Biology subjects

Gerhauser, C.

Publications and source records attributed to Gerhauser, C..

5 recordsLinked to original sources

bioRxiv2026

High-performance xenograft read-sorting for DNA methylation analysis

Xenograft sorting tools separate sequenced DNA reads by species. We have enhanced the computational method and tool Xengsort to enable efficient epigenetic profiling of patient-derived xenograft models; it now supports cytosine-converted whole genome or reduced-representation data. Benchmarking Xengsort against several established methods (ICRG, bamcmp, XenofilteR) revealed consistently high accuracy across tools, but Xengsort outperformed the other tools in computational efficiency due to its alignment-free design.

bioinformatics↗

bioRxiv2025

Methylation reprogramming associated with aggressive prostate cancer and ancestral disparities

African men are disproportionately impacted by aggressive prostate cancer (PCa). Key to this disparity both genetic and environmental factors, alluding to epigenetic modifications. However, African-inclusive prostate tumour DNA methylation studies are lacking. Assembling a multi-geo-ancestral prostate tissue cohort, including men with (57 African, 48 European, 23 Asian) or without (65 African) PCa, we interrogate for genome-wide differential methylation. Overall, methylation appears to be driven by ancestry over geography (152 southern Africa, 41 Australia). African tumours show substantial heterogeneity, with universal hypermethylation indicating epigenetic silencing, encompassing PCa suppressor genes and enhancer-targeted binding motifs. Conversely, African tumour-associated heterochromatic hypomethylation suggests permissive chromatin remodelling, with developmental pathway activation via enhancer targets. Taken together, we show methylation aberrations favour metastatic growth, genomic instability and disease aggressiveness in African tumours, which we hypothesise is driven by extensive plasticity of intergenic regulatory regions.

genomics↗

bioRxiv2024

Pipeline Olympics: continuable benchmarking of computational workflows for DNA methylation sequencing data against an experimental gold-standard

DNA methylation is a widely studied epigenetic mark and a powerful biomarker of cell type, age, environmental exposures, and disease. Whole-genome sequencing following selective conversion of unmethylated cytosines into thymines via bisulfite treatment or enzymatic methods remains the reference method for DNA methylation profiling genome-wide. While numerous software tools facilitate processing of DNA methylation sequencing reads, a comprehensive benchmarking study has been lacking thus far. In this study, we systematically compared complete computational workflows for processing DNA methylation sequencing data using a dedicated benchmarking dataset generated with five genome-wide profiling protocols. As an evaluation reference, we employed highly quantitative locus-specific measurements from our preceding benchmark of targeted DNA methylation assays. Based on this experimental gold-standard assessment and several comprehensive metrics, we identified workflows that consistently demonstrated superior performance and revealed major workflow development trends. To facilitate the sustainability of our benchmark, we implemented an interactive workflow execution and data presentation platform, adaptable to user-defined criteria and seamlessly expandable to future software.

bioinformatics↗

bioRxiv2024

Figeno: multi-region genomic figures with long-read support

SummaryThe vast amount of publicly available genomic data requires analysis and visualization tools. Here, we present figeno, an application for generating publication-quality FIgures for GENOmics. Figeno particularly focuses on multi-region views across genomic breakpoints and on long reads with base modifications. Additionally, we support epigenomic data including ATAC-seq, ChIP-seq or HiC, as well as whole genome sequencing data with copy numbers and structural variants. Availability and ImplementationFigeno is available as a python package with both a command line and graphical user interface. It can be installed via PyPI and the source code is available at https://github.com/CompEpigen/figeno. Supplementary informationSupplementary data is provided.

bioinformatics↗

bioRxiv2019

Random forest-based modelling to detect biomarkers for prostate cancer progression

The clinical course of prostate cancer (PCa) is highly variable, demanding an individualized approach to therapy and robust prognostic markers for treatment decisions. We present a random forest-based classification model to predict aggressive behaviour of PCa. DNA methylation changes between PCa cases with good or poor prognosis (discovery cohort with n=70) were used as input. The model was validated with data from two large independent PCa cohorts from the \"International Cancer Genome Consortium\" (ICGC) and \"The Cancer Genome Atlas\" (TCGA). Ranking of cancer progression-related DNA methylation changes allowed selection of candidate genes for additional validation by immunohistochemistry. We identified loss of ZIC2 protein expression, mediated by alterations in DNA methylation, as a promising novel prognostic biomarker for PCa in >12,000 tissue micro-array tumors. The prognostic value of ZIC2 proved to be independent from established clinico-pathological variables including Gleason grade, tumor stage, nodal stage and PSA. In summary, we have developed a PCa classification model, which either directly or via expression analyses of the identified top ranked candidate genes might help in decision making related to the treatment of prostate cancer patients.

cancer biology↗