High-Throughput Genomics and Systems Biology

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Dr. Marcel H. Schulz

The group develops computational systems biology models and algorithms that further the understanding of transcriptional and post-transcriptional regulation using different genomics data. The main research areas are investigation of regulatory networks in different human diseases to (i) predict new disease gene candidates, (ii) elucidate novel functions of gene products and RNAs, and (iii) study the interplay of small regulatory RNAs and epigenetic modifications. The group applies different techniques from Machine Learning and develops efficient algorithms to improve sequencing data analysis.

Our Projects

Integrative models of gene regulation

The group is interested in deciphering the links between transcriptional and post-transcriptional regulation. In particular we study microRNAs and other small RNAs, transcription factors and their relations to histone modifications with focus on the following topics:

• Modelling dynamic networks from time-series expression and interaction data
• Machine Learning methods for miRNA-target prediction from RNA-seq data
• Small RNA networks and interaction with epigenetic modifications
• Methods for spatial relation of histone modifications to gene expression
• Probabilistic models for open chromatin analysis

Efficient algorithms for sequencing data

With the vast amount of sequencing data produced by modern sequencers, efficent algorithms are essential to leverage the full potential in practice. We apply different string data structures for sequence indexing to devise faster and more accurate algorithms for the analysis of sequencing data with focus on the following topics:

• De novo assembly from NGS data with de Bruijn graphs
• De novo read error correction methods
• Efficient bisulfite methylation analysis for personalized genomics