Biology subjects

Hackermüller, J.

Publications and source records attributed to Hackermüller, J..

6 recordsLinked to original sources

bioRxiv2026

From Lab Notes to Linked Data: MeSyTo for Ontology-Driven Metadata in Toxicological Omics

Toxicological omics studies require comprehensive metadata to support reproducibility, interoperability, and regulatory reuse. However, metadata requirements differ across public repositories, reporting frameworks, and laboratory workflows, resulting in inconsistent annotation and limited data integration. To address this challenge, we developed MeSyTo (Metadata for Systems Toxicology), an ontology-driven framework for harmonizing metadata across toxicological omics. Metadata concepts from public repositories, the OECD Omics Reporting Framework (OORF), community standards, and institutional workflows were semantically aligned and implemented as the MeSyTo Metadata Model (MMM). The MMM serves as the basis for the automatic generation of SHACL validation shapes and framework-specific metadata profiles, while curated value sets are represented as SKOS controlled vocabularies to support metadata collection and validation. The current implementation comprises 105 ontology classes and 527 data properties and supports transcriptomics, proteomics, and metabolomics. A prototype web application demonstrates ontology-driven metadata collection with integrated semantic validation and ontology-based term resolution. The ontology, validation shapes, controlled vocabularies, generation scripts, and software are publicly available as open-source resources. MeSyTo provides a reusable semantic foundation for harmonized, machine-actionable metadata and facilitates repository submission, regulatory reporting, and interoperable data exchange across toxicological omics studies.

bioinformatics↗

bioRxiv2026

There and back again: a multi-omics tale of thyroid co-expression network rewiring

The integration of multi-omics data offers unprecedented insight into complex biological systems but presents significant analytical challenges. In this study, we propose a best-practice framework for constructing simultaneous weighted gene co-expression networks (WGCNA) from transcriptomics, proteomics, and metabolomics data. Using a rodent model of thyroid toxicity induced by propylthiouracil (PTU), we analyzed thyroid tissues from control, treated, and recovery groups. We demonstrate that concatenating individually processed omics layers at the sample level--without additional scaling--preserves meaningful correlation structures and reflects best practices for biologically interpretable network construction. Co-expression networks were constructed for each group, revealing extensive disruption of molecular interactions under treatment and partial restoration during recovery. We highlight the complementary strengths of two analytical strategies: module preservation analysis identifies disrupted co-regulatory structures, while differential connectivity analysis detects feature-level rewiring events. As a methodological advance, we introduce a permutation-based approach for calculating feature-specific p-values for differential connectivity (DiffK), enabling robust statistical inference. This strategy uncovered over 4,400 significantly rewired features, many of which showed stable expression, underscoring the added value of network-based analyses. Our findings demonstrate the utility of integrated multi-omics WGCNA and differential network analysis in capturing dynamic, system-wide regulatory changes.

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bioRxiv2021

AI for predicting chemical-effect associations at the universe level - deepFPlearn

Many chemicals are out there in our environment, and all living species are exposed. However, numerous chemicals pose risks, such as developing severe diseases, if they occur at the wrong time in the wrong place. For the majority of the chemicals, these risks are not known. Chemical risk assessment and subsequent regulation of use require efficient and systematic strategies. Lab-based methods - even if high throughput - are too slow to keep up with the pace of chemical innovation. Existing computational approaches are designed for specific chemical classes or sub-problems but not usable on a large scale. Further, the application range of these approaches is limited by the low amount of available labeled training data. We present the ready-to-use and stand-alone program deepFPlearn that predicts the association between chemical structures and effects on the gene/pathway level using a combined deep learning approach. deepFPlearn uses a deep autoencoder for feature reduction before training a deep feedforward neural network to predict the target association. We received good prediction qualities and showed that our feature compression preserves relevant chemical structural information. Using a vast chemical inventory (unlabeled data) as input for the autoencoder did not reduce our prediction quality but allowed capturing a much more comprehensive range of chemical structures. We predict meaningful - experimentally verified-associations of chemicals and effects on unseen data. deepFPlearn classifies hundreds of thousands of chemicals in seconds. We provide deepFPlearn as an open-source and flexible tool that can be easily retrained and customized to different application settings at https://github.com/yigbt/deepFPlearn. Supplementary informationSupplementary data are available at bioRxiv online. Contactjana.schor@ufz.de

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bioRxiv2021

Benchmarking scRNA-seq imputation tools with respect to network inference highlights deficits in performance at high levels of sparsity

Gene correlation network inference from single-cell transcriptomics data potentially allows to gain unprecendented insights into cell type-specific regulatory programs. ScRNA-seq data is severely affected by dropout, which significantly hampers and restrains current downstream analysis. Although newly developed tools are capable to deal with sparse data, no appropriate single-cell network inference workflow has been established. A potential way to end this deadlock is the application of data imputation methods, which already proofed to be useful in specific contexts of single-cell data analysis, e.g., recovering cell clusters. In order to infer cell-type specific networks, two prerequisites must be met: the identification of cluster-specific cell-types and the network inference itself. Here, we propose a benchmarking framework to investigate both objections. By using suitable reference data with inherent correlation structure, six representative imputation tools and appropriate evaluation measures, we were able to systematically infer the impact of data imputation on network inference. Major network structures were found to be preserved in low dropout data sets. For moderately sparse data sets, DCA was able to recover gene correlation structures, although systematically introducing higher correlation values. No imputation tool was able to recover true signals from high dropout data. However, by using an additional biological data set we could show that cell-cell correlation by means of specific marker gene expression was not compromised through data imputation. Our analysis showed that network inference is feasible for low and moderately sparse data sets by using the unimputed and DCA-prepared data, respectively. High sparsity data, on the other side, still pose a major problem since current imputation techniques are not able to facilitate network inference. The annotation of cluster-specific cell-types as a prerequisite is not hampered by data imputation but their power to restore the deeply hidden correlation structures is still not sufficient enough.

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bioRxiv2020

multiGSEA: A GSEA-based pathway enrichment analysis for multi-omics data

Gaining biological insights into molecular responses to treatments or diseases from omics data can be accomplished by gene set or pathway enrichment methods. A plethora of different tools and algorithms have been developed so far. Among those, the gene set enrichment analysis (GSEA) proved to control both type I and II errors well. In recent years the call for a combined analysis of multiple omics layer became prominent, giving rise to a few multi-omics enrichment tools. Each of which has its own drawbacks and restrictions regarding its universal application. Here, we present the multiGSEA package aiding to calculate a combined GSEA-based pathway enrichment on multiple omics layer. The package queries 8 different pathway databases and relies on the robust GSEA algorithm for a single-omics enrichment analysis. In a final step, those scores will be combined to create a robust composite multi-omics pathway enrichment measure. multiGSEA supports 11 different organisms and includes a comprehensive mapping of transcripts, proteins, and metabolite IDs. It is publicly available under the GPL-3 license at https://github.com/yigbt/multiGSEA and at Bioconductor: https://bioconductor.org/packages/multiGSEA.

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bioRxiv2019

uap: Reproducible and Robust HTS Data Analysis

BackgroundA lack of reproducibility has been repeatedly criticized in computational research. High throughput sequencing (HTS) data analysis is a complex multi-step process. For most of the steps a range of bioinformatic tools is available and for most tools manifold parameters need to be set. Due to this complexity, HTS data analysis is particularly prone to reproducibility and consistency issues. We have defined four criteria that in our opinion ensure a minimal degree of reproducible research for HTS data analysis. A series of workflow management systems is available for assisting complex multi-step data analyses. However, to the best of our knowledge, none of the currently available work flow management systems satisfies all four criteria for reproducible HTS analysis.\n\nResultsHere we present uap, a workflow management system dedicated to robust, consistent, and reproducible HTS data analysis. uap is optimized for the application to omics data, but can be easily extended to other complex analyses. It is available under the GNU GPL v3 license at https://github.com/yigbt/uap.\n\nConclusionsuap is a freely available tool that enables researchers to easily adhere to reproducible research principles for HTS data analyses.

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