Kim Philipp Jablonski

The paper evaluates nine computational tools for identifying SARS-CoV-2 lineages in wastewater sequencing data using simulated samples, finding most tools reliably detect lineages above 5% frequency.

V-pipe 3.0 is a computational pipeline for analyzing viral genomic data, enabling reproducible and scalable estimation of within-sample genetic diversity.

The paper introduces a gene set enrichment method called pareg that models inter-pathway dependencies using regularized regression, showing improved robustness to noise and ability to recover known pathways in breast cancer data.

This paper introduces a method to detect dysregulated signaling pathways in cancer by comparing normal and cancerous cells using causal inference, showing improved performance over existing methods.

This paper investigates the distribution of disease-associated SNPs relative to 3D genome topological domains and finds that cancer risk loci are preferentially located at TAD borders.

V-pipe is a computational pipeline that assesses viral genetic diversity from high-throughput sequencing data by integrating quality control, alignment, mutation calling, and haplotype inference.

The paper examines SARS-CoV-2 genetic diversity within individual patients, finding that a small number of patients carry most of the diversity and that older patients tend to have more diverse viral variants.