MIPS: a database for genomes and protein sequences
Hans-Werner Mewes,Dmitrij Frishman,Ulrich Güldener,Gertrud Mannhaupt,Klaus F. X. Mayer,Martin Mokrejs,Burkhard Morgenstern,Martin Münsterkötter,Stephen Rudd,B. Weil +9 more
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TLDR
This report describes the systematic and up-to-date analysis of genomes (PEDANT), a comprehensive database of the yeast genome (MYGD), a database reflecting the progress in sequencing the Arabidopsis thaliana genome (MATD), the database of assembled, annotated human EST clusters (MEST), and the collection of protein sequence data within the framework of the PIR-International Protein Sequence Database (described elsewhere in this volume).Abstract:
The Munich Information Center for Protein Sequences (MIPS-GSF, Neuherberg, Germany) continues to provide genome-related information in a systematic way. MIPS supports both national and European sequencing and functional analysis projects, develops and maintains automatically generated and manually annotated genome-specific databases, develops systematic classification schemes for the functional annotation of protein sequences, and provides tools for the comprehensive analysis of protein sequences. This report updates the information on the yeast genome (CYGD), the Neurospora crassa genome (MNCDB), the databases for the comprehensive set of genomes (PEDANT genomes), the database of annotated human EST clusters (HIB), the database of complete cDNAs from the DHGP (German Human Genome Project), as well as the project specific databases for the GABI (Genome Analysis in Plants) and HNB (Helmholtz-Netzwerk Bioinformatik) networks. The Arabidospsis thaliana database (MATDB), the database of mitochondrial proteins (MITOP) and our contribution to the PIR International Protein Sequence Database have been described elsewhere [Schoof et al. (2002) Nucleic Acids Res., 30, 91-93; Scharfe et al. (2000) Nucleic Acids Res., 28, 155-158; Barker et al. (2001) Nucleic Acids Res., 29, 29-32]. All databases described, the protein analysis tools provided and the detailed descriptions of our projects can be accessed through the MIPS World Wide Web server (http://mips.gsf.de).read more
Citations
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Gene Ontology: tool for the unification of biology
M Ashburner,Catherine A. Ball,Judith A. Blake,David Botstein,Heather Butler,J. M. Cherry,Allan Peter Davis,Kara Dolinski,Selina S. Dwight,J.T. Eppig,Midori A. Harris,David P. Hill,Laurie Issel-Tarver,Andrew Kasarskis,Suzanna E. Lewis,John C. Matese,Joel E. Richardson,M. Ringwald,Gerald M. Rubin,Gavin Sherlock +19 more
TL;DR: The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing.
Journal ArticleDOI
Complex networks: Structure and dynamics
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.
Journal ArticleDOI
Large-scale analysis of the yeast proteome by multidimensional protein identification technology.
TL;DR: MudPIT was applied to the proteome of the Saccharomyces cerevisiae strain BJ5460 grown to mid-log phase and yielded the largest proteome analysis to date, identifying 131 proteins with three or more predicted transmembrane domains which allowed us to map the soluble domains of many of the integral membrane proteins.
Journal ArticleDOI
An automated method for finding molecular complexes in large protein interaction networks.
TL;DR: A novel graph theoretic clustering algorithm, "Molecular Complex Detection" (MCODE), that detects densely connected regions in large protein-protein interaction networks that may represent molecular complexes is described.
Journal ArticleDOI
Functional profiling of the Saccharomyces cerevisiae genome.
Guri Giaever,Angela M. Chu,Li Ni,Carla Connelly,Linda Riles,Steeve Veronneau,Sally Dow,Ankuta Lucau-Danila,Keith Anderson,Bruno André,Adam P. Arkin,Anna Astromoff,Mohamed El Bakkoury,Rhonda Bangham,Rocío Benito,Sophie Brachat,Stefano Campanaro,Matt Curtiss,Karen Davis,Adam M. Deutschbauer,K. D. Entian,Patrick Flaherty,Françoise Foury,David J. Garfinkel,Mark Gerstein,Deanna Gotte,Ulrich Güldener,Johannes H. Hegemann,Svenja Hempel,Zelek S. Herman,Daniel F. Jaramillo,Diane E. Kelly,Steven L. Kelly,Peter Kötter,Darlene LaBonte,David C. Lamb,Ning Lan,Hong Liang,Hong Liao,Lucy Y. Liu,Chuanyun Luo,Marc Lussier,Rong Mao,Patrice Menard,Siew Loon Ooi,José L. Revuelta,Christopher J. Roberts,Matthias Rose,Petra Ross-Macdonald,Bart Scherens,Greg Schimmack,Brenda Shafer,Daniel D. Shoemaker,Sharon Sookhai-Mahadeo,Reginald Storms,Jeffrey N. Strathern,Giorgio Valle,Marleen Voet,Guido Volckaert,Ching Yun Wang,Teresa R. Ward,Julie Wilhelmy,Elizabeth A. Winzeler,Yonghong Yang,Grace Yen,Elaine M. Youngman,Kexin Yu,Howard Bussey,Jef D. Boeke,Michael Snyder,Peter Philippsen,Ronald W. Davis,Mark Johnston +72 more
TL;DR: It is shown that previously known and new genes are necessary for optimal growth under six well-studied conditions: high salt, sorbitol, galactose, pH 8, minimal medium and nystatin treatment, and less than 7% of genes that exhibit a significant increase in messenger RNA expression are also required for optimal Growth in four of the tested conditions.
References
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