Martijn Vermaat

Phenotype prediction using biologically interpretable neural networks on multi-cohort multi-omics data

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.

Mutalyzer 2: Next Generation HGVS Nomenclature Checker

Mutalyzer 2 checks and corrects HGVS variant descriptions automatically, improving accuracy in genetic data.

Genome-wide identification of genes regulating DNA methylation using genetic anchors for causal inference

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.

Integrated Clinical and Omics Approach to Rare Diseases: Novel Genes and Oligogenic Inheritance in Holoprosencephaly

This study identifies novel HPE genes and oligogenic inheritance patterns in holoprosencephaly by integrating clinical data with exome sequencing and gene prioritization.

Genome-wide identification of directed gene networks using large-scale population genomics data

The paper identifies 49 gene drivers of downstream transcriptional changes using population genomics data and RNA-sequencing from 3,072 individuals.

Full-length mRNA sequencing uncovers a widespread coupling between transcription and mRNA processing

The paper uses full-length mRNA sequencing to uncover widespread coupling between transcription and mRNA processing events in breast cancer cells and human tissues.

Disease variants alter transcription factor levels and methylation of their binding sites

The paper identifies trans-meQTLs linking disease variants to methylation changes at transcription factor binding sites, revealing eight-fold more downstream effects than trans-eQTLs.

Hypothesis-free identification of modulators of genetic risk factors

The paper identifies context-dependent eQTLs in peripheral blood RNA-seq data from Dutch individuals using a hypothesis-free approach, revealing gene expression influenced by cell type and signaling pathways.