Christian Mertes

The paper presents a standardized RNA-seq analysis framework for rare disease diagnosis, identifying molecular diagnoses in 7.7% of previously undiagnosed cases.

The paper uses fibroblast RNA-seq to improve molecular diagnosis in dystonia patients by identifying RNA expression and splicing defects missed by genomic sequencing.

PROTRIDER detects protein expression outliers in proteomics data using conditional autoencoders, outperforming existing methods and revealing enrichments of pathogenic variants in outliers.

The paper introduces the Intron Jaccard Index to improve aberrant splicing detection in RNA-seq, reducing false positives while maintaining sensitivity in rare disease diagnostics.

The paper predicts tissue-specific aberrant splicing by improving DNA-based models with RNA-seq data and benchmarking over 8.8 million variants across 49 human tissues.

The paper implements RNA-seq in diagnostics for Mendelian diseases, achieving a 16% diagnosis rate in WES-inconclusive cases by detecting aberrant expression, splicing, and mono-allelic expression.

Adding tandem mass tag proteomics to genome and transcriptome data resolved 22% of 121 unsolved suspected mitochondrial disease cases and surfaced two new disease genes.

FRASER detects aberrant splicing events in RNA-seq data, identifying pathogenic intron retention and improving sensitivity over z-score methods.

OUTRIDER detects aberrantly expressed genes in RNA-seq data by modeling co-variation and using significance-based thresholds to identify outliers.

This study uses RNA sequencing to identify genetic diagnoses in mitochondrial disease patients by detecting aberrant gene expression and splicing events.