Hiltrud Muhle

Long-read genome sequencing resolves complex structural variants in exome-negative developmental and epileptic encephalopathy

This study evaluates the diagnostic value of long-read genome sequencing in DEE patients, finding it particularly effective for resolving complex structural variants not detected by short-read sequencing.

DNM1-related disorder is characterized by recurrent variants and phenotypic homogeneity

The paper analyzes a large DNM1 cohort to identify mutational hotspots and genotype-phenotype correlations in DNM1-related disorder.

Transcription factor binding and individual genetic risk for valproate teratogenicity

The paper investigates how genetic variants affecting transcription factor binding contribute to valproate-induced birth defects and identifies EP300 as a key regulator involved in this process.

Evaluating Genome Sequencing Strategies: Trio, Singleton, and Standard Testing in Rare Disease Diagnosis

This study compares trio, singleton, and standard genome sequencing in rare disease diagnosis, finding trio sequencing has the highest diagnostic yield.

Ultra-rare constrained missense variants in the epilepsies: Shared and specific enrichment patterns in neuronal gene-sets

The paper analyzes ultra-rare variants in epilepsy patients to identify gene-set enrichment patterns and differential impacts in neuronal gene categories.

Ultra-rare genetic variation in the epilepsies: a whole-exome sequencing study of 17,606 individuals

This study identifies ultra-rare genetic variants associated with different epilepsy types using whole-exome sequencing in 17,634 individuals.

De novo Variants In Neurodevelopmental Disorders With Epilepsy

The paper identifies de novo variants in neurodevelopmental disorders with epilepsy and finds associations with specific genes and clinical features.

Characterization of Glycosylphosphatidylinositol Biosynthesis Defects by Clinical Features, Flow Cytometry, and Automated Image Analysis

The paper evaluates clinical features, flow cytometry, and facial image analysis to classify GPIBDs and identify gene-specific patterns, finding that facial recognition software is most effective for gene prediction.