Oliver Stegle

Vireo is a Bayesian model for demultiplexing pooled single-cell RNA-seq data without requiring genotype references, showing robustness in simulations and real data.

This study systematically analyzes the MHC region to uncover genetic associations between HLA types, gene expression, and disease, revealing novel insights into disease mechanisms.

This paper introduces SVCA, a method to analyze spatial molecular data and quantify the impact of cell-cell interactions on gene expression in breast cancer tissue.

This study identifies alternative promoters as key regulators in the cancer transcriptome using RNA-Seq data from 1,188 cancer samples.

This study evaluates the impact of cold storage on scRNAseq data quality for lung, spleen, and oesophagus tissues, finding minimal effects within 24 hours but increased stress markers at 72 hours.

The paper screens for genes affecting epigenetic age acceleration in humans and identifies NSD1 as a key regulator of the epigenetic ageing clock.

The paper characterizes interaction-mediated control of protein abundance using multi-omics data from cancer samples and finds that 42% of proteins show post-transcriptional regulation.

The paper uses single-cell profiling of expression and DNA methylation to study splicing regulation and heterogeneity in human induced pluripotent stem cells.

The paper shows that targeting tandem amplified regions in CRISPR-Cas9 screens reduces cellular fitness, while chromosomal duplications have minimal effects.

The paper uses single cell triple-omics analysis to reveal how epigenetic changes drive germ layer specification in mouse embryos, showing distinct epigenetic priming and remodeling in different lineages.

Cardelino integrates whole exomes and single-cell transcriptomes to identify clone-of-origin and reveal phenotypic impacts of somatic variants in human fibroblasts.

This paper introduces StructLMM, a linear mixed model for analyzing multivariate gene-environment interactions, demonstrating its effectiveness in detecting GxE signals in BMI and eQTL data.

Kipoi provides a framework for standardized, reusable machine learning models in genomics, enabling tasks like variant effect prediction and model benchmarking.

The paper shows that during exit from pluripotency, coexpression of DNMT3s and TETs drives genome-scale DNA methylation oscillations, with amplitude dependent on CpG density, observed both in vitro and in vivo.

MOFA, a sparse Bayesian factor model with mixed likelihoods, separates shared from modality-specific multi-omic variation, recovering IGHV, trisomy 12 and oxidative-stress axes in 200 leukaemia samples.

The paper integrates genetic and cell biological data from human iPSC lines to identify intrinsic and extrinsic factors driving inter-individual variability in cell behavior.

The paper identifies genetic-environment interactions in skeletal muscle gene expression using allele-specific expression and clinical traits as environmental proxies.

SpatialDE identifies spatially variable genes from spatial transcriptomics and single-cell data, revealing genes with disease implications missed by conventional methods.

The paper analyzes RNA alterations across 27 cancer types using whole-genome sequencing data, identifying gene expression associations with DNA variants and structural fusions.

This paper introduces scNMT-seq, a method for joint profiling of chromatin accessibility, DNA methylation, and transcription in single cells, and validates it in differentiating mouse embryonic stem cells.

LiMMBo enables efficient joint genetic analysis of high-dimensional phenotypes using linear mixed models and bootstrapping, showing power advantages and scalability.

This study analyzes whole genome and RNA-Seq data from 1,188 cancer patients to map the effects of somatic and germline genetic variation on gene expression in cancer.

This paper introduces a scalable latent-factor model for scRNA-seq data that separates biological drivers from confounding effects using pathway annotations and improves gene set annotations.

The paper quantifies proteomic and genomic variation in colorectal cancer cell lines to understand how mutations affect protein levels and drug responses.

This study analyzes 522 human iPSCs to identify genetic sources of phenotypic variation, finding 5-25% of variation in traits like differentiation capacity is due to individual differences.

The paper proposes the Human Cell Atlas project to catalog all human cell types through molecular profiling and connects this data with classical cellular descriptions.

The paper introduces iSet, a method for detecting gene-context interactions using linear mixed models, and finds it identifies more interactions than alternative methods, including a gene-sex interaction for C-reactive protein.

The paper develops a DNA methylation age predictor for mice, showing it accurately measures biological age and responds to interventions like ovariectomy and diet.

This paper introduces DeepCpG, a deep learning method to predict single-cell DNA methylation states from sequence and incomplete profiles, showing improved accuracy over existing methods.

The paper develops a method to map structural variants as quantitative traits in Arabidopsis, identifying transpositions and their phenotypic effects using low-coverage sequencing data.