Enhancing Genomics Annotation with GraphRAG

Introduction

The intersection of generative AI and genomics is rapidly reshaping how researchers understand and annotate complex biological data. Among the emerging techniques, GraphRAG stands out by integrating structured knowledge graphs with large language models to enhance contextual reasoning and data retrieval. In genomics annotation, where relationships between genes, proteins, and phenotypes are intricate and deeply interlinked, GraphRAG offers a novel approach to navigate this complexity with greater precision and interpretability. This blog is a follow up study of our previous paper and explores how GraphRAG can be leveraged to accelerate and improve the annotation of genomic sequences.

Figure 1. Proposed approach

Please find the GraphRAG quick start Jupyter notebook from this link.

You also can find the sample Jupyter notebook for reproducing the content of this blog from this link.

Sample ClinVAR variant record: 

chr1:1523548,na,ATAD3A,not_provided,
"GRCh38_chr:chr1                   GRCh38_pos:1523548           reference_allele:T                   alternative_allele:C                 
dbSNP_ID:na                   Variation_ID:1704755             Allele_ID:1699287                   canonical_SPDI:NC_000001.11:g.1523548T>C             molecular_consequence:SO:0001583|missense_variant                  
 germline_review:Uncertain_significance                  
germline_status:criteria_provided,_single_submitter                  
Gene:ATAD3A          
Condition:not_provided                   
source:clinvar clinvar_URL:https://www.ncbi.nlm.nih.gov/clinvar/variation/1704755/"

Compute environment:

Azure ML Studio VM: Standard_DS15_v2 (20 cores, 140 GB RAM, 280 GB disk)

GraphRAG indexing time: 

72 milliseconds per variant record

Query method: local

GraphRAG supports 4 different methods for more information please visit: Overview - GraphRAG

Model Information:

IMPORTANT: Please update your 'settings.yaml' file on your GraphRAG with your Azure OpenAI Service REST API information.

default_embedding_model:

    type: azure_openai_embedding

    api_base: https://XXX.openai.azure.com

    api_version: 2025-01-01-preview

    auth_type: azure_managed_identity

    model: text-embedding-3-small

    deployment_name: text-embedding-3-small

Indexing command: 

!graphrag index --root ."/genomicsragtest" 

Sample indexing process:

Figure 2. Sample screen of data indexing with GraphRAG

Results

In this blog, we indexed all variants from ClinVAR vcf file with GraphRAG. Here are sample query results from 'Baseline RAG (GPT-4o, from our previous study)' vs 'GraphRAG':

Sample query:

!graphrag query --root ./genomicsragtest --method local --query "Annotate chr1:5863337"

Table 1. Comparison of Baseline RAG and GraphRAG responses

A comparison table between baseline RAG and GraphRAG highlights that GraphRAG produces more structured outputs and is highly sensitive to query phrasing, enabling more precise and context-aware responses. (Table 1)

Visualizing and Debugging Your Knowledge Graph

Figure 3. High Level representation of GraphRAG

The GraphRAG developer team recommends using Gephi for intuitive and scalable visualization of the resulting knowledge graphs. Please review the step-by-step guide walks through the process to visualize a knowledge graph after it's been constructed by GraphRAG. 

Conclusion
As the volume and complexity of genomic data continue to grow, traditional annotation pipelines face limitations in scalability and contextual understanding. GraphRAG presents a compelling solution, bridging the structured world of biological ontologies with the flexible reasoning capabilities of AI models. By harnessing graph-based retrieval, it enhances the relevance and accuracy of annotations, opening doors to deeper insights and faster discoveries. The future of genomics may well lie in this symbiotic relationship between knowledge graphs and AI models. Researchers can transform bioinformatics tools from data-heavy to insight-rich applications.

Acknowledgments

Special thanks to Jesus Aguilar for initiating this work and setting the foundation. I also want to thank Jonathan Larson, who not only provided valuable feedback but also served as a GraphRAG project lead, guiding the direction of this effort.

Notices

This blog is for research and informational purposes only. It is not intended for clinical use. Please note that AI-generated outputs may contain inaccuracies or misleading information.