In general, AtGaP has developed various query modes by integrating diverse information from 23 different public databases (including transcriptional regulation between genes, protein-protein interactions, GWAS study outcomes, gene mutation experiments, etc.) into a comprehensive knowledge graph. Unlike traditional tabular data, AtGaP introduces a graph query mode that leverages the flexibility of the knowledge graph. Users can customize queries for different entities, relationships, and attributes, such as "genes related to root length with associated protein-protein interaction networks." Furthermore, AtGaP provides a user-friendly gene information retrieval page, assisting researchers in quickly accessing existing research outcomes for specific genes. Additionally, AtGaP employs the neo4j system for knowledge graph construction, which offers flexibility in allowing different field names for node types and facilitates convenient updates for AtGaP.
The AtGaP database shows potential in becoming a valuable resource for the Arabidopsis research community to swiftly access information. It efficiently provides researchers with images depicting research outcomes for individual genes, descriptions from various literature sources, and expression levels across different tissues. In comparison to traditional databases like Tair and NCBI, AtGaP offers a more intuitive interface. However, despite these advantages, AtGaP is limited by the scope of available information. The current version of the database has yet to undergo extensive validation by users, necessitating continuous updates and maintenance to meet user needs and ensure synchronization with ongoing research developments.
Simultaneously, with the advancement of artificial intelligence and deep learning, the approach of packaging diverse information from a specific field into a knowledge graph and applying natural language models for question-answering training or embedding community relationship information as supplementary attributes for downstream analysis is becoming increasingly popular. As a knowledge graph rich in diverse gene, phenotype, and functional multimodal data, AtGaP will provide a high-quality and comprehensive dataset for large-scale machine learning methods, further propelling research advancements in the field.
