Jüri Reimand

This study identifies metabolic partnerships between medulloblastoma and macrophages that drive leptomeningeal metastases and suggests targeting the CXCL12-SCARB1-cholesterol axis as a therapeutic strategy.

This study reassesses human mitochondrial DNA methylation using multiple platforms and finds signals likely due to technical artifacts rather than biological methylation.

The paper develops a deep learning model to predict mutation density in metastatic cancers using chromatin accessibility data from primary tumors, showing lineage-linked chromatin context influences regional mutagenesis.

The paper examines therapy-associated mutagenesis at CTCF binding sites across 4,870 tumor genomes, finding increased mutation enrichment at these sites under radiotherapy and trifluridine treatment.

The paper identifies prognostic genomic interactions (PGIs) across multiple cancer types by analyzing pairwise genomic alterations and their association with patient outcomes.

The paper identifies mutational signatures in cancer genomes that alter protein sequence motifs in signaling networks, linking mutation etiology to functional consequences.

The paper identifies TOP2B binding sites as mutational hotspots in cancer genomes, showing their role in structural variants and driver mutations in genes like TP53 and MYC.

The paper identifies immunogenomic interactions across 13 cancer types using a multi-omics framework, linking genomic alterations and immune features to patient survival.

The paper introduces DPM, a directional integration method for multi-omics data that identifies consistent gene and pathway changes across datasets and highlights inconsistent evidence.

This study links chromatin accessibility in primary cancers to regional mutation patterns, showing tissue-specific and cancer-type-specific associations with mutagenesis and epigenomes.

The paper identifies genetic variants affecting phosphorylation sites in human proteins involved in SARS-CoV-2 infection and COVID-19, showing rewiring of signaling networks.

The paper characterizes mutation rate variability in regulatory elements across cancer genomes and identifies mutational signatures and drivers associated with specific genomic features.

The paper constructs a high-quality yeast binary interactome map and compares inner- and outer-complexome organization, finding the outer-complexome contains more interactions and exhibits higher functional heterogeneity.

The paper introduces the Cancer LncRNA Census (CLC), a curated resource of lncRNAs with causative roles in cancer, and shows their genomic and evolutionary features.

Step-by-step protocol for interpreting omics gene lists via g:Profiler or GSEA enrichment testing, then grouping redundant enriched pathways into themes with Cytoscape Enrichment Map.

BEHST improves genomic set enrichment analysis by linking genomic regions to gene annotations via chromatin interactions, enhancing accuracy for non-coding regions.

The paper analyzes non-coding mutations across 2,583 cancer genomes to identify genes and pathways affected, finding clusters of interacting proteins and insights into cancer mechanisms.

The paper presents a systemic analysis of PP2A-regulated phosphosignaling and drug responses, revealing rules for phosphoregulation and cancer therapy classification.

The paper identifies and characterizes coding and non-coding driver mutations across 2,500 cancer genomes, revealing new candidates and questioning previous findings.

ActiveDriverDB annotates disease mutations and population variants in post-translational modification sites using proteo-genomics data from multiple sources.