The PEDANT genome database in 2005

The PEDANT genome database (http://pedant.gsf.de) contains pre-computed bioinformatics analyses of publicly available genomes. Its main mission is to provide robust automatic annotation of the vast majority of amino acid sequences, which have not been subjected to in-depth manual curation by human experts in high-quality protein sequence databases. By design PEDANT annotation is genome-oriented, making it possible to explore genomic context of gene products, and evaluate functional and structural content of genomes using a category-based query mechanism. At present, the PEDANT database contains exhaustive annotation of over 1 240 000 proteins from 270 eubacterial, 23 archeal and 41 eukaryotic genomes

PEDANT genome database: 10 years online

The PEDANT genome database provides exhaustive annotation of 468 genomes by a broad set of bioinformatics algorithms. We describe recent developments of the PEDANT Web server. The all-new Graphical User Interface (GUI) implemented in Java™ allows for more efficient navigation of the genome data, extended search capabilities, user customization and export facilities. The DNA and Protein viewers have been made highly dynamic and customizable. We also provide Web Services to access the entire body of PEDANT data programmatically. Finally, we report on the application of association rule mining for automatic detection of potential annotation errors. PEDANT is freely accessible to academic users at

PEDANT covers all complete RefSeq genomes

The PEDANT genome database provides exhaustive annotation of nearly 3000 publicly available eukaryotic, eubacterial, archaeal and viral genomes with more than 4.5 million proteins by a broad set of bioinformatics algorithms. In particular, all completely sequenced genomes from the NCBI's Reference Sequence collection (RefSeq) are covered. The PEDANT processing pipeline has been sped up by an order of magnitude through the utilization of precalculated similarity information stored in the similarity matrix of proteins (SIMAP) database, making it possible to process newly sequenced genomes immediately as they become available. PEDANT is freely accessible to academic users at http://pedant.gsf.de. For programmatic access Web Services are available at http://pedant.gsf.de/webservices.jsp

MIPS: analysis and annotation of genome information in 2007

The Munich Information Center for Protein Sequences (MIPS-GSF, Neuherberg, Germany) combines automatic processing of large amounts of sequences with manual annotation of selected model genomes. Due to the massive growth of the available data, the depth of annotation varies widely between independent databases. Also, the criteria for the transfer of information from known to orthologous sequences are diverse. To cope with the task of global in-depth genome annotation has become unfeasible. Therefore, our efforts are dedicated to three levels of annotation: (i) the curation of selected genomes, in particular from fungal and plant taxa (e.g. CYGD, MNCDB, MatDB), (ii) the comprehensive, consistent, automatic annotation employing exhaustive methods for the computation of sequence similarities and sequence-related attributes as well as the classification of individual sequences (SIMAP, PEDANT and FunCat) and (iii) the compilation of manually curated databases for protein interactions based on scrutinized information from the literature to serve as an accepted set of reliable annotated interaction data (MPACT, MPPI, CORUM). All databases and tools described as well as the detailed descriptions of our projects can be accessed through the MIPS web server (http://mips.gsf.de)

Applying negative rule mining to improve genome annotation

<p>Abstract</p> <p>Background</p> <p>Unsupervised annotation of proteins by software pipelines suffers from very high error rates. Spurious functional assignments are usually caused by unwarranted homology-based transfer of information from existing database entries to the new target sequences. We have previously demonstrated that data mining in large sequence annotation databanks can help identify annotation items that are strongly associated with each other, and that exceptions from strong positive association rules often point to potential annotation errors. Here we investigate the applicability of negative association rule mining to revealing erroneously assigned annotation items.</p> <p>Results</p> <p>Almost all exceptions from strong negative association rules are connected to at least one wrong attribute in the feature combination making up the rule. The fraction of annotation features flagged by this approach as suspicious is strongly enriched in errors and constitutes about 0.6% of the whole body of the similarity-transferred annotation in the PEDANT genome database. Positive rule mining does not identify two thirds of these errors. The approach based on exceptions from negative rules is much more specific than positive rule mining, but its coverage is significantly lower.</p> <p>Conclusion</p> <p>Mining of both negative and positive association rules is a potent tool for finding significant trends in protein annotation and flagging doubtful features for further inspection.</p

Uncovering metabolic pathways relevant to phenotypic traits of microbial genomes

A new machine learning-based method is presented here for the identification of metabolic pathways related to specific phenotypes in multiple microbial genomes

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

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 ()

SIMAP: the similarity matrix of proteins

Similarity Matrix of Proteins (SIMAP) () provides a database based on a pre-computed similarity matrix covering the similarity space formed by >4 million amino acid sequences from public databases and completely sequenced genomes. The database is capable of handling very large datasets and is updated incrementally. For sequence similarity searches and pairwise alignments, we implemented a grid-enabled software system, which is based on FASTA heuristics and the Smith–Waterman algorithm. Our ProtInfo system allows querying by protein sequences covered by the SIMAP dataset as well as by fragments of these sequences, highly similar sequences and title words. Each sequence in the database is supplemented with pre-calculated features generated by detailed sequence analyses. By providing WWW interfaces as well as web-services, we offer the SIMAP resource as an efficient and comprehensive tool for sequence similarity searches

CYGD: the Comprehensive Yeast Genome Database

The Comprehensive Yeast Genome Database (CYGD) compiles a comprehensive data resource for information on the cellular functions of the yeast Saccharomyces cerevisiae and related species, chosen as the best understood model organism for eukaryotes. The database serves as a common resource generated by a European consortium, going beyond the provision of sequence information and functional annotations on individual genes and proteins. In addition, it provides information on the physical and functional interactions among proteins as well as other genetic elements. These cellular networks include metabolic and regulatory pathways, signal transduction and transport processes as well as co-regulated gene clusters. As more yeast genomes are published, their annotation becomes greatly facilitated using S.cerevisiae as a reference. CYGD provides a way of exploring related genomes with the aid of the S.cerevisiae genome as a backbone and SIMAP, the Similarity Matrix of Proteins. The comprehensive resource is available under http://mips.gsf.de/genre/proj/yeast/

AGMIAL: implementing an annotation strategy for prokaryote genomes as a distributed system

We have implemented a genome annotation system for prokaryotes called AGMIAL. Our approach embodies a number of key principles. First, expert manual annotators are seen as a critical component of the overall system; user interfaces were cyclically refined to satisfy their needs. Second, the overall process should be orchestrated in terms of a global annotation strategy; this facilitates coordination between a team of annotators and automatic data analysis. Third, the annotation strategy should allow progressive and incremental annotation from a time when only a few draft contigs are available, to when a final finished assembly is produced. The overall architecture employed is modular and extensible, being based on the W3 standard Web services framework. Specialized modules interact with two independent core modules that are used to annotate, respectively, genomic and protein sequences. AGMIAL is currently being used by several INRA laboratories to analyze genomes of bacteria relevant to the food-processing industry, and is distributed under an open source license