The Helmholtz Network for Bioinformatics: an integrative web portal for bioinformatics resources.

SUMMARY: The Helmholtz Network for Bioinformatics (HNB) is a joint venture of eleven German bioinformatics research groups that offers convenient access to numerous bioinformatics resources through a single web portal. The 'Guided Solution Finder' which is available through the HNB portal helps users to locate the appropriate resources to answer their queries by employing a detailed, tree-like questionnaire. Furthermore, automated complex tool cascades ('tasks'), involving resources located on different servers, have been implemented, allowing users to perform comprehensive data analyses without the requirement of further manual intervention for data transfer and re-formatting. Currently, automated cascades for the analysis of regulatory DNA segments as well as for the prediction of protein functional properties are provided. AVAILABILITY: The HNB portal is available at http://www.hnbioinfo.de

Minimum information about a microarray experiment (MIAME)-toward standards for microarray data.

Microarray analysis has become a widely used tool for the generation of gene expression data on a genomic scale. Although many significant results have been derived from microarray studies, one limitation has been the lack of standards for presenting and exchanging such data. Here we present a proposal, the Minimum Information About a Microarray Experiment (MIAME), that describes the minimum information required to ensure that microarray data can be easily interpreted and that results derived from its analysis can be independently verified. The ultimate goal of this work is to establish a standard for recording and reporting microarray-based gene expression data, which will in turn facilitate the establishment of databases and public repositories and enable the development of data analysis tools. With respect to MIAME, we concentrate on defining the content and structure of the necessary information rather than the technical format for capturing it.

BioSim--a new qualitative simulation environment for molecular biology.

Traditionally, biochemical systems are modelled using kinetics and differential equations in a quantitative simulator. However, for many biological processes detailed quantitative information is not available, only qualitative or fuzzy statements about the nature of interactions. In a previous paper we have shown the applicability of qualitative reasoning methods for molecular biological regulatory processes. Now, we present a newly developed simulation environment, BioSim, that is written in Prolog using constraint logic programming techniques. The simulator combines the basic ideas of two main approaches to qualitative reasoning and integrates the contents of a molecular biology knowledge base, EcoCyc. We show that qualitative reasoning can be combined with automatic transformation of contents of genomic databases into simulation models to give an interactive modelling system that reasons about the relations and interactions of biological entities. This is demonstrated on the glycolytic pathway.

Ontologies for molecular biology.

Molecular biology has a communication problem. There are many databases using their own labels and categories for storing data objects and some using identical labels and categories but with a different meaning. A prominent example is the concept "gene" which is used with different semantics by major international genomic databases. Ontologies are one means to provide a semantic repository to systematically order relevant concepts in molecular biology and to bridge the different notions in various databases by explicitly specifying the meaning of and relation between the fundamental concepts in an application domain. Here, the upper level and a database branch of a prospective ontology for molecular biology (OMB) is presented and compared to other ontologies with respect to suitability for molecular biology (http:/(/)igd.rz-berlin.mpg.de/approximately www/oe/mbo.html).

Design and implementation of a qualitative simulation model of lambda phage infection.

MOTIVATION: Molecular biology databases hold a large number of empirical facts about many different aspects of biological entities. That data is static in the sense that one cannot ask a database 'What effect has protein A on gene B?' or 'Do gene A and gene B interact, and if so, how?'. Those questions require an explicit model of the target organism. Traditionally, biochemical systems are modelled using kinetics and differential equations in a quantitative simulator. For many biological processes however, detailed quantitative information is not available, only qualitative or fuzzy statements about the nature of interactions. RESULTS: We designed and implemented a qualitative simulation model of lambda phage growth control in Escherichia coli based on the existing simulation environment QSim. Qualitative reasoning can serve as the basis for automatic transformation of contents of genomic databases into interactive modelling systems that can reason about the relations and interactions of biological entities.

Adding semantics to genome databases: towards an ontology for molecular biology.

Molecular biology has a communication problem. There are many databases using their own labels and categories for storing data objects and some using identical labels and categories but with a different meaning. Conversely, one concept is often found under different names. Prominent examples are the concepts "gene" and "protein sequence" which are used with different semantics by major international genomic and protein databases thereby making database integration difficult and strenuous. This situation can only be improved by either defining individual semantic interfaces between each pair of databases (complexity of order n2) or by implementing one agreeable, transparent and computationally tractable semantic repository and linking each database to it (complexity of order n). Ontologies are one means to provide such semantic repository by explicitly specifying the meaning of and relation between the fundamental concepts in an application domain. Here, heuristics for building an ontology and the upper level and a database branch of a prospective Ontology for Molecular Biology are presented and compared to other ontologies with respect to suitability for molecular biology (http:/(/)igd.rz-berlin.mpg.de/www/oe/mbo.html).

IPSA-Inductive Protein Structure Analysis.

The Inductive Structure Protein Analysis (IPSA) project presents a new method for investigating protein structure. IPSA includes the creation of a new database which was designed specifically for the analysis of protein structure by statistics and machine learning. The Protein Representation Language (PRL) database includes explicit and symbolic representations of geometrical, topological and chemophysical information about secondary structures and the relationships between secondary structures. The IPSA methodology consists of: the use of PRL information to produce a new database of examples of secondary structures which associate together (examples of possible super-secondary structures); then the use of a variety of clustering techniques to produce a consensus clustering of these examples (super-secondary structures); these super-secondary structures are finally examined to uncover any biological features of significance. We have applied this method to find simple super-secondary structures consisting of pairs of alpha-helices. We found four well-defined super-secondary structures, one formed exclusively by long range interactions, and another in association with an additional element of secondary structure (alpha t alpha-motif). Examinations were carried out using homologous pairs and conformational fits which confirm our clustering.