A5024674835- Single-cell and spatial transcriptomics
- CRISPR and Genetic Engineering
- Genomics and Chromatin Dynamics
- Microbial Metabolic Engineering and Bioproduction
- Bioinformatics and Genomic Networks
- Pluripotent Stem Cells Research
- Cell Image Analysis Techniques
- Metabolomics and Mass Spectrometry Studies
- Gene expression and cancer classification
- Nutritional Studies and Diet
- Rural development and sustainability
- RNA Research and Splicing
- Genetics, Aging, and Longevity in Model Organisms
- Genetic and phenotypic traits in livestock
- Gene Regulatory Network Analysis
- Diet and metabolism studies
- Telomeres, Telomerase, and Senescence
- Energy Load and Power Forecasting
- Plant Molecular Biology Research
Heidelberg University
2020-2024
Broad Institute
2023-2024
Heidelberg University
2024
German Cancer Research Center
2020-2022
University Hospital Heidelberg
2021-2022
Berlin Institute of Health at Charité - Universitätsmedizin Berlin
2022
Many methods allow us to extract biological activities from omics data using information prior knowledge resources, reducing the dimensionality for increased statistical power and better interpretability. Here, we present decoupleR, a Bioconductor Python package containing computational these within unified framework. decoupleR allows flexibly run any method with given resource, including that leverage mode of regulation weights interactions, which are not in other frameworks. Moreover, it...
Abstract Risk stratification is critical for the early identification of high-risk individuals and disease prevention. Here we explored potential nuclear magnetic resonance (NMR) spectroscopy-derived metabolomic profiles to inform on multidisease risk beyond conventional clinical predictors onset 24 common conditions, including metabolic, vascular, respiratory, musculoskeletal neurological diseases cancers. Specifically, trained a neural network learn disease-specific states from 168...
Factor analysis is a widely used method for dimensionality reduction in genome biology, with applications from personalized health to single-cell biology. Existing factor models assume independence of the observed samples, an assumption that fails spatio-temporal profiling studies. Here we present MEFISTO, flexible and versatile toolbox modeling high-dimensional data when spatial or temporal dependencies between samples are known. MEFISTO maintains established benefits multimodal data, but...
Abstract Factor analysis is among the most-widely used methods for dimensionality reduction in genome biology, with applications from personalized health to single-cell studies. Existing implementations of factor assume independence observed samples, an assumption that fails emerging spatio-temporal profiling Here, we present MEFISTO, a flexible and versatile toolbox modelling high-dimensional data when spatial or temporal dependencies between samples are known. MEFISTO maintains established...
Motivation Pooled single cell CRISPR screens have emerged as a powerful tool in functional genomics to probe the effect of genetic interventions at scale. A crucial step analysis resulting data is assignment cells gRNAs corresponding specific intervention. However, this challenging due lack systematic benchmarks and accessible software apply compare different guide strategies. To address this, we here propose crispat (CRISPR tool), Python package facilitate choice suitable strategy for...
Pooled single-cell CRISPR screens have emerged as a powerful tool in functional genomics to probe the effect of genetic interventions at scale. A crucial step analysis resulting data is assignment cells gRNAs corresponding specific intervention. However, this challenging due lack systematic benchmarks and accessible software apply compare different guide strategies. To address this, we here propose crispat (CRISPR tool), Python package facilitate choice suitable strategy for screens.
Population-scale resources of genetic, molecular, and cellular information form the basis for understanding human genomes, charting heritable disease, tracing effects mutations. Pooled perturbation assays applied to models, probing effect many perturbations coupled with an scRNA-seq readout (Perturb-seq), are especially potent references interpreting disease-linked mutations or gene expression changes. However, utility existing maps has been limited by comprehensiveness possible, relevance...
Aging is associated with a progressive decline in cellular function. To reset the aged phenotype, various reprogramming approaches, including mechanical routes, have been explored. However, epigenetic mechanisms underlying rejuvenation are poorly understood. Here, we studied cytoskeletal, genome-wide chromatin and transcriptional changes young, mechanically rejuvenated fibroblasts using immunofluorescence, RNA-seq Hi-C experiments. The fibroblasts, that had partially their transcription to...
Abstract Human life expectancy is constantly increasing and aging has become a major risk factor for many diseases, although the underlying gene regulatory mechanisms are still unclear. Using transcriptomic chromosomal conformation capture (Hi‐C) data from human skin fibroblasts individuals across different age groups, we identified tight coupling between changes in co‐regulation co‐localization of genes. We obtained transcription factors, cofactors, chromatin regulators that could drive...
Abstract Summary Many methods allow us to extract biological activities from omics data using information prior knowledge resources, reducing the dimensionality for increased statistical power and better interpretability. Here, we present decoupleR, a Bioconductor package containing computational these within unified framework. decoupleR allows flexibly run any method with given resource, including that leverage mode of regulation weights interactions. Using evaluated performance on...