Closing the Gap Between GWAS Peaks and Candidate Genes: Introducing Agentic KnetMiner

If you work in plant genetics, identifying a promising GWAS peak or a significant cluster in your Differential Gene Expression (DGE) data is a great moment. But as any plant scientist knows, it’s also where the real bottleneck begins.

Translating those signals into high-confidence candidate genes requires navigating a maze of disconnected data. You have to jump between your genomic data, transcriptomics, protein-protein interaction networks, and decades of scientific literature.

For years, researchers have used KnetMiner to solve this exact problem. By building massive, interconnected "knowledge graphs," KnetMiner weaves together genes, traits, phenotypes, and literature to help scientists find hidden biological links. It is an incredibly powerful tool—if you know exactly how to formulate the right search strategy.

However, we know there are friction points. Figuring out which datasets to include, which exact keywords to use, how to filter complex results, and how to efficiently analyze thousands of relations can be highly time-consuming. Because of this learning curve, scientists sometimes miss out on the platform's most powerful hidden features.

To automate the heavy lifting and make comprehensive gene discovery accessible to everyone, we are introducing Agentic KnetMiner.

An AI-Driven Research Assistant for Post-GWAS Analysis

Instead of manually stitching data together, Agentic KnetMiner uses a multi-agent AI system designed specifically for post-GWAS and DGE analysis. You can think of it as a team of specialized computational biologists working in parallel to interpret your data within the context of the entire biological network.

Here is how the agentic system transforms the workflow, starting with our initial prototype:

• Automated Data Retrieval (Arabidopsis Prototype): Currently built specifically for Arabidopsis thaliana, the system automatically searches and extracts public trait data from AraGWAS alongside gene expression data from the EBI Expression Atlas (GXA). You no longer need to query these databases separately.
• Deep Functional Integration: The system seamlessly combines those GWAS and expression datasets with the deep functional genomics knowledge already mapped within KnetMiner. It cross-analyzes the data against known phenotypes, scientific literature, and protein interaction networks to build a complete biological picture.
• Smart Meta-Analysis: Instead of manually cross-referencing spreadsheets, the agents perform meta-analyses to identify significant and overlapping genes across these independent data sources.
• Dynamic Reasoning for Prioritization: Rather than returning a massive list of possibilities, the system uses dynamic reasoning to evaluate the evidence and prioritize a shortlist of high-confidence candidate genes.
• Interactive Knowledge Graphs: It builds a bespoke knowledge graph around your top candidates. Through our new chat interface, you can actually "talk" to your data—asking the system why a gene was prioritized, tracing the exact evidence paths, and sanity-checking its reasoning.

No Black Boxes. The Human Stays in the Loop.

In bioinformatics, trust requires transparency. That is why Agentic KnetMiner is strictly a human-in-the-loop system.

Because every output is grounded in our knowledge graph, there are no AI black boxes. The result is always tied to traceable, verifiable scientific evidence. You retain complete control over the starting parameters and final validation, while the agents streamline your workflow and surface the hidden connections you might otherwise miss.

Join the Early Testing Group

Fill in the application form here

We are currently inviting a small group of scientists to test the Arabidopsis prototype of Agentic KnetMiner.