Johannes Griss

This study shows inflammation drives X-linked mosaicism resolution in the epidermis by eliminating mutant cells, with wild-type cells compensating for loss.

This study benchmarks H&E-based gene expression prediction methods in skin tissue, finding poor performance overall and limited utility for single-cell analysis.

The paper constructs a scRNA-seq atlas of chronic inflammatory skin diseases by integrating datasets from 27 diseases, identifying shared and disease-specific inflammatory signatures.

The paper evaluates consensus spectrum generation methods in proteomics, finding BEST and BIN to be the most reliable for peptide identification.

The paper introduces a standardized metadata format for proteomics datasets to improve reusability and integration across public repositories.

scClassifR is an R package that accurately classifies cell types in scRNA-seq data, offering robustness in not-classifying unknown cells and reporting ambiguous assignments.

ReactomeGSA enables multi-omics, cross-species pathway analysis by integrating data from ExpressionAtlas and TCGA, revealing B cell-related NFkappaB activation differences in cancer.

Phoenix Enhancer improves proteomics data analysis by clustering spectra to identify potential mis-identifications and enhance peptide spectrum match confidence.

IsoProt provides a reproducible workflow for analyzing iTRAQ/TMT proteomics data using open-source tools and a containerized environment.