ANI-netID: a genome similarity-network based biological identification system

Abstract

Fungal identification is based on sequence similarity comparisons, with multiple genes often utilized in conjunction, similar to most taxonomic studies. The identification process involves the individual comparison of the similarities of each gene. However, owing to insufficient information, incomplete lineage sorting, gene transfer, hybridization, gene duplication, and loss, different species may match, depending on the DNA region used for comparison. Additionally, identification methods exist that set a uniform similarity threshold value, but they hit multiple species above the threshold. In this study, we introduced ANI-netID, which was developed to address these challenges. ANI-netID is an identification method based on the phylogenetic species concept, where the threshold value is determined by the combination of individuals with the lowest nucleotide similarity within groups (clique groups), such as species and genera. This checks whether the queried individual fall into the same clique group as the reference individual with the highest similarity. ANI-netID solves not only the above-mentioned problems but also indicates that the individual being identified may represent a new lineage, if the individual does not belong to any clique group.