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

By Hans-Werner Mewes, C. Amid, Roland Arnold, Dmitrij Frishman, U. Güldener, G. Mannhaupt, M. Münsterkötter, P. Pagel, N. Strack, V. Stümpflen and 2 more. Nucleic Acids Res., 2005

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The Munich Information Center for Protein Sequences (MIPS at the GSF), Neuherberg, Germany, provides resources related to genome information. Manually curated databases for several reference organisms are maintained. Several of these databases are described elsewhere in this and other recent NAR database issues. In a complementary effort, a comprehensive set of >400 genomes automatically annotated with the PEDANT system are maintained. The main goal of our current work on creating and maintaining genome databases is to extend gene centered information to information on interactions within a generic comprehensive framework. We have concentrated our efforts along three lines (i) the development of suitable comprehensive data structures and database technology, communication and query tools to include a wide range of different types of information enabling the representation of complex information such as functional modules or networks Genome Research Environment System, (ii) the development of databases covering computable information such as the basic evolutionary relations among all genes, namely SIMAP, the sequence similarity matrix and the CABiNet network analysis framework and (iii) the compilation and manual annotation of information related to interactions such as protein–protein interactions or other types of relations (e.g. MPCDB, MPPI, CYGD). All databases described and the detailed descriptions of our projects can be accessed through the MIPS WWW server ().

Subfields: Molecular Biology

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Citations in the corpus

BioGRID: a general repository for interaction datasets (2005, background): “Spurred on by these potential applications, signiﬁcant efforts to curate interaction data from the primary literature are underway by several databases (6–11), as well as by the Gene Ontology (GO) consortium (26).”
Toward a Comprehensive Atlas of the Physical Interactome of Saccharomyces cerevisiae*S (2007, methodology): “True Positive and True Negative Calculation—True positives were calculated for PPIs within complexes (for MIPS and SGD).”
A human protein-protein interaction network: a resource for annotating the proteome. (2005, neutral): “Next, we merged these data sets with published model organism PPI data from Dm [14], Ce [24], and Sc [ 28 ] and determined the appearance of orthologous Y2H interactions in interaction loop motifs.”
Mixed Membership Stochastic Blockmodels (2007, methodology): “1 PROTEIN INTERACTION DATA AND FUNCTIONAL ANNOTATION DATA The Munich Institute for Protein Sequencing (MIPS) database was created in 1998 based on evidence derived from a variety of experimental techniques (Mewes et al., 2004).”
The FunCat, a functional annotation scheme for systematic classification of proteins from whole genomes. (2004, methodology): “The dataset of yeast protein–protein interactions (29) and the associated FunCat annotation is also used for the analysis of proteome data.”
Efficient Algorithms for Detecting Signaling Pathways in Protein Interaction Networks (2005, methodology): “(a) The main chain of the known pathway, adapted from Roberts et al. (2000).”
TransportDB: a comprehensive database resource for cytoplasmic membrane transport systems and outer membrane channels (2006, neutral): “Cross-referenced links to external database are also provided, including Entrez Gene (14), TIGR’s Comprehensive Microbial Resource (CMR) (15), MIPS (16), EcoCyc (17) and PubMed.”
Evaluation of clustering algorithms for protein-protein interaction networks (2006, methodology): “We collected from the MIPS database the collection of protein complexes annotated for the yeast Saccharomyces cerevisiae [40], from which we discarded those resulting from high-throughput experiments [41].”
GC–MS libraries for the rapid identification of metabolites in complex biological samples (2005, background): “For example, MIPS hosts databases of Saccharomyces cerevisiae and Neurospora crassa which comprise maps of protein–protein interactions, protein localization, and information on transcription factors, cDNA libraries and gene homology [2].”
Scalable phylogenetic profiling using MinHash uncovers likely eukaryotic sexual reproduction genes (2019, methodology): “In that benchmark, the true positive protein-protein interactions (PPIs) were constructed using data available from CORUM [21] and the MIPS [22] databases for the human and yeast interaction datasets.”
The Biomolecular Interaction Network Database and related tools 2005 update (2004, methodology): “In addition, a network of collaborating interaction databases has been organized, the International Molecular-Interaction Exchange (IMEx) consortium which seeks to achieve similar goals, comprising the DIP (10), MINT (11), IntAct (12), MIPS (13) and BIND database organizations.”
GMD@CSB.DB: the Golm Metabolome Database (2005, methodology): “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 and many further features.”
Evaluation of different biological data and computational classification methods for use in protein interaction prediction (2006, methodology): “It should be noted that the gene expression features we used in our prediction came from a large number ( 20 ) of different experiments carried out under a diverse set of conditions.7”
Detecting overlapping protein complexes in protein-protein interaction networks (2012, methodology): “We have also used two different gold standards: the MIPS compendium of protein complexes [9] and a set derived from the Gene Ontology annotations of the Saccharomyces Genome Database [10] (Supplementary Data 2).”