Maurice Scheer

TRANSFAC®: transcriptional regulation, from patterns to profiles

The TRANSFAC database on eukaryotic transcriptional regulation, comprising data on transcription factors, their target genes and regulatory binding sites, has been extended and further developed, both in number of entries and in the scope and structure of the collected data. Structured fields for expression patterns have been introduced for transcription factors from human and mouse, using the CYTOMER database on anatomical structures and developmental stages. The functionality of Match, a tool for matrix-based search of transcription factor binding sites, has been enhanced. For instance, the program now comes along with a number of tissue-(or state-)specific profiles and new profiles can be created and modified with Match Profiler. The GENE table was extended and gained in importance, containing amongst others links to LocusLink, RefSeq and OMIM now. Further, (direct) links between factor and target gene on one hand and between gene and encoded factor on the other hand were introduced. The TRANSFAC public release is available at http://www.gene-regulation.com. For yeast an additional release including the latest data was made available separately as TRANSFAC Saccharomyces Module (TSM) at http://transfac.gbf.de. For CYTOMER free download versions are available at http://www.biobase.de:8080/index.html.

BRENDA, AMENDA and FRENDA the enzyme information system: new content and tools in 2009.

The BRENDA (BRaunschweig ENzyme DAtabase) (http://www.brenda-enzymes.org) represents the largest freely available information system containing a huge amount of biochemical and molecular information on all classified enzymes as well as software tools for querying the database and calculating molecular properties. The database covers information on classification and nomenclature, reaction and specificity, functional parameters, occurrence, enzyme structure and stability, mutants and enzyme engineering, preparation and isolation, the application of enzymes, and ligand-related data. The data in BRENDA are manually curated from more than 79 000 primary literature references. Each entry is clearly linked to a literature reference, the origin organism and, where available, to the protein sequence of the enzyme protein. A new search option provides the access to protein-specific data. FRENDA (Full Reference ENzyme DAta) and AMENDA (Automatic Mining of ENzyme DAta) are additional databases created by continuously improved text-mining procedures. These databases ought to provide a complete survey on enzyme data of the literature collection of PubMed. The web service via a SOAP (Simple Object Access Protocol) interface for access to the BRENDA data has been further enhanced.

BRENDA, the enzyme information system in 2011

The BRENDA (BRaunschweig ENzyme Database, http://www.brenda-enzymes.org) enzyme information system is the main collection of enzyme functional and property data for the scientific community. The majority of the data are manually extracted from the primary literature. The content covers information on function, structure, occurrence, preparation and application of enzymes as well as properties of mutants and engineered variants. The number of manually annotated references increased by 30% to more than 100 000, the number of ligand structures by 45% to almost 100 000. New query, analysis and data management tools were implemented to improve data processing, data presentation, data input and data access. BRENDA now provides new viewing options such as the display of the statistics of functional parameters and the 3D view of protein sequence and structure features. Furthermore a ligand summary shows comprehensive information on the BRENDA ligands. The enzymes are linked to their respective pathways and can be viewed in pathway maps. The disease text mining part is strongly enhanced. It is possible to submit new, not yet classified enzymes to BRENDA, which then are reviewed and classified by the International Union of Biochemistry and Molecular Biology. A new SBML output format of BRENDA kinetic data allows the construction of organism-specific metabolic models.

PrediSi: prediction of signal peptides and their cleavage positions

We have developed PrediSi (Prediction of Signal peptides), a new tool for predicting signal peptide sequences and their cleavage positions in bacterial and eukaryotic amino acid sequences. In contrast to previous prediction tools, our new software is especially useful for the analysis of large datasets in real time with high accuracy. PrediSi allows the evaluation of whole proteome datasets, which are currently accumulating as a result of numerous genome projects and proteomics experiments. The method employed is based on a position weight matrix approach improved by a frequency correction which takes in to consideration the amino acid bias present in proteins. The software was trained using sequences extracted from the most recent version of the SwissProt database. PrediSi is accessible via a web interface. An extra Java package was designed for the integration of PrediSi into other software projects. The tool is freely available on the World Wide Web at http://www.predisi.de.

JCat: a novel tool to adapt codon usage of a target gene to its potential expression host

A novel method for the adaptation of target gene codon usage to most sequenced prokaryotes and selected eukaryotic gene expression hosts was developed to improve heterologous protein production. In contrast to existing tools, JCat (Java Codon Adaptation Tool) does not require the manual definition of highly expressed genes and is, therefore, a very rapid and easy method. Further options of JCat for codon adaptation include the avoidance of unwanted cleavage sites for restriction enzymes and Rho-independent transcription terminators. The output of JCat is both graphically and as Codon Adaptation Index (CAI) values given for the pasted sequence and the newly adapted sequence. Additionally, a list of genes in FASTA-format can be uploaded to calculate CAI values. In one example, all genes of the genome of Caenorhabditis elegans were adapted to Escherichia coli codon usage and further optimized to avoid commonly used restriction sites. In a second example, the Pseudomonas aeruginosa exbD gene codon usage was adapted to E.coli codon usage with parallel avoidance of the same restriction sites. For both, the degree of introduced changes was documented and evaluated. JCat is integrated into the PRODORIC database that hosts all required information on the various organisms to fulfill the requested calculations. JCat is freely accessible at .

BRENDA in 2013: integrated reactions, kinetic data, enzyme function data, improved disease classification: new options and contents in BRENDA

The BRENDA (BRaunschweig ENzyme DAtabase) enzyme portal (http://www.brenda-enzymes.org) is the main information system of functional biochemical and molecular enzyme data and provides access to seven interconnected databases. BRENDA contains 2.7 million manually annotated data on enzyme occurrence, function, kinetics and molecular properties. Each entry is connected to a reference and the source organism. Enzyme ligands are stored with their structures and can be accessed via their names, synonyms or via a structure search. FRENDA (Full Reference ENzyme DAta) and AMENDA (Automatic Mining of ENzyme DAta) are based on text mining methods and represent a complete survey of PubMed abstracts with information on enzymes in different organisms, tissues or organelles. The supplemental database DRENDA provides more than 910 000 new EC number–disease relations in more than 510 000 references from automatic search and a classification of enzyme–disease-related information. KENDA (Kinetic ENzyme DAta), a new amendment extracts and displays kinetic values from PubMed abstracts. The integration of the EnzymeDetector offers an automatic comparison, evaluation and prediction of enzyme function annotations for prokaryotic genomes. The biochemical reaction database BKM-react contains non-redundant enzyme-catalysed and spontaneous reactions and was developed to facilitate and accelerate the construction of biochemical models.

Virtual Footprint and PRODORIC: an integrative framework for regulon prediction in prokaryotes

SUMMARY A new online framework for the accurate and integrative prediction of transcription factor binding sites (TFBSs) in prokaryotes was developed. The system consists of three interconnected modules: (1) The PRODORIC database as a comprehensive data source and extensive collection of TFBSs with corresponding position weight matrices. (2) The pattern matching tool Virtual Footprint for the prediction of genome based regulons and for the analysis of individual promoter regions. (3) The interactive genome browser GBPro for the visualization of TFBS search results in their genomic context and links to gene and regulator-specific information in PRODORIC. The aim of this service is to provide researchers a free and easy to use collection of interconnected tools in the field of molecular microbiology, infection and systems biology. AVAILABILITY http://www.prodoric.de/vfp.

Anaerobic adaptation in Pseudomonas aeruginosa: definition of the Anr and Dnr regulons

The anaerobic metabolism of the opportunistic pathogen Pseudomonas aeruginosa is important for growth and biofilm formation during persistent infections. The two Fnr-type transcription factors Anr and Dnr regulate different parts of the underlying network in response to oxygen tension and NO. Little is known about all members of the Anr and Dnr regulons and the mediated immediate response to oxygen depletion. Comprehensive transcriptome and bioinformatics analyses in combination with a limited proteome analyses were used for the investigation of the P. aeruginosa response to an immediate oxygen depletion and for definition of the corresponding Anr and Dnr regulons. We observed at first the activation of fermentative pathways for immediate energy generation followed by induction of alternative respiratory chains. A solid position weight matrix model was deduced from the experimentally identified Anr boxes and used for identification of 170 putative Anr boxes in potential P. aeruginosa promoter regions. The combination with the experimental data unambiguously identified 130 new members for the Anr and Dnr regulons. The basis for the understanding of two regulons of P. aeruginosa central to biofilm formation and infection is now defined.

The BRENDA Tissue Ontology (BTO): the first all-integrating ontology of all organisms for enzyme sources

BTO, the BRENDA Tissue Ontology (http://www.BTO.brenda-enzymes.org) represents a comprehensive structured encyclopedia of tissue terms. The project started in 2003 to create a connection between the enzyme data collection of the BRENDA enzyme database and a structured network of source tissues and cell types. Currently, BTO contains more than 4600 different anatomical structures, tissues, cell types and cell lines, classified under generic categories corresponding to the rules and formats of the Gene Ontology Consortium and organized as a directed acyclic graph (DAG). Most of the terms are endowed with comments on their derivation or definitions. The content of the ontology is constantly curated with ∼1000 new terms each year. Four different types of relationships between the terms are implemented. A versatile web interface with several search and navigation functionalities allows convenient online access to the BTO and to the enzymes isolated from the tissues. Important areas of applications of the BTO terms are the detection of enzymes in tissues and the provision of a solid basis for text-mining approaches in this field. It is widely used by lab scientists, curators of genomic and biochemical databases and bioinformaticians. The BTO is freely available at http://www.obofoundry.org.

SYSTOMONAS — an integrated database for systems biology analysis of Pseudomonas

To provide an integrated bioinformatics platform for a systems biology approach to the biology of pseudomonads in infection and biotechnology the database SYSTOMONAS (SYSTems biology of pseudOMONAS) was established. Besides our own experimental metabolome, proteome and transcriptome data, various additional predictions of cellular processes, such as gene-regulatory networks were stored. Reconstruction of metabolic networks in SYSTOMONAS was achieved via comparative genomics. Broad data integration is realized using SOAP interfaces for the well established databases BRENDA, KEGG and PRODORIC. Several tools for the analysis of stored data and for the visualization of the corresponding results are provided, enabling a quick understanding of metabolic pathways, genomic arrangements or promoter structures of interest. The focus of SYSTOMONAS is on pseudomonads and in particular Pseudomonas aeruginosa, an opportunistic human pathogen. With this database we would like to encourage the Pseudomonas community to elucidate cellular processes of interest using an integrated systems biology strategy. The database is accessible at .