Biology subjects

Goymann, P.

Publications and source records attributed to Goymann, P..

3 recordsLinked to original sources

bioRxiv2026

FRED: a universal tool to generate FAIR metadata for omics experiments

Scientific research relies on transparent dissemination of data and its associated interpretations. This task encompasses accessibility of raw data, its metadata, details concerning experimental design, along with parameters and tools employed for data interpretation. Production and handling of these data represents an ongoing challenge, extending beyond publication into individual facilities, institutes and research groups, often termed Research Data Management (RDM). It is foundational to scientific discovery and innovation, and can be paraphrased as Findability, Accessibility, Interoperability and Reusability (FAIR). Although the majority of peer-reviewed journals require the deposition of raw data in public repositories in alignment with FAIR principles, metadata frequently lacks full standardization. This critical gap in data management practices hinders effective utilization of research findings and complicates sharing of scientific knowledge. Here we present a flexible design of a machine-readable metadata format to store experimental metadata, along with an implementation of a generalized tool named FRED. It enables i) dialog based creation of metadata files, ii) structured semantic validation, iii) logical search, iv) an external programming interface (API), and v) a standalone web-front end. The tool is intended to be used by non-computational scientists as well as specialized facilities, and can be seamlessly integrated in existing RDM infrastructure.

bioinformatics↗

bioRxiv2025

SC-Framework: A robust and FAIR semi-interactive environment for single-cell resolution datasets

The accelerated development of single-cell technologies has profoundly impacted the field of biological research, facilitating unparalleled insights into cellular heterogeneity. However, this progress has also produced new computational challenges in the field of bioinformatics: single-cell datasets are increasingly high-dimensional, multimodal, and large-scale, while analysis workflows often remain fragmented, data type specific, ad hoc, and difficult to reproduce. The prevailing methodologies are dependent on a combination of public tools, which hinders the reproducibility of results, limits scalability, and complicates the efforts to establish benchmarks. The necessity for a higher-level, unified framework for single-cell data analysis is paramount to address these inherent limitations. Here, we introduce the SC-Framework, providing the integration of standardized data structures, declarative workflows and standardized computational backends in a containerized environment, enabling analysts to focus on biological interpretation rather than technical overhead. SC-Framework is available at GitHub (https://github.com/loosolab/SC-Framework).

bioinformatics↗

bioRxiv2019

Beyond accessibility: ATAC-seq footprinting unravels kinetics of transcription factor binding during zygotic genome activation

While footprinting analysis of ATAC-seq data can theoretically enable investigation of transcription factor (TF) binding, the lack of a computational tool able to conduct different levels of footprinting analysis has so-far hindered the widespread application of this method. Here we present TOBIAS, a comprehensive, accurate, and fast footprinting framework enabling genome-wide investigation of TF binding dynamics for hundreds of TFs simultaneously. As a proof-of-concept, we illustrate how TOBIAS can unveil complex TF dynamics during zygotic genome activation (ZGA) in both humans and mice, and explore how zygotic Dux activates cascades of TFs, binds to repeat elements and induces expression of novel genetic elements. TOBIAS is freely available at: https://github.com/loosolab/TOBIAS.

bioinformatics↗