Matthias Heinig

The paper develops a proteomic aging clock using longitudinal plasma proteomics data to show that biological aging is dynamically influenced by modifiable risk factors.

The paper shows that sampling biases in negative regulatory interactions inflate performance in gene regulatory network inference and proposes a degree-aware sampling approach to address this issue.

The paper presents results from an open competition to develop aging biomarkers using DNA methylation data, showing improved predictive models for age, mortality, and multi-morbidity.

The paper performs a federated cis-eQTL meta-analysis across single-cell datasets to identify cell-type-specific genetic effects and link them to disease mechanisms.

This study integrates multi-omic data from glioblastoma patients to identify a high-risk progenitor phenotype linked to poor survival and spatially defined tumor progression.

The paper maps cis- and trans-eQTLs in blood samples to infer gene regulatory networks and prioritize trait-relevant pathways.

This study creates an integrated single-cell atlas of human atherosclerotic plaques using scRNA-seq data, enabling accurate cell type annotation and downstream analyses.

The paper identifies co-eQTLs that alter gene co-expression relationships in specific cell types, linking these variants to immune disease associations.

This study evaluates foundation models on human 3'UTR sequences, finding that 3UTR-specific models outperform genome-wide models in three of four RNA biology tasks.

The paper maps tissue-specific mtDNA heteroplasmy and its associations with age and mtDNA gene expression using bulk RNAseq data from GTEx.

The paper investigates the role of mitochondrial DNA in gene regulation across 48 tissues and 684 individuals, identifying eQTLs and associations with nuclear DNA genes.

The paper develops and validates multiplexing strategies for brain organoids combined with single cell RNA-seq to study neurodevelopmental trajectories across multiple genetic lines.

Speos predicts core genes for complex diseases using graph representation learning, achieving validation rates comparable to Mendelian disorder genes and identifying druggable targets.

The paper develops a machine learning model to predict promoter proximal Pol II pausing using chromatin and transcriptome data, identifies novel regulators, and quantifies their effects on gene regulation.

The paper reconstructs regulatory networks underlying trans-QTL hotspots using human multi-omics data and biological priors, identifying novel networks related to schizophrenia and lean body mass.

The paper maps binary SARS-CoV-2 protein interactions with host proteins, identifying key processes like immune signaling and ubiquitination, and suggests therapeutic targets.

The paper identifies co-expression QTLs (co-eQTLs) using single-cell RNA-seq data, revealing how genetic variants affect gene co-expression patterns and regulatory networks in specific cell types.

This paper introduces a neural network method for somatic SNP calling in WGS tumor samples without a matched normal, showing effectiveness across 5 cancer types.

This study integrates genomics, transcriptomics, and proteomics of human atrial tissue to identify regulatory variants and trans-acting networks associated with atrial fibrillation.

The paper presents a statistical framework for experimental design and power analysis in single cell RNA-seq studies, showing that higher power is achieved with shallow sequencing of more cells per individual.

The paper identifies a trans locus in rats that causes a protein length-dependent shift in translational efficiency, affecting cardiac hypertrophy and sarcomere protein stoichiometry.

The paper characterizes the cellular composition and gene expression of the adult human heart using single-cell transcriptomics, identifying distinct cell types and their functional roles.

The paper presents a computational framework to parameterize large-scale mechanistic models for predicting drug response in cancer cell lines using exome and transcriptome data, achieving higher accuracy than statistical approaches.

The paper develops a pipeline to detect microbial and viral reads in human RNA-seq data and creates the MetaMap resource for disease-related microbiome analysis.