GoGene

GoGene aggregates literature-derived gene–term associations via high-throughput text mining to enrich gene annotation and support interpretation of high-throughput genomic experiments.

Key Features:

• High-Throughput Text Mining: Extracts co-occurrences of genes and ontology terms from a corpus of over 18 million PubMed entries.
• Comprehensive Gene Associations: Contains more than 4 million associations across ten model organisms spanning traditional GeneOntology categories (process, function, location) and biomedical categories such as diseases, compounds, techniques, and mutations.
• Evidence-Based Associations: Links each gene–term association to supporting literature evidence for transparency and verifiability.
• Search Modalities: Supports searches by keywords, gene lists, gene sequences, and protein sequences, and enables queries against PubMed, EntrezGene, and via BLAST.
• Ranking by Novelty and Importance: Integrates diverse gene-related information to rank genes according to novelty and scientific importance for prioritization.
• Complementation of Manual Annotations: Augments high-quality manual annotations with additional literature-derived associations to broaden annotation coverage.

Scientific Applications:

• High-Throughput Genomic Studies: Enriches annotations for microarray and RNAi screens to aid biological interpretation of large gene lists.
• Clustering and Exploration: Provides expanded annotation categories to facilitate clustering and exploration of genes identified in large-scale experiments.
• Translational Research: Supports investigation of gene–disease and gene–compound associations relevant to translational studies.
• Gene Prioritization: Enables prioritization of candidate genes from large datasets using literature-supported novelty and importance rankings.

Methodology:

Systematically analyzes over 18 million PubMed entries using high-throughput text mining to identify co-occurrences between genes and ontology terms and generate literature-backed gene–term associations.