Orthology detection combining clustering and synteny for very large datasets.

摘要: The elucidation of orthology relationships is an important step both in gene function prediction as well towards understanding patterns sequence evolution. Orthology assignments are usually derived directly from similarities for large data because more exact approaches exhibit too high computational costs. Here we present PoFF, extension the standalone tool Proteinortho, which enhances detection by combining clustering, similarity, and synteny. In course this work, FFAdj-MCS, a heuristic that assesses pairwise order using adjacencies (a similarity measure related to breakpoint distance) was adapted support multiple linear chromosomes extended detect duplicated regions. PoFF largely reduces number false positives enables fine-grained predictions than purely similarity-based approaches. maintains low memory requirements efficient concurrency options its basis making software applicable very datasets.