through Modular Queries and Knowledge Graph Construction

Abstract

In biological research, integrating experimental data with publicly available resources is pivotal for understanding complex biological mechanisms. However, this process is often intricate and timeconsuming due to the complexity and diversity of data. Furthermore, the lack of consistent harmonization across diferent data types complicates the management of disparate data formats and sources. Addressing this, we introduce BioDataFuse, a query-based Python tool for seamless integration of biomedical data resources. BioDataFuse establishes a modular framework for eficient data wrangling, enabling contextspecific knowledge graph creation and supporting graph-based analyses. With a user-friendly interface, it enables users to dynamically create knowledge graphs from their input experimental data. Supported by a robust Python package, pyBiodatafuse, this tool excels in data harmonization, aggregating diverse sources through modular queries. Moreover, BioDataFuse provides plugin capabilities for Cytoscape and Neo4j, allowing local graph hosting. Ongoing refinements enhance the graph utility through tasks like link prediction, making BioDataFuse a versatile solution for eficient and efective biological data integration.

Keywords

• Knowledge

1. Introduction

BioDataFuse is an innovative solution facilitating the seamless integration of diverse data resources on the fly. It establishes a modular framework, adhering to the “what-you-see-is-whatyou-get (WYSIWYG)” principle, granting users control over the graph creation. The adaptable backend and user-friendly interface ofer a solution to address data integration challenges. The backend, implemented through the Python package pyBiodatafuse (GitHub repo), supports various tool functionalities. Concurrently, the front-end interface improves accessibility for non-programmers. The BioDataFuse aims to develop into a comprehensive toolkit that enables users to seamlessly explore, interpret, and visualize biomedical data across a diverse range of resources, fostering interoperability and facilitating efortless navigation for extraction of meaningful insights from context-specific biomedical graphs.

2. BioDataFuse framework

The BioDataFuse framework is structured around five primary components:

1. Data harmonizer ensures unique persistent identifiers for diverse biomedical modalities using BridgeDb framework [ 1 ]. This component supports various input data, including genes, metabolites, and tables from diferential expression analysis.

2. Data annotators empower modular queries across diverse resources through SPARQL Protocol And RDF Query Language (SPARQL). Annotators, including Wikidata [ 2 ], Bgee [ 3 ], Molecules on Membranes Database (MolMeDB) [ 4 ], Open Targets [ 5 ], DisGeNET [ 6 ], WikiPathways [ 7 ], and STRING [ 8 ], enrich gene-related metadata, gene-disease relationships, expression profiles, pathway information, and protein-protein interactions.

3. Graph generator creates knowledge graphs from annotated data using NetworkX [ 9 ], ofering a clear and structured representation. The Python module in pyBiodatafuse constructs graphs exportable to Cytoscape [ 10 ] and Neo4j [11].

4. Graph analyzer employs Python packages like Matplotlib [12], Seaborn [13], and Plotly for basic plots and network-specific summaries, facilitating a quick understanding of the data and its interconnections.

5. User interface is developed using Streamlit, enabling users to input gene lists, select identifier types, and annotate data from chosen sources.

3. Future work

Planned future directions for BioDataFuse include continued attention to the graph analyzer component, emphasizing the importance of refining and expanding its capabilities. Additionally, the future work includes supporting additional input data types, integrating annotators from drug databases and larger repositories, and ensuring the continuous updating of pyBiodatafuse at PyPi. Exploration of migrating the user interface to frameworks like Shiny or Dash is underway, aiming to improve functionality and provide an enhanced user experience. These eforts align with our dedication to advancing BioDataFuse for improved data interoperability.

4. Acknowledgments

We acknowledge the support from ELIXIR BioHackthon 2023, resulting in a preprint [14]. els of biomolecular interaction networks, Genome Research 13 (2003) 2498–2504. doi:10.1101/gr.1239303. [11] Neo4j, Neo4j - the world’s leading graph database, 2012. URL: http://neo4j.org/. [12] J. D. Hunter, Matplotlib: A 2d graphics environment, Computing in Science & Engineering 9 (2007) 90–95. doi:10.1109/MCSE.2007.55. [13] M. L. Waskom, seaborn: statistical data visualization, Journal of Open Source Software 6 (2021) 3021. doi:10.21105/joss.03021. [14] Y. Gadiya, A. Ammar, E. Willighagen, D. Martinat, A. C. Sima, H. Balci, T. Abbassi-Daloii, Biohackeu23 report: Extending interoperability of experimental data using modular queries across biomedical resources, 2023. doi:10.37044/osf.io/mhsqp.

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