GeneWeaver

GeneWeaver integrates curated experimental results of gene and gene-product associations from high-throughput technologies such as GWAS, QTL analyses, microarray experiments, RNA-sequencing, and mutant phenotyping to enable discovery of gene–function relationships across species, conditions, behaviors, and biological processes.

Key Features:

• Curated repository: Aggregates published experimental results related to gene and gene-product associations derived from GWAS, QTL analyses, microarray experiments, RNA-sequencing, and mutant phenotyping.
• Dynamic dataset integration: Integrates disparate genomic datasets to enable cross-study and cross-species comparison of gene associations.
• Identifier unification and automation: Automates and streamlines integration tasks including unifying gene identifiers across datasets.
• Ontological Discovery Environment: Uses the Ontological Discovery Environment to infer biological relationships and derive empirical frameworks from aggregated data.
• Data organization by inferred relationships: Organizes large volumes of independently published genomic data into new conceptual frameworks based on inferred biological relationships rather than pre-existing semantic structures.
• Facilitates exploration of gene-function associations: Supports investigation of complex biological questions concerning gene–function relationships across species, conditions, behaviors, and processes.

Scientific Applications:

• Cross-species gene-function discovery: Identifying conserved and divergent gene associations across multiple species and biological contexts.
• Integrative analysis of diverse genomic studies: Combining GWAS, QTL, microarray, RNA-sequencing, and mutant phenotyping results for meta-analysis of gene associations.
• Empirical ontology construction: Inferring ontological structures of biological functions from aggregated experimental evidence.
• Consolidation of published genomic experiments: Aggregating and re-framing independently published datasets to reveal novel gene–function relationships.

Methodology:

Aggregates curated experimental gene- and gene-product association sets from GWAS, QTL, microarray, RNA-sequencing, and mutant phenotyping; dynamically integrates datasets, unifies gene identifiers, and applies the Ontological Discovery Environment to infer biological relationships and organize data into an empirical ontology.