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High-throughput ANI Analysis of 90K Prokaryotic Genomes Reveals Clear Species Boundaries

View ORCID ProfileChirag Jain, Luis M. Rodriguez-R, Adam M. Phillippy, Konstantinos T. Konstantinidis, Srinivas Aluru
doi: https://doi.org/10.1101/225342
Chirag Jain
aSchool of Computational Science and Engineering, Georgia Institute of Technology, Atlanta GA 30332
dNational Human Genome Research Institute, National Institutes of Health, Bethesda MD 20894
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  • ORCID record for Chirag Jain
Luis M. Rodriguez-R
bSchool of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta GA 30332
cSchool of Biological Sciences, Georgia Institute of Technology, Atlanta GA 30332
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Adam M. Phillippy
dNational Human Genome Research Institute, National Institutes of Health, Bethesda MD 20894
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Konstantinos T. Konstantinidis
bSchool of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta GA 30332
cSchool of Biological Sciences, Georgia Institute of Technology, Atlanta GA 30332
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  • For correspondence: kostas.konstantinidis@gatech.edu aluru@cc.gatech.edu
Srinivas Aluru
aSchool of Computational Science and Engineering, Georgia Institute of Technology, Atlanta GA 30332
eInstitute for Data Engineering and Science, Georgia Institute of Technology, Atlanta GA 30332
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  • For correspondence: kostas.konstantinidis@gatech.edu aluru@cc.gatech.edu
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Abstract

A fundamental question in microbiology is whether there is a continuum of genetic diversity among genomes or clear species boundaries prevail instead. Answering this question requires robust measurement of whole-genome relatedness among thousands of genomes and from diverge phylogenetic lineages. Whole-genome similarity metrics such as Average Nucleotide Identity (ANI) can provide the resolution needed for this task, overcoming several limitations of traditional techniques used for the same purposes. Although the number of genomes currently available may be adequate, the associated bioinformatics tools for analysis are lagging behind these developments and cannot scale to large datasets. Here, we present a new method, FastANI, to compute ANI using alignment-free approximate sequence mapping. Our analyses demonstrate that FastANI produces an accurate ANI estimate and is up to three orders of magnitude faster when compared to an alignment (e.g., BLAST)-based approach. We leverage FastANI to compute pairwise ANI values among all prokaryotic genomes available in the NCBI database. Our results reveal a clear genetic discontinuity among the database genomes, with 99.8% of the total 8 billion genome pairs analyzed showing either >95% intra-species ANI or <83% inter-species ANI values. We further show that this discontinuity is recovered with or without the most frequently represented species in the database and is robust to historic additions in the public genome databases. Therefore, 95% ANI represents an accurate threshold for demarcating almost all currently named prokaryotic species, and wide species boundaries may exist for prokaryotes.

Footnotes

  • This manuscript was compiled on November 27, 2017

  • K.T.K. conceived the study; C.J., A.M.P, and S.A. designed method; C.J., and L.M.R. analyzed data; and C.J., L.M.R., K.T.K., and S.A. wrote the paper.

  • The authors declare no conflict of interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted November 27, 2017.
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High-throughput ANI Analysis of 90K Prokaryotic Genomes Reveals Clear Species Boundaries
Chirag Jain, Luis M. Rodriguez-R, Adam M. Phillippy, Konstantinos T. Konstantinidis, Srinivas Aluru
bioRxiv 225342; doi: https://doi.org/10.1101/225342
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High-throughput ANI Analysis of 90K Prokaryotic Genomes Reveals Clear Species Boundaries
Chirag Jain, Luis M. Rodriguez-R, Adam M. Phillippy, Konstantinos T. Konstantinidis, Srinivas Aluru
bioRxiv 225342; doi: https://doi.org/10.1101/225342

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