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Featured researches published by Christian von Mering.


Nucleic Acids Research | 2015

STRING v10: protein-protein interaction networks, integrated over the tree of life.

Damian Szklarczyk; Andrea Franceschini; Stefan Wyder; Kristoffer Forslund; Davide Heller; Jaime Huerta-Cepas; Milan Simonovic; Alexander Roth; Alberto Santos; Kalliopi Tsafou; Michael Kuhn; Peer Bork; Lars Juhl Jensen; Christian von Mering

The many functional partnerships and interactions that occur between proteins are at the core of cellular processing and their systematic characterization helps to provide context in molecular systems biology. However, known and predicted interactions are scattered over multiple resources, and the available data exhibit notable differences in terms of quality and completeness. The STRING database (http://string-db.org) aims to provide a critical assessment and integration of protein–protein interactions, including direct (physical) as well as indirect (functional) associations. The new version 10.0 of STRING covers more than 2000 organisms, which has necessitated novel, scalable algorithms for transferring interaction information between organisms. For this purpose, we have introduced hierarchical and self-consistent orthology annotations for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution. Further improvements in version 10.0 include a completely redesigned prediction pipeline for inferring protein–protein associations from co-expression data, an API interface for the R computing environment and improved statistical analysis for enrichment tests in user-provided networks.


Nucleic Acids Research | 2012

STRING v9.1: protein-protein interaction networks, with increased coverage and integration

Andrea Franceschini; Damian Szklarczyk; Sune Frankild; Michael Kuhn; Milan Simonovic; Alexander Roth; Jianyi Lin; Pablo Minguez; Peer Bork; Christian von Mering; Lars Juhl Jensen

Complete knowledge of all direct and indirect interactions between proteins in a given cell would represent an important milestone towards a comprehensive description of cellular mechanisms and functions. Although this goal is still elusive, considerable progress has been made—particularly for certain model organisms and functional systems. Currently, protein interactions and associations are annotated at various levels of detail in online resources, ranging from raw data repositories to highly formalized pathway databases. For many applications, a global view of all the available interaction data is desirable, including lower-quality data and/or computational predictions. The STRING database (http://string-db.org/) aims to provide such a global perspective for as many organisms as feasible. Known and predicted associations are scored and integrated, resulting in comprehensive protein networks covering >1100 organisms. Here, we describe the update to version 9.1 of STRING, introducing several improvements: (i) we extend the automated mining of scientific texts for interaction information, to now also include full-text articles; (ii) we entirely re-designed the algorithm for transferring interactions from one model organism to the other; and (iii) we provide users with statistical information on any functional enrichment observed in their networks.


Nucleic Acids Research | 2011

The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored

Damian Szklarczyk; Andrea Franceschini; Michael Kuhn; Milan Simonovic; Alexander Roth; Pablo Minguez; Tobias Doerks; Manuel Stark; Jean Muller; Peer Bork; Lars Juhl Jensen; Christian von Mering

An essential prerequisite for any systems-level understanding of cellular functions is to correctly uncover and annotate all functional interactions among proteins in the cell. Toward this goal, remarkable progress has been made in recent years, both in terms of experimental measurements and computational prediction techniques. However, public efforts to collect and present protein interaction information have struggled to keep up with the pace of interaction discovery, partly because protein–protein interaction information can be error-prone and require considerable effort to annotate. Here, we present an update on the online database resource Search Tool for the Retrieval of Interacting Genes (STRING); it provides uniquely comprehensive coverage and ease of access to both experimental as well as predicted interaction information. Interactions in STRING are provided with a confidence score, and accessory information such as protein domains and 3D structures is made available, all within a stable and consistent identifier space. New features in STRING include an interactive network viewer that can cluster networks on demand, updated on-screen previews of structural information including homology models, extensive data updates and strongly improved connectivity and integration with third-party resources. Version 9.0 of STRING covers more than 1100 completely sequenced organisms; the resource can be reached at http://string-db.org.


Nature | 2002

Comparative assessment of large-scale data sets of protein–protein interactions

Christian von Mering; Roland Krause; Berend Snel; Michael Cornell; Stephen G. Oliver; Stanley Fields; Peer Bork

Comprehensive protein–protein interaction maps promise to reveal many aspects of the complex regulatory network underlying cellular function. Recently, large-scale approaches have predicted many new protein interactions in yeast. To measure their accuracy and potential as well as to identify biases, strengths and weaknesses, we compare the methods with each other and with a reference set of previously reported protein interactions.


Nucleic Acids Research | 2009

STRING 8—a global view on proteins and their functional interactions in 630 organisms

Lars Juhl Jensen; Michael Kuhn; Manuel Stark; Samuel Chaffron; Christopher J. Creevey; Jean Muller; Tobias Doerks; Philippe Julien; Alexander Roth; Milan Simonovic; Peer Bork; Christian von Mering

Functional partnerships between proteins are at the core of complex cellular phenotypes, and the networks formed by interacting proteins provide researchers with crucial scaffolds for modeling, data reduction and annotation. STRING is a database and web resource dedicated to protein–protein interactions, including both physical and functional interactions. It weights and integrates information from numerous sources, including experimental repositories, computational prediction methods and public text collections, thus acting as a meta-database that maps all interaction evidence onto a common set of genomes and proteins. The most important new developments in STRING 8 over previous releases include a URL-based programming interface, which can be used to query STRING from other resources, improved interaction prediction via genomic neighborhood in prokaryotes, and the inclusion of protein structures. Version 8.0 of STRING covers about 2.5 million proteins from 630 organisms, providing the most comprehensive view on protein–protein interactions currently available. STRING can be reached at http://string-db.org/.


Science | 2006

Toward automatic reconstruction of a highly resolved tree of life

Francesca D. Ciccarelli; Tobias Doerks; Christian von Mering; Christopher J. Creevey; Berend Snel; Peer Bork

We have developed an automatable procedure for reconstructing the tree of life with branch lengths comparable across all three domains. The tree has its basis in a concatenation of 31 orthologs occurring in 191 species with sequenced genomes. It revealed interdomain discrepancies in taxonomic classification. Systematic detection and subsequent exclusion of products of horizontal gene transfer increased phylogenetic resolution, allowing us to confirm accepted relationships and resolve disputed and preliminary classifications. For example, we place the phylum Acidobacteria as a sister group of δ-Proteobacteria, support a Gram-positive origin of Bacteria, and suggest a thermophilic last universal common ancestor.


Nucleic Acids Research | 2004

STRING: known and predicted protein–protein associations, integrated and transferred across organisms

Christian von Mering; Lars Juhl Jensen; Berend Snel; Sean D. Hooper; Markus Krupp; Mathilde Foglierini; Nelly Jouffre; Martijn A. Huynen; Peer Bork

A full description of a proteins function requires knowledge of all partner proteins with which it specifically associates. From a functional perspective, ‘association’ can mean direct physical binding, but can also mean indirect interaction such as participation in the same metabolic pathway or cellular process. Currently, information about protein association is scattered over a wide variety of resources and model organisms. STRING aims to simplify access to this information by providing a comprehensive, yet quality-controlled collection of protein–protein associations for a large number of organisms. The associations are derived from high-throughput experimental data, from the mining of databases and literature, and from predictions based on genomic context analysis. STRING integrates and ranks these associations by benchmarking them against a common reference set, and presents evidence in a consistent and intuitive web interface. Importantly, the associations are extended beyond the organism in which they were originally described, by automatic transfer to orthologous protein pairs in other organisms, where applicable. STRING currently holds 730 000 proteins in 180 fully sequenced organisms, and is available at http://string.embl.de/.


Nucleic Acids Research | 2007

STRING 7—recent developments in the integration and prediction of protein interactions

Christian von Mering; Lars Juhl Jensen; Michael Kuhn; Samuel Chaffron; Tobias Doerks; Beate Krüger; Berend Snel; Peer Bork

Information on protein–protein interactions is still mostly limited to a small number of model organisms, and originates from a wide variety of experimental and computational techniques. The database and online resource STRING generalizes access to protein interaction data, by integrating known and predicted interactions from a variety of sources. The underlying infrastructure includes a consistent body of completely sequenced genomes and exhaustive orthology classifications, based on which interaction evidence is transferred between organisms. Although primarily developed for protein interaction analysis, the resource has also been successfully applied to comparative genomics, phylogenetics and network studies, which are all facilitated by programmatic access to the database backend and the availability of compact download files. As of release 7, STRING has almost doubled to 373 distinct organisms, and contains more than 1.5 million proteins for which associations have been pre-computed. Novel features include AJAX-based web-navigation, inclusion of additional resources such as BioGRID, and detailed protein domain annotation. STRING is available at


Nucleic Acids Research | 2017

The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible

Damian Szklarczyk; John H. Morris; Helen Cook; Michael Kuhn; Stefan Wyder; Milan Simonovic; Alberto Santos; Nadezhda T. Doncheva; Alexander Roth; Peer Bork; Lars Juhl Jensen; Christian von Mering

A system-wide understanding of cellular function requires knowledge of all functional interactions between the expressed proteins. The STRING database aims to collect and integrate this information, by consolidating known and predicted protein–protein association data for a large number of organisms. The associations in STRING include direct (physical) interactions, as well as indirect (functional) interactions, as long as both are specific and biologically meaningful. Apart from collecting and reassessing available experimental data on protein–protein interactions, and importing known pathways and protein complexes from curated databases, interaction predictions are derived from the following sources: (i) systematic co-expression analysis, (ii) detection of shared selective signals across genomes, (iii) automated text-mining of the scientific literature and (iv) computational transfer of interaction knowledge between organisms based on gene orthology. In the latest version 10.5 of STRING, the biggest changes are concerned with data dissemination: the web frontend has been completely redesigned to reduce dependency on outdated browser technologies, and the database can now also be queried from inside the popular Cytoscape software framework. Further improvements include automated background analysis of user inputs for functional enrichments, and streamlined download options. The STRING resource is available online, at http://string-db.org/.


Cell | 1998

Expression of Amino-Terminally Truncated PrP in the Mouse Leading to Ataxia and Specific Cerebellar Lesions

Doron Shmerling; Ivan Hegyi; Marek Fischer; Thomas Blättler; Sebastian Brandner; Jürgen Götz; Thomas Rülicke; Eckhard Flechsig; Antonio Cozzio; Christian von Mering; Christoph Hangartner; Adriano Aguzzi; Charles Weissmann

The physiological role of prion protein (PrP) remains unknown. Mice devoid of PrP develop normally but are resistant to scrapie; introduction of a PrP transgene restores susceptibility to the disease. To identify the regions of PrP necessary for this activity, we prepared PrP knockout mice expressing PrPs with amino-proximal deletions. Surprisingly, PrP lacking residues 32-121 or 32-134, but not with shorter deletions, caused severe ataxia and neuronal death limited to the granular layer of the cerebellum as early as 1-3 months after birth. The defect was completely abolished by introducing one copy of a wild-type PrP gene. We speculate that these truncated PrPs may be nonfunctional and compete with some other molecule with a PrP-like function for a common ligand.

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Peer Bork

University of Würzburg

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Damian Szklarczyk

Swiss Institute of Bioinformatics

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Samuel Chaffron

Vrije Universiteit Brussel

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Tobias Doerks

European Bioinformatics Institute

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Michael Kuhn

Vienna Institute of Demography

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Alexander Roth

Swiss Institute of Bioinformatics

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Andrea Franceschini

Swiss Institute of Bioinformatics

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João F. Matias Rodrigues

Swiss Institute of Bioinformatics

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