Barbara Hutter

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

The paper introduces REMIND-Cancer Pipeline and pSNV Hunter to identify and prioritize functional promoter SNVs, including lowly recurrent and singleton mutations, using multi-omics data from the PCAWG dataset.

The paper identifies previously overlooked activating promoter mutations in cancer using a computational workflow applied to the PCAWG dataset, validating several candidates through luciferase assays.

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

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.

The paper analyzes whole-genome sequencing data from cancer samples to identify genomic footprints of telomere maintenance mechanisms, finding specific telomere variant repeat patterns associated with ATRX/DAXX mutations.

The paper examines how somatic structural variations affect TAD boundaries and gene regulation in human cancers using whole genome sequencing data.

This study analyzes chromothripsis in 2,658 cancers using whole-genome sequencing, finding it prevalent across multiple cancer types with complex structural alterations.

The paper integrates single cell RNA-seq with bulk epigenomic data to identify distinct regulatory states in bidirectional promoters and their associations with structural features and TF binding.

The paper describes the PCAWG project's large-scale analysis of cancer whole genomes across multiple computing environments, producing validated consensus variants for downstream research.

This paper introduces TEPIC, a method that combines open-chromatin data with TF binding affinities to predict gene expression, achieving results comparable to TF ChIP-seq.

TelomereHunter estimates and characterizes telomere content from whole genome sequencing data in cancer studies.

This study benchmarks somatic mutation calling in cancer genomes, identifying inconsistencies and proposing solutions to improve accuracy.

This study compares whole genome sequencing pipelines across five centers using a uniform tumor-normal sample pair, identifying factors affecting output consistency.