Algorithms for Computational Genomics

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Prof. Dr. Tobias Marschall

The Marschall group develops algorithms and statistical methods for computational genomics. In particular, we work on methods to analyze high-throughput sequencing data to study genetic diversity, epigenetics, and cancer. On the one hand, we develop the required theoretical foundations in algorithmic statistics, combinatorial optimization, and sequence algorithms and, on the other hand, we apply the resulting methods in collaboration with biomedical researchers to gain biological insights. Topics addressed in the group range from algorithms for low level data processing to questions of population genetics.

Our projects

Beyond single nucleotide polymorphisms (SNPs), larger differences like insertions, deletions, translocations, and inversions significantly contribute to the genetic diversity within populations. We develop novel algorithms to discover such variants and determine their genotype from next-generation sequencing data and employ them in big projects like the Genome of the Netherlands Project. Structural variations also play a significant role in cancer genomics. One of our projects therefore aims at estimating the allele frequency of structural variations in heterogenous tumor tissue. Beyond genotypes and allele frequencies, the knowledge of the individual haplotypes is of great interest. Therefore, we develop algorithms to reconstruct haplotypes in diploid organism and within-host virus populations.