LSGFA: domain-based infraspecific large-scale prokaryotic genomic orthologous gene inference

BACKGROUND: Orthologous gene inference is a crucial technical challenge in evolutionary biology. It typically depends on sequence similarity searches and employs a graph clustering method to infer homologous gene families. However, the all-vs-all sequence similarity search is time-consuming for large-scale genome datasets. In this work, we present LSGFA, a method that detects subgraphs based on the similarity of protein domain architectures and then performs graph clustering within each subgraph, corresponding to sequences that share similar compositions of protein domains. RESULTS: LSGFA carries out four steps in the analysis workflow: protein domain annotation, initial clustering based on Pfam domain architecture, SSN-based clustering, and detection of pan-genomic patterns. Benchmarking against five state-of-the-art tools (OrthoFinder, Roary, PanTA, Panaroo, and PGAP2) across multiple datasets demonstrates that LSGFA achieves a balanced trade-off between computational efficiency and biological accuracy. It takes less time than OrthoFinder while identifying more core genes than high-speed heuristic tools, and its orthogroup inference results show strong consistency with OrthoFinder. CONCLUSIONS: Due to the high proportion of proteins with known domain architectures in prokaryotes, LSGFA is particularly well-suited for prokaryotic genomes, where it significantly reduces computational time while yielding accurate homologous gene inference.