Abstract
BACKGROUND: Ortholog prediction, essential for various genomic research areas, faces growing inconsistencies amidst the expanding array of ortholog databases. The common strategy of computing consensus orthologs introduces additional arbitrariness, emphasizing the need to examine the causes of such inconsistencies and identify proteins susceptible to prediction errors. RESULTS: We introduce the Signal Jaccard Index (SJI), a novel metric rooted in unsupervised genome context clustering, designed to assess protein similarity. Leveraging SJI, we construct a protein network and reveal that peripheral proteins within the network are the primary contributors to inconsistencies in orthology predictions. Furthermore, we show that a protein's degree centrality in the network serves as a strong predictor of its reliability in consensus sets. CONCLUSIONS: We present an objective, unsupervised SJI-based network encompassing all proteins, in which its topological features elucidate ortholog prediction inconsistencies. The degree centrality (DC) effectively identifies error-prone orthology assignments without relying on arbitrary parameters. Notably, DC is stable, unaffected by species selection, and well-suited for ortholog benchmarking. This approach transcends the limitations of universal thresholds, offering a robust and quantitative framework to explore protein evolution and functional relationships.