Daniel Hübschmann

This study introduces scIRES-seq to analyze CTCs from cervical cancer, revealing multimodal molecular heterogeneity through integrated genetic, epigenomic, and transcriptomic profiling.

This study links multiscale biological interactions to clinical outcomes in AML by analyzing single-cell data and developing an analytical framework.

TOPAS converts phosphoproteomic data into patient-specific reports for molecular tumor boards, focusing on clinically relevant signaling and therapeutic targets.

This study uses pan-cancer phosphoproteomics to identify therapeutic targets and improve precision oncology by analyzing 1,998 tumor samples and developing new scores for kinase activity and immune response.

The paper identifies genomic and transcriptomic features of telomere maintenance mechanisms in complex karyotype sarcomas, linking ALT status to therapeutic vulnerabilities.

This study uses single-cell transplantation and mathematical modeling to show that lineage biases in hematopoiesis arise from extrinsic feedback and clonal competition, not intrinsic HSC properties.

The paper introduces Knowledge Connector, a decision support system that integrates multiomics data with clinical information to aid in precision oncology decision-making.

ZygosityPredictor evaluates the number of gene copies affected by mutations in sequencing data, providing confidence measures for precision oncology applications.

This paper introduces an AI-based pipeline for analyzing cerebral organoids using MRI, achieving high segmentation accuracy and quality classification.

aRgus provides a webtool for visualizing and analyzing non-synonymous variants with pathogenicity scoring to assess genetic vulnerability.

rGREAT is an R/Bioconductor package that provides local implementation of the GREAT algorithm for functional enrichment on genomic regions, supporting over 500 organisms and user-provided gene sets.

The paper introduces a method to cluster and visualize functional enrichment results, showing improved consistency and exclusivity in term grouping.

The paper introduces a tool to analyze dependency heaviness in R packages and performs a global analysis of dependencies in CRAN and Bioconductor ecosystems.

This paper introduces hierarchical consensus partitioning to improve subgroup identification in high-throughput data, demonstrating its effectiveness on a DNA methylation dataset.

The paper introduces an R package that adds interactivity to static heatmaps, enabling deeper exploration through Shiny web applications.

The paper introduces an R package called spiralize for visualizing data on spirals, demonstrating its flexibility and power through real-world examples.

The paper introduces a framework for assessing the quality of whole genome cancer sequences using a star rating system based on five QC measures, providing a tool for researchers to evaluate and exclude low-quality data.

ACEseq estimates allele-specific copy numbers, tumor purity, ploidy, and homologous recombination deficiency scores from tumor whole-genome sequencing by combining GC-bias correction, replication-timing correction, and structural variant breakpoints.