Robin Andersson

The paper maps context-dependent genetic regulation of cardiometabolic loci in human adipocytes using a multi-donor cell village system and single-nucleus multiome sequencing under various metabolic stress conditions.

The paper introduces scE2G models to predict enhancer-gene regulatory interactions from single-cell data and demonstrates their performance across multiple cell types and perturbations.

The paper introduces nucCAGE and PRIME to identify active cis-regulatory elements with high sensitivity and applies them to create a cell-type-resolved CRE atlas.

The paper introduces DeepCompARE, an in silico ablation framework to analyze TF cooperativity, finding most TF motifs are independent with defined synergy and redundancy effects.

This study creates a gene regulation map of the human fetal heart to interpret genetic variants associated with congenital heart disease and cardiac traits, identifying valvular interstitial cells as key regulators.

This paper links CAD GWAS variants to causal genes in SMCs using single-cell chromatin accessibility and Hi-C data, creating a scV2E2G map for disease mechanism discovery.

The paper identifies transcription factor expression as the main factor driving variability in gene co-activity domains using human expression data.

The paper shows MYC enhances cancer-specific gene programs by inducing an epigenetic switch that promotes RNAPII recruitment to enhancers.

The paper examines how promoter sequence and transcription start site architecture influence expression variability and genetic variant effects in human genes.

This paper introduces ChromTransfer, a transfer learning method that improves cell-type specific chromatin accessibility prediction from DNA sequences using pre-trained models.

The paper characterizes transposon-derived regulatory elements in mouse embryonic stem cells, showing their role in gene regulation and pluripotency programs.

hyperTRIBER is an R package for detecting differential RNA editing, showing high statistical power in analyzing low-expression and low-editing proportion scenarios.

The paper identifies overlapping mRNA targets of ECT2 and ECT3 using HyperTRIBE and shows they regulate target mRNA abundance, not alternative polyadenylation.

The paper identifies ECT2 binding sites in Arabidopsis m6A-containing mRNAs using iCLIP and HyperTRIBE, revealing sequence preferences and competition with other RNA-binding proteins.

The paper identifies widespread transcription initiation at microsatellites across species and develops deep learning models to predict STR-associated transcription and its impact on disease variants.

CAGEfightR is an R/Bioconductor package for analyzing CAGE data, enabling TSS and enhancer identification, annotation, quantification, and visualization.

The paper identifies GBAF as an enhancer-associated chromatin remodeler that interacts with H3K27ac and is essential for enhancer transcription.

The paper analyzes transcription start sites in D. melanogaster, revealing widespread divergent transcription and relationships between core promoter strength, enhancer potential, and expression levels.

The paper identifies shared expression patterns of genetic variants across tissues and cell types, enabling fine mapping of disease-associated variants and revealing key cell types involved in disease pathogenesis.

The paper decomposes transcriptional activity to identify how much is influenced by chromatin architecture and cell-specific regulatory interactions across 76 human cell types.

The paper investigates how promoter spacing influences PROMPT expression, stability, and length, revealing a mechanism for generating alternative TSSs in mammalian transcriptomes.

This paper proposes a unified model of promoters and enhancers as a single class of transcriptional regulatory elements with shared architectural features.

The paper develops a method to classify human RNAs based on exosome sensitivity and transcription initiation, identifying functional lncRNAs and distinguishing them from unstable transcripts.