Gene Ontology Rules
github.com/geneontology/go-siteGO Rules are a way of documenting the set of filters and reports that should apply to GAF annotation data. Some rules are expressed as SPARQL on a triplestore, some are code in the GAF parsing software, ontobio.
Sourced from
- Bioregistry — go.rule
- GitHub — github.com/geneontology/go-site
Related resources
An extension of Schema.org to annotate metadata on software projects
An issue on the UBERON GitHub Issue tracker
Famat is made to collect data about lists of genes and metabolites provided by user, and to visualize it through a Shiny app. Information collected is: - Pathways containing some of the user's genes and metabolites (obtained using a pathway enrichment analysis). - Direct interactions between user's elements inside pathways. - Information about elements (their identifiers and descriptions). - Go terms enrichment analysis performed on user's genes. The Shiny app is composed of: - information about genes, metabolites, and direct interactions between them inside pathways. - an heatmap showing which elements from the list are in pathways (pathways are structured in hierarchies). - hierarchies of enriched go terms using Molecular Function and Biological Process.
Protein-protein interaction data is essential for omics data analysis and modeling. Database knowledge is general, not specific for cell type, physiological condition or any other context determining which connections are functional and contribute to the signaling. Functional annotations such as Gene Ontology and Human Phenotype Ontology might help to evaluate the relevance of interactions. This package predicts functional relevance of protein-protein interactions based on functional annotations such as Human Protein Ontology and Gene Ontology, and prioritizes genes based on network topology, functional scores and a path search algorithm.
Standalone browser-based Gene Ontology network viewer for exploring, filtering, searching, and exporting GO term and gene annotation neighborhoods from locally preprocessed GO OBO and GAF data.
An Evolutionary-scale Model (ESM) for protein function prediction from amino acid sequences using the Gene Ontology (GO). Based on the ESM2 Transformer architecture, pre-trained on UniRef50, and fine-tuned on the AmiGO dataset, this model predicts the GO subgraph for a particular protein sequence -…