fusions to generate a library. We used a high-throughput screening procedure to screen nearly all of the 6,000 predicted yeast proteins, expressed as Gal4 DNA-binding domain fusion proteins, against the library, and characterized positives by sequence analysis. These approaches resulted in the detection of 957 putative interactions involving 1,004 S. cerevisiae proteins. These data reveal 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. The results of these screens are shown here.
4,877 citations
TL;DR: This approach correctly predicts a functional category for 72% of the 1,393 characterized proteins with at least one partner of known function, and has been applied to predict functions for 364 previously uncharacterized proteins.
Abstract: A global analysis of 2,709 published interactions between proteins of the yeast Saccharomyces cerevisiae has been performed, enabling the establishment of a single large network of 2,358 interactions among 1,548 proteins. Proteins of known function and cellular location tend to cluster together, with 63% of the interactions occurring between proteins with a common functional assignment and 76% occurring between proteins found in the same subcellular compartment. Possible functions can be assigned to a protein based on the known functions of its interacting partners. This approach correctly predicts a functional category for 72% of the 1,393 characterized proteins with at least one partner of known function, and has been applied to predict functions for 364 previously uncharacterized proteins.
1,373 citations
Flanders Marine Institute1, Australian Museum2, University of New South Wales3, University of Southern Mississippi4, National Oceanography Centre, Southampton5, University of Hasselt6, WorldFish7, American Museum of Natural History8, San Diego State University9, Museum Victoria10, Natural History Museum11, Dowling College12, University of Hamburg13, University of Johannesburg14, James Cook University15, National Museum of Natural History16, National Taiwan Ocean University17, Scripps Institution of Oceanography18, National Oceanic and Atmospheric Administration19, University of Queensland20, University of Sassari21, Université libre de Bruxelles22, Vrije Universiteit Brussel23, Queensland Museum24, University of California, Merced25, Ghent University26, Naturalis27, Howard University28, University of Gothenburg29, California Academy of Sciences30, Florida Museum of Natural History31, Centre for Environment, Fisheries and Aquaculture Science32, Osaka University33, University of Santiago de Compostela34, University of Alaska Anchorage35, University of Málaga36, National Institute of Water and Atmospheric Research37, National University of Ireland, Galway38, University of Alaska Fairbanks39, Spanish National Research Council40, CABI41, University of Siegen42, Massey University43, University of Copenhagen44, Naturhistorisches Museum45, University of Washington46, Museum für Naturkunde47, Woods Hole Oceanographic Institution48, Western Washington University49, University of Bergen50, Nova Southeastern University51, Shirshov Institute of Oceanology52, National University of Singapore53, Shimane University54, Agnes Scott College55, University of the Ryukyus56, University of California, Davis57, Federal University of Paraná58, University of the Basque Country59, University of Veterinary Medicine Hanover60, Royal Belgian Institute of Natural Sciences61, Tel Aviv University62, Swedish Museum of Natural History63, Joint Nature Conservation Committee64, The Evergreen State College65, Estonian University of Life Sciences66, University of Maine67, Virginia Commonwealth University68, Trinity College, Dublin69, University of Auckland70
TL;DR: The first register of the marine species of the world is compiled and it is estimated that between one-third and two-thirds of marine species may be undescribed, and previous estimates of there being well over one million marine species appear highly unlikely.
822 citations
TL;DR: This comparison of the recently available protein-protein interaction networks of Caenorhabditis elegans, Drosophila melanogaster, and Saccharomyces cerevisiae revealed 71 network regions that were conserved across all three species and many exclusive to the metazoans.
Abstract: To elucidate cellular machinery on a global scale, we performed a multiple comparison of the recently available protein–protein interaction networks of Caenorhabditis elegans, Drosophila melanogaster, and Saccharomyces cerevisiae This comparison integrated protein interaction and sequence information to reveal 71 network regions that were conserved across all three species and many exclusive to the metazoans We used this conservation, and found statistically significant support for 4,645 previously undescribed protein functions and 2,609 previously undescribed protein interactions We tested 60 interaction predictions for yeast by two-hybrid analysis, confirming approximately half of these Significantly, many of the predicted functions and interactions would not have been identified from sequence similarity alone, demonstrating that network comparisons provide essential biological information beyond what is gleaned from the genome
814 citations
University of California, Los Angeles1, J. Craig Venter Institute2, Swiss Institute of Bioinformatics3, University of Rome Tor Vergata4, Simon Fraser University5, University of Edinburgh6, Ontario Institute for Cancer Research7, Centre national de la recherche scientifique8, University of British Columbia9, University of Toronto10, Claude Bernard University Lyon 111, Mount Sinai Hospital, Toronto12, Virginia Commonwealth University13, University of Lausanne14
TL;DR: The International Molecular Exchange consortium is an international collaboration between major public interaction data providers to share literature-curation efforts and make a nonredundant set of protein interactions available in a single search interface on a common website.
Abstract: The International Molecular Exchange (IMEx) consortium is an international collaboration between major public interaction data providers to share literature-curation efforts and make a nonredundant set of protein interactions available in a single search interface on a common website (http://www.imexconsortium.org/). Common curation rules have been developed, and a central registry is used to manage the selection of articles to enter into the dataset. We discuss the advantages of such a service to the user, our quality-control measures and our data-distribution practices.
490 citations
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28 Jul 2005
TL;DR: PfPMP1）与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。
18,940 citations
TL;DR: Developments in this field are reviewed, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
Abstract: Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
17,647 citations
TL;DR: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols used xiii 1.
Abstract: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201
14,171 citations
TL;DR: The latest version of STRING more than doubles the number of organisms it covers, and offers an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input.
Abstract: Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein-protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein-protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.
10,584 citations
TL;DR: The major concepts and results recently achieved in the study of the structure and dynamics of complex networks are reviewed, and the relevant applications of these ideas in many different disciplines are summarized, ranging from nonlinear science to biology, from statistical mechanics to medicine and engineering.
9,441 citations