Biology subjects

Ranzinger, R.

Publications and source records attributed to Ranzinger, R..

3 recordsLinked to original sources

bioRxiv2026

BiomarkerKB: FAIR and Integrated Biomarker Knowledge Connecting Biomolecular and Clinical Data Types

Biomarkers are essential tools for disease detection, risk assessment, therapeutic monitoring, and precision medicine. However, biomarker data are dispersed across heterogeneous resources, inconsistently reported in the literature, and rarely standardized for computational use. This fragmentation limits reproducibility, cross-study integration, and the discovery of novel biomarker and disease relationships. We developed BiomarkerKB, a knowledgebase designed to harmonize and integrate biomarker information under a standardized data model. The model follows the FDA-NIH BEST biomarker definition and captures both core fields (biomarker entity, disease/condition, exposure agent) and contextual metadata (specimen, biomarker role, evidence, provenance). Biomarker data and related annotations were either curated from publications or collected from public resources (e.g., OpenTargets, GWAS Catalog, ClinVar, CIViC, OncoMX) and were also contributed by the Common Fund Data Coordinating Centers and the Early Detection Research Network (EDRN). Standardization was achieved using ontologies and reference resources such as Disease Ontology, UBERON, UniProtKB, and HUGO Gene Nomenclature Committee (HGNC) gene symbols. BiomarkerKB data were ingested into a Neo4j-based knowledge graph and integrated with the Common Fund Data Ecosystem (CFDE) Knowledge Graph. The initial release of BiomarkerKB contains over 200,000 biomarker-disease associations spanning genes, proteins, metabolites, glycans, and chemical elements. The knowledge graph comprises more than 300,000 nodes and 1.2 million edges, enabling structured exploration of biomarker relationships within CFDE data as demonstrated through the knowledge graph query-based use cases presented in this study. A publicly accessible web portal (https://biomarkerkb.org) provides keyword search, filtering, data downloads, and access to graph visualization to support both researchers and computational analyses. BiomarkerKB addresses a critical gap in biomarker informatics by providing an integrated, FAIR (Findable, Accessible, Interoperable, and Reusable), and unified framework for biomarker knowledge exploration and discovery.

bioinformatics↗

bioRxiv2025

The Data Distillery: A Graph Framework for Semantic Integration and Querying of Biomedical Data

The Data Distillery Knowledge Graph (DDKG) is a framework for semantic integration and querying of biomedical data across domains. Built for the NIH Common Fund Data Ecosystem, it supports translational research by linking clinical and experimental datasets in a unified graph model. Clinical standards such as ICD-10, SNOMED, and DrugBank are integrated through UMLS, while genomics and basic science data are structured using ontologies and standards such as HPO, GENCODE, Ensembl, STRING, and ClinVar. The DDKG uses a property graph architecture based on the UBKG infrastructure and supports ontology-based ingestion, identifier normalization, and graph-native querying. The system is modular and can be extended with new datasets or schema modules. We demonstrate its utility for informatics queries across eight use cases, including regulatory variant analysis, tissue-specific expression, biomarker discovery, and cross-species variant prioritization. The DDKG is accessible via a public interface, a programmatic API, and downloadable builds for local use.

bioinformatics↗

bioRxiv2020

Mass spectrometric method for the unambiguous profiling of cellular dynamic glycosylation.

Various biological processes at the cellular level are regulated by glycosylation which is a highly micro-heterogeneous post-translational modification (PTM) on proteins and lipids. The dynamic nature of glycosylation can be studied through bio-orthogonal tagging of metabolically engineered non-natural sugars into glycan epitopes. However, this approach possesses a significant drawback due to non-specific background reactions and ambiguity of non-natural sugar metabolism. Here we report a tag-free strategy for their direct detection by glycoproteomics and glycomics using mass spectrometry. The method dramatically simplifies the detection of non-natural functional group bearing monosaccharides installed through promiscuous sialic acid, GalNAc, and GlcNAc biosynthetic pathways. Multistage enrichment of glycoproteins by cellular fractionation, subsequent ZIC-HILIC based glycopeptide enrichment, and a spectral enrichment algorithm for the MS data processing enabled direct detection of non-natural monosaccharides that are incorporated at low abundance on the N/O-glycopeptides along with their natural counterparts. Our approach allowed the detection of both natural and non-natural sugar bearing glycopeptides, N and O-glycopeptides, differentiation of non-natural monosaccharide types on the glycans and also their incorporation efficiency through quantitation. Through this we could deduce some interconversion of monosaccharides during their processing through glycan salvage pathway and subsequent incorporation into glycan chains. The study of glycosylation dynamics through this method can be conducted in high throughput as few sample processing steps are involved, enabling understanding of glycosylation dynamics under various external stimuli and thereby could bolster the use of metabolic glycan engineering in glycosylation functional studies.

biochemistry↗