Bioinformatics for the functional analysis of mammalian genomes

As a bioinformatics partner, Biomax Informatics AG is responsible for providing an infrastructure for genome analysis and the integration of all related information for a consortium of several academic and commercial partners involved in the exploration of mammalian genome information.

The German Federal Ministry of Education and Research (Bundesministerium für Bildung and Forschung) funds the Bioinformatics for the Functional Analysis of Mammalian Genomes (BFAM) project to integrate experimental biology with the systematic analysis of static and dynamic properties of mammalian genomes, including regulatory effects, genotype-phenotype correlations, or protein–protein interactions.

To meet the needs of the large, diverse and active user base distributed over several locations and institutions, the infrastructure provides a project-oriented environment for collecting and maintaining data from diverse sources and formats. Developments focus on the combination of various types of data to enable integrative analysis of high-throughput biological data in a single bioinformatics framework — a systematic approach which extends the value of individual data sets and analysis results by allowing relationships between sets to be uncovered.

The implemented infrastructure provides the following components:

• A framework for integrative analysis and management of high-throughput biological data, which includes statistical methods and annotation searches to reveal biologically relevant items from data sets (read more).
• Visualization and analysis tools for generating organism-specific metabolic pathways (read more) which use a Biomax catalog containing thousands of reactions.
• Systematic generation, storage and distribution of annotation for genes from the information contained in public and proprietary databases (read more) using a controlled vocabulary developed at Biomax.

Reference

Ramge A, Losko S and Heumann K (2006) Integration of biological data using BioXM Knowledge Management Environment. Bioinformatics Munich: from Genomes to Systems Biology Workshop. Munich, Germany
(PDF, 1.2 MB)

Genes are clustered according to gene expression patterns and the clusters are represented as a two-dimensional matrix. A third dimension indicates the number of genes within a cluster that have been annotated with a specified value. Thus interesting groups of genes can be identified by connecting gene expression and function.