Mapping Researchers, Methodologies, and Institutes in a Knowledge Graph

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Here I showcase implementation of a knowledge graph that aggregates and structures scientific studies around sequencing technologies, methodologies, researchers, and institutes, enabling relationship-driven analysis that goes beyond traditional tabular or bibliometric approaches.

The graph integrates studies covering major sequencing modalities, including RNA-seq, scRNA-seq, WGS, WES, and ATAC-seq, and explicitly models how methods, platforms, institutions, and geographies intersect.
While implemented using graph-native technology, the conceptual model is portable to relational or hybrid SQL–graph architectures.

Core Database Structure

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The schema is designed to capture who uses what, where, and how, supporting queries such as:

• Which institutes are driving adoption of emerging sequencing methods?
• How platform usage differs across countries or research domains
• How specific methodologies diffuse over time across the research landscape

Visualising Query Capabilities

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The graph enables flexible traversal across entities, allowing rapid exploration of:

• studies linked to specific journals or methodologies
• researchers grouped by geography or institutional affiliation

📽️ Additional Media

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Short screen recordings demonstrating interactive graph exploration are available here and here.

Querying Platforms, Techniques, and Methodologies

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The plots below illustrate how the graph can be queried to surface technology prevalence and methodological patterns across the literature.

Sequencing Platform Mentions

Single-read vs Paired-end & WGS vs WES (Example: Germany)

Single-cell and Spatial Transcriptomics