Enhancing gene set overrepresentation analysis with large language models
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces llm2geneset, a novel framework using large language models (LLMs) to dynamically create gene set databases for analyzing high-throughput biological data. This approach offers flexible, context-aware interpretation, matching human-curated gene set quality.
Area Of Science
- Bioinformatics
- Computational Biology
- Genomics
Background
- Traditional gene set overrepresentation analysis (ORA) relies on static, human-curated databases, limiting flexibility in interpreting high-throughput transcriptomics and proteomics data.
- Existing methods struggle to adapt to specific biological contexts or dynamically generated gene lists.
Purpose Of The Study
- To develop a flexible framework, llm2geneset, that utilizes large language models (LLMs) to dynamically generate gene set databases.
- To enable context-aware functional interpretation of biological data by integrating LLM-generated gene sets with analysis methods like ORA.
- To benchmark the performance of LLM-generated gene sets against human-curated databases.
Main Methods
- Development of the llm2geneset framework, leveraging LLMs to create gene sets based on input genes and natural language biological context.
- Integration of dynamically generated gene sets with established analysis methods, such as ORA, for functional annotation.
- Comparative analysis of LLM-generated gene sets against human-curated databases using benchmarking studies.
- Application of the framework to RNA-sequencing data from iPSC-derived microglia treated with a TREM2 agonist.
Main Results
- LLM-generated gene sets demonstrated comparable quality to human-curated gene sets.
- The llm2geneset framework successfully identified biological processes within input gene sets, outperforming traditional ORA and direct LLM prompting.
- The framework facilitated flexible, context-aware gene set generation and improved the interpretation of high-throughput biological data, as shown in the TREM2 agonist study.
Conclusions
- llm2geneset provides a powerful and flexible alternative to traditional gene set enrichment analysis, utilizing LLMs for dynamic database generation.
- The framework enhances the interpretation of complex biological datasets by offering context-specific functional annotations.
- llm2geneset represents a significant advancement in bioinformatics tools for biological data analysis and discovery.
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