Moritz Gerstung

The paper identifies LMO7 as a fusion-regulated driver of metastasis in Ewing sarcoma using spatial transcriptomics and integrates clinical data to show its association with poor outcomes.

The paper shows that irradiation reprograms tumor endothelium to enhance T cell infiltration and effector function in gliomas, improving immunotherapy outcomes.

ProteoPy provides a framework for integrated proteomics analysis using AnnData, enabling proteoform group inference and differential analysis with integration into multi-omics workflows.

The paper introduces CHOCOLAT-G2P, a method integrating functional genomics and spatial transcriptomics to study genotype-phenotype relationships in tumors, revealing zonation patterns and epistasis in liver cancer.

Segger improves cell segmentation in spatial transcriptomics by using a graph neural network, achieving high sensitivity and speed with minimal manual intervention.

The paper identifies an X-Y translocation in Four-Core Genotypes mice that increases Tlr7 expression and affects genetic interpretation.

SpatialData, a Zarr/OME-NGFF-based format plus Python library, unifies images, labels, points, shapes and tables across spatial omics platforms with coordinate alignment, lazy loading and cross-modal aggregation.

This study finds that somatic mutation rates per year decrease with species lifespan across mammals, suggesting evolutionary constraints on mutagenesis and its role in ageing.

cell2location uses a Bayesian model to integrate single-cell and spatial transcriptomics, mapping cell types in situ across mouse brain regions and human lymph node immune compartments.

PoSTcode improves transcript decoding from spatial transcriptomic images by reducing mislabeling and doublet signals.

This study analyzes within-host genetic diversity in 1,181 SARS-CoV-2 samples, finding high mutation rates, strand asymmetries, and purifying selection, with implications for transmission chain reconstruction.

The paper quantifies mismatch repair (MMR) activity in human tissues by analyzing mutation rates in individuals with constitutional MMR deficiency, revealing genotype-specific mutational signatures and the role of MMR components in DNA repair.

This study shows genomic copy number changes can predict oesophageal cancer up to 10 years before diagnosis, enabling earlier treatment and reduced unnecessary monitoring.

This paper uses deep transfer learning on H&E images to correlate histopathological patterns with genomic and survival data across 28 cancer types, revealing associations between histopathology and molecular features.

The paper analyzes mutational spectra in C. elegans DNA repair mutants to identify how different repair pathways affect mutagenesis in germ cells.

This study shows that mutation spectra in C. elegans are shaped by both DNA damage and repair processes, with repair deficiencies altering mutation rates and spectra in many experimental conditions.

The paper examines how somatic mutation distributions vary across chromatin folding domains in cancer genomes, revealing distinct patterns associated with different mutational processes.

The paper identifies mutational signatures linked to DNA mismatch repair deficiency in C. elegans and human cancers, revealing similarities and differences between species.

The paper quantifies selection patterns in cancer evolution using evolutionary genomics methods across 7,664 human cancers, finding positive selection dominates and many driver mutations occur in unknown genes.

The paper develops a prognostic algorithm for AML using genomic and clinical data, showing improved prediction accuracy over current methods.