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Using average nucleotide identity (ANI) to evaluate microsporidia species boundaries based on their genetic relatedness.

Nathalia R M de AlbuquerqueKaren Luisa Haag
Published in: The Journal of eukaryotic microbiology (2022)
Microsporidia are obligatory intracellular parasites related to fungi and since their discovery their classification and origin has been controversial due to their unique morphology. Early taxonomic studies of microsporidia were based on ultrastructural spore features, characteristics of their life cycle and transmission modes. However, taxonomy and phylogeny based solely on these characteristics can be misleading. SSU rRNA is a traditional marker used in taxonomical classifications, but the power of SSU rRNA to resolve phylogenetic relationships between microsporidia is considered weak at the species level, as it may not show enough variation to distinguish closely related species. Overall genome relatedness indices (OGRI), such as average nucleotide identity (ANI), allows fast and easy-to-implement comparative measurements between genomes to assess species boundaries in prokaryotes, with a 95% cutoff value for grouping genomes of the same species. Due to the increasing availability of complete genomes, metrics of genome relatedness have been applied for eukaryotic microbes taxonomy such as microsporidia. However, the distribution of ANI values and cutoff values for species delimitation have not yet been fully tested in microsporidia. In this study we examined the distribution of ANI values for 65 publicly available microsporidian genomes and tested whether the 95% cutoff value is a good estimation for circumscribing species based on their genetic relatedness.
Keyphrases
  • genome wide
  • genetic diversity
  • machine learning
  • deep learning