Dr. Jan-Niklas Runge

As part of the Molecular Pathology unit, I help in diagnosing tumors in patients by analyzing the results of genotyping assays. On top of this group effort, I develop bioinformatics solutions to track quality control parameters for regulatory compliance. I also build database structures to help the group find answers quickly.

- Conducted competitive mapping from FASTQ files to separately analyze primary and acquired genomes in a population genomics and phylogenetics study of 1000+ samples of an allopolyploid yeast. - Analyzed allele frequencies by sequencing pools of segregants, discovering novel transmission ratio distortion signals. - Explored the associated phylogenetic, structural variant, and other genomic signals to gain a comprehensive understanding of the loci under distorted transmission.

Assembled a wild house mouse genome using a hybrid approach, featuring a unique 40 Mb region. Continued collaboration with Columbia University on a population study of over 10,000 wild mice. Developed "SPORE", a pipeline that leverages identity-by-descent and Mendelian errors for accurate pedigree inferences in highly inbred populations. Validated these methods via simulated pedigrees. Analyzed inbreeding trends using runs of homozygosity and ancestry proportions, identifying deleterious loci.

Primarily remote collaboration with Columbia University. See above and below.

- Conducted extensive data preparation and implemented Generalized Linear Mixed Model (GLMM) analyses on long-term life history data, discovering a novel behavior-genotype association. - Designed and executed a series of extensive behavioral experiments involving wild house mice, confirming the novel behavior-genotype association. - Conceptualized and developed a custom agent-based simulation to analyze the evolution of dispersal in a selfish genetic element, resulting in novel insights.

Developed a custom pipeline utilizing ancestral proportion inference to facilitate the transition from raw sequencing data to imputed genotypes in ultra-low coverage (~0.03x) house mouse samples, enabling low-cost genome-wide genotyping.

- Curated CSV-like data streams utilizing SQL and SAP Hybris technologies - Conducted thorough data analysis and quality control to ensure accuracy and consistency - Collaborated with cross-functional teams to optimize data flow and streamline processes

Developed a robust HTML crawler using PHP to facilitate research on dog breeding

Thesis: "Discovery of a selfish supergene's dispersal phenotype in house mice Mus musculus domesticus." doi: 10.5167/uzh-191862

Behavioural Biology; Statistics with R Performed extensive field research in Madagascar to observe, document, and analyze animal behavior, overcoming challenging conditions

Completed Bioinformatics I & II courses, gaining expertise in analyzing genetic data and utilizing various computational tools