Biology subjects

Canzler, S.

Publications and source records attributed to Canzler, S..

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.

bioinformatics↗

bioRxiv2025

From Toxicogenomics Data to Cumulative Assessment Groups: A Mechanistic Framework for Chemical Grouping

The grouping of chemicals based on shared properties or molecular mechanisms of action is pivotal for advancing regulatory toxicology, reducing data gaps, and enabling cumulative risk assessments. This study introduces a novel framework usingChemical-Gene-Phenotype-Disease (CGPD) tetramers derived from the Comparative Toxicogenomics Database (CTDbase). Our approach integrates toxicogenomics data to identify and cluster chemicals with similar molecular and phenotypic effects across diverse categories, including pesticides, pharmaceuticals, and industrial chemicals such as bisphenols and per- and poly-fluoroalkyl substances (PFAS). We validated our method by comparing CGPD Tetramer-based clusters with cumulative assessment groups (CAGs) for pesticides, demonstrating strong overlap with established groupings while identifying additional compounds relevant for risk assessment. Key examples include clusters associated with endocrine disruption and metabolic disorders.By bridging omics-derived molecular data with phenotypic and disease endpoints, this framework provides a comprehensive tool for chemical grouping and supports evidence-based regulatory decision-making, facilitating the transition to next-generation risk assessment methodologies.

pharmacology and toxicology↗

bioRxiv2021

ProteinPrompt: a webserver for predictingprotein-protein interactions

MotivationProtein-protein interactions play an essential role in a great variety of cellular processes and are therefore of significant interest for the design of new therapeutic compounds as well as the identification of side-effects due to unexpected binding. Here, we present ProteinPrompt, a webserver that uses machine-learning algorithms to calculate specific, currently unknown protein-protein interactions. Our tool is designed to quickly and reliably predict contacts based on an input sequence in order to scan large sequence libraries for potential binding partners, with the goal to accelerate and assure the quality of the laborious process of drug target identification. MethodsWe collected and thoroughly filtered a comprehensive database of known contacts from several sources, which is available as download. ProteinPrompt provides two complementary search methods of similar accuracy for comparison and consensus building. The default method is a random forest algorithm that uses the auto-correlations of seven amino acid scales. Alternatively, a graph neural network implementation can be selected. Additionally, a consensus prediction is available. For each query sequence, potential binding partners are identified from a protein sequence database. The proteom of several organisms are available and can be searched for contacts. ResultsTo evaluate the predictive power of the algorithms, we prepared a test dataset that was rigorously filtered for redundancy. No sequence pairs similar to the ones used for training were included in this dataset. With this challenging dataset, the random forest method achieved an accuracy rate of 0.88 and an area under curve of 0.95. The graph neural network achieved an accuracy rate of 0.86 using the same dataset. Since the underlying learning approaches are unrelated, comparing the results of random forest and graph neural networks reduces the likelihood of errors. The consensus reached an accuracy of 0.89. ProteinPrompt is available online at: http://proteinformatics.org/ProteinPrompt The server makes it possible to scan the human proteome for potential binding partners of an input sequence within minutes. For local offline usage, we furthermore created a ProteinPrompt Docker image which allows for batch submission: https://gitlab.hzdr.de/Proteinprompt/ProteinPrompt. In conclusion, we offer a fast, accurate, easy-to-use online service for predicting binding partners from an input sequence.

bioinformatics↗

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.

bioinformatics↗

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.

bioinformatics↗