Biology subjects

Wuerf, V.

Publications and source records attributed to Wuerf, V..

3 recordsLinked to original sources

bioRxiv2025

Navigating the Lipid Universe with LipidLibrarian: A Cross-Linked Database for Lipidomics Data Integration

There are numerous public resources and guidelines available for lipidomics research, including standard nomenclatures, classification systems, and lipid databases. However, these resources are not always aligned with one another, making it difficult to find and compare information on the same lipid across different databases. To tackle these challenges we present LipidLibrarian, a lipid search engine that enables a combined search of all major lipid databases by aggregating the available information and presenting it in a unified manner. The three main sources of information that build the foundation of LipidLibrarian as a comprehensive search-engine are SwissLipids, LIPID MAPS and ALEX123. Furthermore, various secondary resources such as LION/web, LINEX, LipidLynxX, and Goslin were incorporated to enhance the results and conduct name and hierarchy conversions. LipidLibrarian is accessible via a user-friendly website, allowing the user to query lipids using their trivial names, shorthand notations, database identifiers, or their masses. Alternatively, LipidLibrarian can be accessed as a Python package for integration into high-throughput lipidomics pipelines. The output of a LipidLibrarian query is split into multiple categories, such as nomenclature, database identifiers, masses, adducts, fragments, ontology terms, and reactions. For each of these categories, LipidLi-brarian aggregates the results from all databases and provides the source from which each value originates. This enables the user to quickly assess if the databases contain differing or conflicting information. In summary, LipidLibrarian provides an effortless, comprehensive and automated search for lipid information, thereby accelerating the research workflow and making it a meaningful tool for the scientific community.

bioinformatics↗

bioRxiv2024

MeNu GUIDE - a metabolite nutrition graph to uncover interactions with disease etiology

AO_SCPLOWBSTRACTC_SCPLOWThe relationship between diet and disease is well-documented, yet the complex interactions among foods, metabolites, and genetics makes research challenging. This study explores the potential insights offered by a knowledge graph that connects nutrition and diseases on a metabolic level. Ten ontologies and data from six databases were merged, resulting in a graph with over 25 million triple statements, stored in a Turtle file and added to a GraphDB repository. SPARQL queries revealed biases towards specific foods and conditions within the integrated databases. Despite these biases, this knowledge graph serves as a proof-of-concept, demonstrating the feasibility of integrating information from diverse resources to yield valuable insights and enabling the drawing of meaningful conclusions. The graph allows efficient identification of disease-related compounds and their food sources and enables the exploration of changes in metabolite concentrations, such as those occurring during food processing. Researchers could use such a knowledge graph to identify biomarkers, help generate new hypotheses, and improve experimental designs. Expanding the graph with automated text-mining and recipe data would further enhance its utility for nutrition research. Such a resource could advance understanding of the molecular mechanisms behind diet-disease relationships, guiding more targeted interventions.

bioinformatics↗

bioRxiv2024

LipiDetective - a deep learning model for the identification of molecular lipid species in tandem mass spectra

Lipids are involved in many vital processes within the cell, and alterations in lipid homeostasis have been associated with various diseases such as cancer or type 2 diabetes. Confidently identifying lipids in samples is a prerequisite for understanding the multiple functions lipids fulfill in health and disease. However, the accurate identification of molecular lipid species based on tandem mass spectrometry data is still a key challenge in lipidomics. Most current approaches rely on using a custom pipeline to process and match the measured spectra against an in-house spectra reference library, which hinders the comparability of results. To address this challenge, a transformer model called LipiDetective was developed and trained on a dataset composed of reference spectra measured from lipid standards, spectra from databases, and published experiments, utilizing both shotgun as well as liquid-chromatography mass spectrometry. LipiDetective demonstrates, for the first time, that artificial neural networks can learn the characteristic lipid fragmentation patterns to automatically and accurately annotate molecular lipids species in tandem mass spectra independently of the experimental setup. The model can even correctly predict lipid species for which it has never seen a spectrum before as it is able to generalize the learned lipid fragmentation patterns. Analysis of the integrated gradients reveals that LipiDetective focuses on relevant peaks that can be matched to known fragments and are thus humanly interpretable. Therefore, LipiDetective has the potential to be a valuable tool to aid in the lipid identification process and support the comparability of results from different sources. Aside from Lipidetective as a "ready-to-use" application, this work primarily offers a deeper understanding of how the model functions and how future deep learning models for lipid identification in mass spectra could be improved.

bioinformatics↗