Luregn Jan Schlapbach

The paper integrates multi-omics data from pediatric sepsis patients to identify a reproducible inflammatory host-response axis associated with disease severity.

The paper introduces a framework for defining and reusing qualifying variants (QVs) in genomic analysis to improve transparency, reproducibility, and interoperability across pipelines.

The paper introduces the Archipelago method to visualize variant set association test statistics by assigning genomic coordinates to P values, enabling combined visualization of set-level and individual variant associations.

PanelAppRex aggregates and standardizes disease gene panels to enable efficient search and integration into bioinformatic workflows, recovering causal genes in all benchmarked cases.

The paper identifies genetic pathways and variants associated with sepsis susceptibility in children through genomic analysis of a paediatric cohort.

This paper identifies a genetic locus associated with sepsis susceptibility in children using a genome-wide association study of a national cohort.

This paper introduces a statistical framework to quantify prior probabilities of disease-causing variants in inborn errors of immunity, revealing top genetic contributors and validating results in national cohorts.

The paper identifies a 5-transcript signature in blood samples that distinguishes bacterial from viral pneumonia with high accuracy.

The paper compares Nanopore direct RNA-seq and Illumina cDNA-seq in sepsis patients, finding strong correlation and identifying post- and co-transcriptional biomarkers.

This study identifies distinct lipidomic changes in febrile children with bacterial vs. viral infections, showing potential for metabolic biomarkers to aid in clinical diagnosis.