Entity

Time filter

Source Type


Luo Q.,TU Munich | Pagel P.,TU Munich | Pagel P.,Institute for Bioinformatics and Systems Biology MIPS | Vilne B.,TU Munich | And 2 more authors.
Nucleic Acids Research | Year: 2011

Domain Interaction MAp (DIMA, available at http:// webclu.bio.wzw.tum.de/ dima) is a database of predicted and known interactions between protein domains. It integrates 5807 structurally known interactions imported from the iPfam and 3did databases and 46 900 domain interactions predicted by four computational methods: domain phylogenetic profiling, domain pair exclusion algorithm correlated mutations and domain interaction prediction in a discriminative way. Additionally predictions are filtered to exclude those domain pairs that are reported as non-interacting by the Negatome database. The DIMA Web site allows to calculate domain interaction networks either for a domain of interest or for entire organisms, and to explore them interactively using the Flash-based Cytoscape Web software. © The Author(s) 2010. Source


Mewes H.W.,Institute for Bioinformatics and Systems Biology MIPS | Mewes H.W.,TU Munich | Ruepp A.,Institute for Bioinformatics and Systems Biology MIPS | Theis F.,Institute for Bioinformatics and Systems Biology MIPS | And 11 more authors.
Nucleic Acids Research | Year: 2011

The Munich Information Center for Protein Sequences (MIPS at the Helmholtz Center for Environmental Health, Neuherberg, Germany) has many years of experience in providing annotated collections of biological data. Selected data sets of high relevance, such as model genomes, are subjected to careful manual curation, while the bulk of high-throughput data is annotated by automatic means. High-quality reference resources developed in the past and still actively maintained include Saccharomyces cerevisiae, Neurospora crassa and Arabidopsis thaliana genome databases as well as several protein interaction data sets (MPACT, MPPI and CORUM). More recent projects are PhenomiR, the database on microRNA-related phenotypes, and MIPS PlantsDB for integrative and comparative plant genome research. The interlinked resources SIMAP and PEDANT provide homology relationships as well as up-to-date and consistent annotation for 38 000 000 protein sequences. PPLIPS and CCancer are versatile tools for proteomics and functional genomics interfacing to a database of compilations from gene lists extracted from literature. A novel literature-mining tool, EXCERBT, gives access to structured information on classified relations between genes, proteins, phenotypes and diseases extracted from Medline abstracts by semantic analysis. All databases described here, as well as the detailed descriptions of our projects can be accessed through the MIPS WWW server (http://mips.helmholtz- muenchen.de). © The Author(s) 2010. Source


Blohm P.,Institute for Bioinformatics and Systems Biology MIPS | Blohm P.,Clueda AG | Frishman G.,Institute for Bioinformatics and Systems Biology MIPS | Smialowski P.,Institute for Bioinformatics and Systems Biology MIPS | And 7 more authors.
Nucleic Acids Research | Year: 2014

Knowledge about non-interacting proteins (NIPs) is important for training the algorithms to predict protein-protein interactions (PPIs) and for assessing the false positive rates of PPI detection efforts. We present the second version of Negatome, a database of proteins and protein domains that are unlikely to engage in physical interactions (available online at http://mips.helmholtz- muenchen.de/proj/ppi/negatome). Negatome is derived by manual curation of literature and by analyzing three-dimensional structures of protein complexes. The main methodological innovation in Negatome 2.0 is the utilization of an advanced text mining procedure to guide the manual annotation process. Potential non-interactions were identified by a modified version of Excerbt, a text mining tool based on semantic sentence analysis. Manual verification shows that nearly a half of the text mining results with the highest confidence values correspond to NIP pairs. Compared to the first version the contents of the database have grown by over 300%. © 2013 The Author(s). Published by Oxford University Press. Source

Discover hidden collaborations