Vicente A. Yépez

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.

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.

This study identifies homozygous loss-of-function MDGA2 variants in individuals with developmental and epileptic encephalopathy, establishing MDGA2 as a novel gene for this condition.

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

The paper improves aberrant splicing variant prediction across developmental stages and identifies developmentally restricted variants linked to neurodevelopmental disorders.

The paper uses HiFi long-read sequencing to identify genetic variants in undiagnosed rare disease families, finding novel diagnoses in 13% of cases and candidate variants in 4.3%.

This study shows that using fragment counts instead of binarized data improves scATAC-seq analysis methods like clustering and batch integration.

The paper develops AbExp, a model predicting aberrant gene expression across tissues, achieving 12% average precision by integrating variant effects and tissue-specific expression variability.

The paper shows codon optimality affects mRNA stability across human tissues, with energy metabolism and age modulating this effect.

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.

This study analyzes 2,023 pediatric exomes to diagnose mitochondrial disease, identifying 1,109 molecular diagnoses and 62 novel disease genes through exome sequencing and functional studies.

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 develops OCR-Stats, a statistical method to improve the accuracy of mitochondrial respiration measurements from Seahorse XF Analyzer data by addressing noise and variation across plates and wells.