Peter A.C. ’t Hoen

The paper identifies major sources of variability in iPSC-derived neurons and shows how correcting these improves disease signature detection.

The paper identifies uncharacterized genes differentially expressed in immune cells stimulated by pathogens and links them to rare immune disorders.

KG-Bench benchmarks graph neural networks for drug repurposing using the Open Targets dataset, finding TransformerConv achieves the highest performance.

This study uses AlphaFold2 to predict viable hybrid domains from exon skipping combinations in USH2A, expanding therapeutic options for Usher syndrome.

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 develops a retina-specific splice prediction tool based on SpliceAI to improve identification of splice sites in the human neural retina, but it does not significantly enhance predictions for tissue-specific variants or identify novel pathogenic variants.

The paper presents a comprehensive multi-omics dataset of healthy individuals, revealing sex and age influences and providing reference ranges for omics features.

The paper generates a proteogenomic atlas of the human neural retina using long-read RNA-sequencing and mass spectrometry, identifying novel transcript isoforms and peptides.

The paper uses long-read sequencing and cDNA capture to sequence large transcripts associated with Usher syndrome in the human neural retina, identifying novel isoforms and splicing events.

The paper develops interpretable neural networks for multi-omics data to predict smoking status, age, and LDL levels, showing improved performance and generalizability across cohorts.

This paper identifies biosignatures specific to different COVID-19 disease phases through integrative multi-omics network analysis and finds that disease severity correlates with increased biosignature interactions across omics data.

This paper uses multilayer network analysis to identify molecular determinants of severity in Congenital Myasthenic Syndromes by integrating protein-protein interactions, pathways, and metabolomics data.

PANDORA v2.0 benchmarks peptide-MHC II modeling software and improves its performance and usability for generating 3D pMHC-II structures.

The paper describes EATRIS activities for quality assessment in multi-omics data generation and integration, aiming to support translational medicine through a Multi-Omics Toolbox.

The paper integrates multi-omics data from CAKUT patients to identify collagen's role in disease development and provides containerized workflows for analysis.

The paper develops a multi-omics data analysis workflow packaged as a FAIR Digital Object to improve reproducibility and reuse in computational research.

OTTERS is a TWAS framework that uses summary-level eQTL reference data to estimate eQTL weights and perform omnibus TWAS, showing improved power and applicability.

The paper constructs a transcriptome atlas of human leg muscles, identifying molecular and cellular differences associated with muscle location and function.

SUsPECT improves variant annotation by using custom long-read transcriptomes to better predict variant effects in clinical settings.

This study evaluates metabolic predictors as alternatives to clinical variables in TWAS, finding they yield similar results and can complement incomplete phenotypic data.

The paper creates a splicing atlas in the frontal cortex of DM1 patients and healthy individuals, identifying fetal-like splicing patterns and linking them to MBNL and CELF1 expression changes.

The paper uses blood eQTL meta-analysis to explore the polygenic architecture of complex traits, finding that trans-eQTLs are more informative for identifying causal genes than cis-eQTLs.

The paper compares proteogenomics and open variant search approaches for detecting single amino acid variants in shotgun proteomics data using NA12878 cell line data.

This paper introduces MR-link, a Mendelian randomization method that identifies causal relationships between gene expression and LDL-C levels, finding 19 causally linked genes including PVRL2.

The paper uses allele-specific expression analysis in a large cohort to identify high-impact, autosomal-dominant variants that are compensated by imbalanced expression.

The paper identifies 818 genes influencing DNA methylation in blood using genetic anchors and causal inference, revealing both transcription factors and non-DNA-binding proteins with regulatory roles.

This paper introduces TWMR, a Mendelian Randomization method that identifies gene-trait associations by integrating GWAS and eQTL data, revealing 3,913 novel associations across 43 human phenotypes.

The paper uses RNA-Seq data from 296 family trios to identify genomic imprinting by analyzing allelic expression and parental haplotypes, finding 45 imprinted genes and one novel imprinted transcript.