GOALS: Gene Ontology Analysis with Layered Shells for Enhanced Functional Insight and Visualization

Gene Ontologies (GOs) are standardized descriptions of gene functions in terms of biological processes, molecular functions, and cellular components, capturing their Parent-Child relationships in a structured framework and advancing cancer biological modeling to provide consistent and meaningful insights into functional genomics analysis. The conventional GO hierarchical structure is defined by human curation experts, with levels determined by the shortest path to the root term. However, grouping GOs poses challenges due to the uneven distribution of gene members within GO terms and inconsistencies in the level of detail across terms at the same GO level.

In this work, we introduce Gene Ontology Analysis using Layered Shells (GOALS), a novel tool that discretizes GOAs into optimal layers. GOALS creates scalable GO layers while maintaining a balanced number of genes across GOs in each layer. Unlike existing tools, the GOALS framework organizes GO terms using a bottom-up approach based on their co-membership network, discretizing GOs to achieve an exponential fit with GO’s gene member size. Meanwhile, GOALS reveals clusters or supersets reflecting biological relevance by unsupervised clustering of GO’s latent projections.

In a case study on mouse natural killer (NK) cell development, GOALS identified distinct GO functional clusters with multi-GO layers to reveal multiple levels of detail from specific to abstract contexts to maximize signal discovery and uncover those signals’ associations with trajectory divergence. More importantly, GOALS enhances enrichment analysis by introducing additional GO stratification and latent GO map that enables more accurate classification of functional differences.

GOALS offers a robust and innovative framework for exploring disordered GO clusters, mining GO activities, and analyzing potential GO-GO interplays. By addressing critical challenges in functional genomics, GOALS provides a powerful tool for advancing our understanding of cell heterogeneity and potentially uncovering actionable insights for therapeutic development.