KRAKEN: A provenance-tracked knowledge graph for multiomic and wellness research
- 1Phenome Health
Abstract
Existing general-purpose biomedical knowledge graphs tend to focus on disease mechanisms and drug repurposing, leaving multiomic and wellness-relevant content underrepresented. KRAKEN (Knowledge Research & Analysis Kit for Evidence Networks) addresses this gap by integrating existing graphs (including Translator KG Open, RTX-KG2, and ROBOKOP) with specialized sources such as RefMet, LIPID MAPS, NIH Common Data Elements, Polygenic Score Catalog, and derived wellness measures including biological age and biological BMI. The resulting graph spans ~26M nodes and ~121M edges across 62 entity types. KRAKEN adopts the Biolink Model as its semantic layer, ensuring compatibility with standardized resources emerging from the NIH NCATS Biomedical Data Translator program. A lightweight, modular build system rebuilds the full graph (including entity resolution), with peak memory consumption under 48 GB, and supports flexible inclusion or exclusion of sources, allowing the user to scope the graph to a domain of interest. Built-in analytical tools include multi-hop reasoning, subgraph extraction, text, vector and hybrid entity search, and enrichment analyses, all accessible through an interactive web interface, a REST API, and a Model Context Protocol server, the last enabling direct consumption by agentic and LLM-based systems. KRAKEN is freely available at https://app.krakenkg.com.
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BibTeX
@misc{glen2026kraken,
title = {{KRAKEN: A provenance-tracked knowledge graph for multiomic and wellness research}},
author = {A. K. Glen and D. Witherington and T. Leslie and A. Baumgartner and A. Fernando and B. Vemuri and O. Nahman and G. Glusman and L. Hood and L. Pflieger and N. Rappaport},
howpublished = {bioRxiv},
year = {2026},
month = aug,
doi = {10.64898/2026.08.18.745544},
url = {https://www.biorxiv.org/content/10.64898/2026.08.18.745544v2},
note = {CC BY},
}
RIS
TY - UNPB
TI - KRAKEN: A provenance-tracked knowledge graph for multiomic and wellness research
AU - A. K. Glen
AU - D. Witherington
AU - T. Leslie
AU - A. Baumgartner
AU - A. Fernando
AU - B. Vemuri
AU - O. Nahman
AU - G. Glusman
AU - L. Hood
AU - L. Pflieger
AU - N. Rappaport
PY - 2026
DA - 2026/08/18
PB - bioRxiv
DO - 10.64898/2026.08.18.745544
UR - https://www.biorxiv.org/content/10.64898/2026.08.18.745544v2
AB - Existing general-purpose biomedical knowledge graphs tend to focus on disease mechanisms and drug repurposing, leaving multiomic and wellness-relevant content underrepresented. KRAKEN (Knowledge Research & Analysis Kit for Evidence Networks) addresses this gap by integrating existing graphs (including Translator KG Open, RTX-KG2, and ROBOKOP) with specialized sources such as RefMet, LIPID MAPS, NIH Common Data Elements, Polygenic Score Catalog, and derived wellness measures including biological age and biological BMI. The resulting graph spans ~26M nodes and ~121M edges across 62 entity types. KRAKEN adopts the Biolink Model as its semantic layer, ensuring compatibility with standardized resources emerging from the NIH NCATS Biomedical Data Translator program. A lightweight, modular build system rebuilds the full graph (including entity resolution), with peak memory consumption under 48 GB, and supports flexible inclusion or exclusion of sources, allowing the user to scope the graph to a domain of interest. Built-in analytical tools include multi-hop reasoning, subgraph extraction, text, vector and hybrid entity search, and enrichment analyses, all accessible through an interactive web interface, a REST API, and a Model Context Protocol server, the last enabling direct consumption by agentic and LLM-based systems. KRAKEN is freely available at https://app.krakenkg.com.
ER -