Overall genome relatedness index (OGRI)

Gene frequency plot in pan-genome
Intergenic region

Overall genome relatedness index (OGRI)

Overall genome relatedness index (OGRI) is a term first coined by Chun & Rainey (2014) and represents any measurements indicating how similar two genome sequences are. There are many algorithms to calculate OGRI values, but the most widely used algorithm for taxonomic studies is Average Nucleotide Identity (ANI). The original ANI algorithm was first introduced and refined by Jim Teidje’s group (Konstantinidis et al. 2005; Goris et al. 2007). Since then, as more genome sequences are accumulated in public databases, ANI has been widely used in the description of novel species and, less frequently, for the identification of newly isolated strains (as genome sequencing is still expensive for routine identification).

Even though the algorithm for ANI calculation is clear, its implementation as a software can be different. Different ANI values can be obtained by different software tools as reported by Figueras et al. (2015). Actually, this is a general problem in bioinformatics; different computer programs using the same algorithm may produce different results. This is particularly problematic when web-service is used since web-service cannot be replicated with confidence in future.

OrthoANI is a modified algorithm of the original ANI and has advantages over the original ANI [Learn more about OrthoANI]. We recommend OrthoANIu (with usearch program) for taxonomic purposes. Web-service is available here and standalone software can be downloaded from here.

Last updated on Sept 16, 2017 (JC)


  1. Chun, J. & Rainey, F.A. Integrating genomics into the taxonomy and systematics of the Bacteria and Archaea. Int J Syst Evol Microbiol 64, 316-24 (2014).
  2. Konstantinidis, K.T. & Tiedje, J.M. Genomic insights that advance the species definition for prokaryotes. Proc Natl Acad Sci U S A 102, 2567-72 (2005).
  3. Goris, J. et al. DNA-DNA hybridization values and their relationship to whole-genome sequence similarities. Int J Syst Evol Microbiol 57, 81-91 (2007).
  4. Figueras, M.J., Beaz-Hidalgo, R., Hossain, M.J. & Liles, M.R. Taxonomic affiliation of new genomes should be verified using average nucleotide identity and multilocus phylogenetic analysis. Genome Announc 2(6) (2014).
  5. Lee, I., Kim, Y.O., Park, S.C. & Chun, J. OrthoANI: An improved algorithm and software for calculating average nucleotide identity. Int J Syst Evol Microbiol 66: 1100-1103 (2015).