NGFN-PLUS
Bioinformatics Support
Coordinator: | Dr. Thomas Manke | |
Institution: | Max Planck Institute for Molecular Genetics | |
Homepage: | cmb.molgen.mpg.de |
The expression of genes, depending on the biological context, is regulated by protein-DNA interactions in non-coding sequence regions. Expressed genes also interact as proteins with each other for executing cellular processes. These elementary mechanisms of gene expression and protein interaction may be disturbed by sequence variations (SNPs), which can lead to the misregulation of the observed phenotypes and diseases.
In this project, we will study and classify SNPs with respect to their regulatory potential and their influence on protein function, and we will prioritize them according to their relevance for environmental disorders. To this end, we will integrate binding data of transcription factors as well as information on chromatin modifications into the analysis of non-coding SNPs. These experimental data will be complemented by our computational predictions of the binding strength of transcription factors to DNA and of the effect of sequence variations at regulatory sites.
Furthermore, we will use methods from comparative genomics to analyze sequence variations and to rank SNPs according to their degree of evolutionary conservation. In contrast to non-coding SNPs, coding SNPs in proteins will be evaluated using proteomics data and various other bioinformatics methods for protein analysis.
This aims at the generation of hypotheses for further experimental studies about the potential impact of the SNPs on the structure and interaction of the involved disease proteins. A practical outcome of our work will consist of the identification and classification of SNPs regarding their effective potential and their possible contribution to complex diseases.
Further Coordinators:
In this project, we will study and classify SNPs with respect to their regulatory potential and their influence on protein function, and we will prioritize them according to their relevance for environmental disorders. To this end, we will integrate binding data of transcription factors as well as information on chromatin modifications into the analysis of non-coding SNPs. These experimental data will be complemented by our computational predictions of the binding strength of transcription factors to DNA and of the effect of sequence variations at regulatory sites.
Furthermore, we will use methods from comparative genomics to analyze sequence variations and to rank SNPs according to their degree of evolutionary conservation. In contrast to non-coding SNPs, coding SNPs in proteins will be evaluated using proteomics data and various other bioinformatics methods for protein analysis.
This aims at the generation of hypotheses for further experimental studies about the potential impact of the SNPs on the structure and interaction of the involved disease proteins. A practical outcome of our work will consist of the identification and classification of SNPs regarding their effective potential and their possible contribution to complex diseases.
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