Biology subjects

Manz, Q.

Publications and source records attributed to Manz, Q..

4 recordsLinked to original sources

bioRxiv2026

EpiATLAS - a reference for human epigenomic research

The sequence of the human genome provides a foundation for understanding cellular processes in health and disease1. The organisation of this primary genetic information into cell-specific structure and function is critical to understanding the cell type-specific interpretation and execution of the genome. Epigenetic processes are essential for packaging and higher-level functional organisation of the genome, and changes therein are increasingly recognised as contributors to human disease. Building on primary data generated by multinational consortia, the International Human Epigenome Consortium2 (IHEC) has uniformly processed a collection of more than 2000 comprehensive human reference epigenomes, collectively referred to as EpiATLAS. This effort involved the development of standardised molecular and bioinformatics protocols, metadata models, and analytical tools to manage, integrate, display, and share vast amounts of epigenomic data. This includes the creation of a publicly available Epigenome Reference Registry, which provides a system for accessing protected human subject datasets and facilitates open searching of de-identified samples and experimental data. The integrated EpiATLAS ecosystem and its comprehensive human reference epigenome maps provide an unprecedented resource for the biosciences, expanding the annotated epigenomic landscape while uncovering previously unappreciated relationships among regulatory layers and revealing how epigenetic inputs underpin fundamental cellular functions and disease associations.

genomics↗

bioRxiv2025

Inferring and Evaluating Network Medicine-Based Disease Modules with Nextflow

Most human diseases result from complex molecular interactions of genes and proteins. Various network-based computational methods characterize these mechanisms by expanding seed genes into disease-associated subnetworks, or disease modules. Evaluating these diverse methods is tedious due to unique installation and data preparation requirements. Moreover, the underlying algorithmic strategies differ, making it difficult to determine which of the created modules are most useful or biologically plausible. To address this challenge, we developed an all-in-one Nextflow pipeline that enables automated and reproducible analyses. It handles installation, input preparation, execution, and systematic evaluation of six widely used module detection tools, considering module topology, functional coherence, robustness, and the capacity to recover seeds. In addition, it annotates the resulting disease modules with biological context information, prioritizes potential drug candidates, and generates visualizations and a comprehensive summary report. To showcase the value of our pipeline and offer guidance to potential users, we performed a comprehensive evaluation across 50 different disease-network combinations, revealing substantial variability among the derived disease modules. We show that this variability is driven by differences in modeling approach, input network, and seed composition. While most methods are robust to minor perturbations, they struggle to recover omitted seeds, and none consistently outperforms others, underscoring the need for careful method selection. Our work enables the research community to systematically compare approaches for disease module discovery, promoting reproducible network medicine research. Integrated into the nf-core project (https://nf-co.re/diseasemodulediscovery), it is intended as an extendable, long-term resource for tracking progress in the field.

bioinformatics↗

bioRxiv2024

Revisiting Evidence for Epigenetic Control of Alternative Splicing

Alternative splicing is crucial for increasing eukaryotic cell transcriptome and proteome diversity. Changes in alternative splicing play a key role in cell differentiation and tissue development, and aberrations in this process have been associated with diseases. Despite its importance, the exact mechanisms for regulating alternative splicing are poorly understood. Several epigenetic marks, such as histone modification H3K36me3, have previously been associated with changes in alternative splicing. Here, we leverage the EpiATLAS data set to systematically re-evaluate evidence for epigenetic control of alternative splicing. We used SUPPA2 to calculate percentage spliced-in (PSI) values for skipped exons and retained introns and integrated this information with histone ChIP-seq and DNA methylation data. In addition to genome-wide association analysis with partial correlation and machine learning, we perform locus-specific PSI modeling on individual alternative splicing events. The latter represents a new contribution enabled by the unprecedented number of uniformly processed datasets in EpiATLAS. Our results confirm previously reported global associations of DNAm and H3K36me3 for exon inclusion and emphasize the importance of intrinsic features for genome-wide associations. On an event-specific level, trying to identify co-transcriptionally spliced events with epigenetic influence, we show that overall gene expression biases locus-specific analyses. Further, we show that epigenetic signal can predict PSI value in cis and trans, indicating sample-specific epigenetic fingerprints that distort across-sample analyses. Specifically, generalized linear models select epigenetic features predictive for PSI values not only in their genomic vicinity but also on different chromosomes. Our study demonstrates that epigenetic marks are associated with the alternative inclusion of exons and introns. Without a mechanistic explanation for these associations, our work emphasizes the need for more detailed research into the relationship between epigenetic changes and transcriptome diversity.

bioinformatics↗

bioRxiv2021

A Systematic Comparison of Differential Analysis Methods for CyTOF Data

Cytometry techniques are widely used to discover cellular characteristics at single-cell resolution. Many data analysis methods for cytometry data focus solely on identifying subpopulations via clustering and testing for differential cell abundance. For differential expression analysis of markers between conditions, only few tools exist. These tools either reduce the data distribution to medians, discarding valuable information, or have underlying assumptions that may not hold for all expression patterns. Here, we systematically evaluated existing and novel approaches for differential expression analysis on real and simulated CyTOF data. We found that methods using median marker expressions compute fast and reliable results when the data is not strongly zero-inflated. Methods using all data detect changes in strongly zero-inflated markers, but partially suffer from overprediction or cannot handle big datasets. We present a new method, CyEMD, based on calculating the Earth Movers Distance between expression distributions that can handle strong zero-inflation without being too sensitive. Additionally, we developed CYANUS, a user-friendly R Shiny App allowing the user to analyze cytometry data with state-of-the-art tools, including well-performing methods from our comparison. A public web interface is available at https://exbio.wzw.tum.de/cyanus/.

bioinformatics↗