Evaluating completeness, coherence, and consistency of genome-scale function annotations.

Protein function annotation traditionally follows a reductionist approach, assigning functions to individual proteins acting in isolation. This treats each annotation as an independent fact, disconnected from the broader biological system. However, proteins operate within integrated networks where their functions depend on genomic context and interacting partners. This needs to be reflected in function annotation and evaluation frameworks. We assess whether annotated protein functions could plausibly coexist within a living organism. To achieve this goal, we formalize three criteria grounded in systems biology principles: completeness (presence of essential functions), coherence (satisfaction of functional dependencies), and consistency (absence of mutually exclusive functions). We applied this framework to manually curated function annotations from six model organisms and computational function predictions from seven methods. While model organism annotations largely satisfied our constraints, computational function prediction methods systematically failed to produce biologically plausible genome-scale annotations. Our review reveals a measurable gap between the per-protein objectives of current annotation methods and the system-level criteria that an annotation set must satisfy to describe a viable organism. Our evaluation framework grounded in systems biology principles provides quantitative metrics for evaluating biological plausibility and establishes a foundation for developing system-aware annotation approaches. Augmenting protein-level annotation with system-level criteria offers a tractable path to improving annotation of the rapidly growing collection of sequenced genomes and metagenomes.