This repository contains the code and metadata needed to build a Knowledge Graph (KG) for NASA GeneLab omics datasets hosted on the Open Science Data Repository (OSDR).
- Automated graph construction from datasets in the OSDR
- Incremental update for new datasets
- Statistical filtering of results for significance
- Species selection via a configurable whitelist
- Versioned metadata for reproducibility
- Natural language queries using MCP-GeneLab
| Measurement | Technology | Property | Selection Criteria |
|---|---|---|---|
| Transcription profiling | RNA Sequencing (RNA‑Seq) | Log2 fold change (expression) | Adjusted p-value <= 0.1 |
| Transcription profiling | DNA microarray | Log2 fold change (expression) | Adjusted p-value <= 0.1 |
| DNA methylation profiling | Whole Genome Bisulfite Sequencing | Methylation difference % | q-value <= 0.1 |
| DNA methylation profiling | Reduced‑Representation Bisulfite Sequencing (RRBS) | Methylation difference % | q-value <= 0.1 |
| Amplicon Sequencing | 16S, 18S, ITS, 16S and ITS | Log2 fold change (abundance) | Adjusted p-value <= 0.1 |
| Amplicon Sequencing | 16S, 18S, ITS, 16S and ITS | Ln fold change (abundance) | q-value <= 0.1 |
- Fetch omics study records using the OSDR API
- Filter datasets by statistical thresholds and target species
- Map model organism genes to human genes
- Map cell and tissue types to the Cell (CL) and Uber Anatomy Ontology (UBERON) ontology, respectively
- Export CSV files for graph database upload
- Import CSV files into a Neo4j Graph database
The following node and relationship metadata files define the graph schema.
-
Nodes
https://github.com/BaranziniLab/spoke_genelab/blob/main/kg/v0.3.0/metadata/nodes/ -
Relationships
https://github.com/BaranziniLab/spoke_genelab/blob/main/kg/v0.3.0/metadata/relationships/
The organization and conventions for defining the metadata and data are described in the kg-import Git repository.
classDiagram
direction TB
class Mission {
STRING identifier
STRING name
STRING flight_program
STRING space_program
DATE start_date
DATE end_date
}
class Study {
STRING identifier
STRING name
STRING description
STRING project_title
STRING project_type
STRING organism
STRING taxonomy
STRING host_organism
STRING host_strain
STRING host_taxonomy
}
class Assay {
STRING identifier
STRING name
STRING measurement
STRING technology
STRING differential_analysis_method
STRING factor_space_1
STRING factor_space_2
LIST factors_1
LIST factors_2
STRING material_1
STRING material_2
STRING material_id_1
STRING material_id_2
STRING material_name_1
STRING material_name_2
}
class MGene {
STRING identifier
STRING name
STRING symbol
STRING organism
STRING taxonomy
}
class Gene {
STRING identifier
}
class MethylationRegion {
STRING identifier
STRING name
STRING chromosome
INTEGER start
INTEGER end
BOOLEAN in_exon
BOOLEAN in_intron
BOOLEAN in_promoter
INTEGER dist_to_feature
}
class Anatomy {
STRING identifier
}
class CellType {
STRING identifier
}
class Organism {
STRING identifier
STRING name
}
class MEASURED_DIFFERENTIAL_EXPRESSION_ASmMG {
float log2fc
float adj_p_value
float group_mean_1
float group_mean_2
float group_stdev_1
float group_stdev_2
}
class MEASURED_DIFFERENTIAL_METHYLATION_ASmMR {
float methylation_diff
float q_value
float group_mean_1
float group_mean_2
float group_stdev_1
float group_stdev_2
}
class MEASURED_DIFFERENTIAL_ABUNDANCE_ASmO {
float log2fc
float lnfc
float adj_p_value
float q_value
float group_mean_1
float group_mean_2
float group_stdev_1
float group_stdev_2
}
Mission --> Study : CONDUCTED_MIcS
Study --> Assay : PERFORMED_SpAS
Assay --> MEASURED_DIFFERENTIAL_EXPRESSION_ASmMG
MEASURED_DIFFERENTIAL_EXPRESSION_ASmMG --> MGene
Assay --> MEASURED_DIFFERENTIAL_METHYLATION_ASmMR
MEASURED_DIFFERENTIAL_METHYLATION_ASmMR --> MethylationRegion
Assay --> MEASURED_DIFFERENTIAL_ABUNDANCE_ASmO
MEASURED_DIFFERENTIAL_ABUNDANCE_ASmO --> Organism
Assay --> Anatomy : INVESTIGATED_ASiA
Assay --> CellType : INVESTIGATED_ASiCT
MGene --> MethylationRegion : METHYLATED_IN_MGmMR
MGene --> Gene : IS_ORTHOLOG_MGiG
In Claude Desktop, add the following to the claude_desktop_config.json file to connect to a local Neo4j instance.
"spoke-genelab-local": {
"command": "uvx",
"args": ["mcp-genelab"],
"env": {
"NEO4J_URI": "bolt://localhost:7687",
"NEO4J_USERNAME": "neo4j",
"NEO4J_PASSWORD": "neo4jdemo",
"NEO4J_DATABASE": "spoke-genelab-v0.3.0",
"INSTRUCTIONS": "Query the GeneLab KG to identify NASA spaceflight experiments containing omics datasets, specifically differential gene expression (transcriptomics), DNA methylation (epigenomics) and Amplicon (metagenomics) data."
}
},
Figure: Integration of the SPOKE and GeneLab knowledge graphs using proxy nodes.
The GeneLab graph (right), a knowledge graph representing spaceflight omics datasets, depicts key experimental entities: Assay, Study, Mission, MGene, and MethylationRegion, along with their relationships.
Proxy nodes (gray) represent external identifiers (ENTREZ, UBERON, CL) and enable linkage to the SPOKE graph (left), a rich biomedical knowledge graph comprising biological processes, molecular functions, diseases, compounds, and more. The dashed lines indicate mappings to enable the construction of a composite Neo4j graph database. The composite graph enables federated queries across multiple KGs.
PW Rose, CA Nelson, SG Gebre, AM Saravia-Butler, K Soman, KA Grigorev, LM Sanders, SV Costes, SE Baranzini, NASA SPOKE-GeneLab Knowledge Graph. Available online: https://github.com/BaranziniLab/spoke_genelab (2025)
CA Nelson, PW Rose, K Soman, LM Sanders, SG Gebre, SV Costes, SE Baranzini, Nasa Genelab-Knowledge Graph Fabric Enables Deep Biomedical Analysis of Multi-Omics Datasets, https://ntrs.nasa.gov/citations/20250000723 (2025)
NSF Award number 2333819, Proto-OKN Theme 1: Connecting Biomedical information on Earth and in Space via the SPOKE knowledge graph.