Partner Description
The Interdisciplinary Centre for Bioinformatics (IZBI) is a Central research centre of the Leipzig University. The IZBI possesses its own staff, location and administration unit. IZBI promotes research in selected fields of bioinformatics and systems biology and supports interdisciplinary projects with demands in bioinformatics data analysis and modelling.
Our major fields of research are the following: Genetic Evolution to analyse genetic diversity and to use this knowledge for discerning the evolutionary relationships among species
Molecular mechanisms of gene regulation in healthy and diseased humans and upon ageing. Here we focus on transcriptional, epigenetic and genetic mechanisms and on diseases such as cancer, civilization and infectious diseases
Differentiation of cells and tissue development
Development and application methods for the analysis of high throughput omics data, of algorithms for complex multidimensional phenotyping data and of systems biological modeling on molecular, cellular and tissue levels
We are interested in molecular determinants and mechanisms that govern human’s health under a systems perspective. We focus on genomic regulation by pursuing a holistic view on different ‘omics’ realms (genome, epigenome, transcriptome, and also proteome and metabolome) and different levels of organization (molecular, cellular, tissue etc.) and relations (e.g. pathways, interaction nets).
From a methodical perspective we pursue a data-driven approach by developing and applying algorithms and methods that aim at extracting relevant biological information from large sets of high-throughput data, e.g. collected in large-scale patient-cohorts, and at developing and validating conceptual or even mathematical models e.g. about cancer progression, human development and ageing.
The molecular portrayal method developed by us uses algorithms of artificial intelligence to disentangle large and complex data. The method is complemented by a comprehensive pipeline of downstream analysis tasks such as class discovery, marker selection, functional knowledge mining and it includes visualization tools down to the single sample level.
Presently we mainly investigate a series of cancer entities such as brain, colon, skin and blood malignancies; infectious diseases and also genomic and transcriptomic features of healthy populations as reference. We analyze large scale molecular data (especially DNA, RNA and Meth Seq) generated in patient cohorts, epidemiological collectives, single cell experiments and also ‘traditional’ cell-line experiments.
Role within Project
- Immune-biology mechanisms, genomic regulation and related adverse effects of immune therapy, characterizing immune-phenotypes
- Omics analyses, machine learning, SOM portrayal of omics data sets, single cell transcriptomics, feature selection and knowledge mining
- Data management providing and analysing omics data set