GOcats

GOcats refines Gene Ontology-based gene-annotation enrichment by organizing GO into semantically scoped subgraphs and reinterpreting ontological relations to improve term mapping and enrichment accuracy.

Key Features:

• Semantic Linkage Optimization: Organizes the Gene Ontology into user-defined subgraphs and enforces congruence with scoping semantics to prevent erroneous term mappings.
• Improved Path Traversal Method: Reinterprets and retains edges in the ontological graph that conventional ancestor path-tracing omits, increasing statistical power and accuracy of enrichment analyses.
• Application to Diverse Datasets: Improved GO term enrichment in a breast cancer microarray dataset, enhancing 182 of 217 significantly enriched GO terms (one-sided binomial test p = 1.86E-25) and revealing additional experimentally validated enriched terms.
• Versatility Across Biological Contexts: Demonstrated significant improvements in GO term enrichment for horse cartilage development RNAseq datasets (one-sided binomial test p-values 1.32E-03 to 2.58E-44).

Scientific Applications:

• Gene-expression studies: Enhances annotation enrichment for microarray and RNAseq gene-expression studies to support more accurate biological interpretation and discovery.
• Functional interpretation and interaction analysis: Facilitates deeper understanding of gene functions and interactions within complex biological systems by enforcing semantic scoping in ontology relations.

Methodology:

Organizes GO into user-defined subgraphs, enforces scoping semantics on relations, and reinterprets edges in the ontological graph that conventional ancestor path-tracing omits.