FastAAI: efficient estimation of genome average amino acid identity and phylum-level relationships using tetramers of universal proteins

Estimation of whole-genome relatedness and taxonomic identification are two important bioinformatics tasks in describing environmental or clinical microbiomes. The genome-aggregate Average Nucleotide Identity is routinely used to derive the relatedness of closely related (species level) microbial and viral genomes, but it is not appropriate for more divergent genomes. Average Amino-acid Identity (AAI) can be used in the latter cases, but no current AAI implementation can efficiently compare thousands of genomes. Here we present FastAAI, a tool that estimates whole-genome pairwise relatedness using shared tetramers of universal proteins in a matter of microseconds, providing a speedup of up to 5 orders of magnitude when compared with current methods for calculating AAI or alternative whole-genome metrics. Further, FastAAI resolves distantly related genomes related at the phylum level with comparable accuracy to the phylogeny of ribosomal RNA genes, substantially improving on a known limitation of current AAI implementations. Our analysis of the resulting AAI matrices also indicated that bacterial lineages predominantly evolve gradually, rather than showing bursts of diversification, and that AAI thresholds to define classes, orders, and families are generally elusive. Therefore, FastAAI uniquely expands the toolbox for microbiome analysis and allows it to scale to millions of genomes.

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Publication Details

Journal
Nucleic Acids Research
Published
2025-04-22
DOI
https://doi.org/10.1093/nar/gkaf348
Citations
24
Primary Topic
Genomics and Phylogenetic Studies
Type
article
Field-Weighted Citation Impact
19.34

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article

FastAAI: efficient estimation of genome average amino acid identity and phylum-level relationships using tetramers of universal proteins

Luis M. Rodriguez‐R, K. Gerhardt, Carlos A. Ruiz-Pérez, Chirag Jain et al.
24 citations
Nucleic Acids Research
Genomics and Phylogenetic Studies
19.34
article

FastAAI: efficient estimation of genome average amino acid identity and phylum-level relationships using tetramers of universal proteins

Luis M. Rodriguez‐R, K. Gerhardt, Carlos A. Ruiz-Pérez, Chirag Jain, James M. Tiedje, Konstantinos T. Konstantinidis, James R. Cole
article en
24 citations

Abstract

Estimation of whole-genome relatedness and taxonomic identification are two important bioinformatics tasks in describing environmental or clinical microbiomes. The genome-aggregate Average Nucleotide Identity is routinely used to derive the relatedness of closely related (species level) microbial and viral genomes, but it is not appropriate for more divergent genomes. Average Amino-acid Identity (AAI) can be used in the latter cases, but no current AAI implementation can efficiently compare thousands of genomes. Here we present FastAAI, a tool that estimates whole-genome pairwise relatedness using shared tetramers of universal proteins in a matter of microseconds, providing a speedup of up to 5 orders of magnitude when compared with current methods for calculating AAI or alternative whole-genome metrics. Further, FastAAI resolves distantly related genomes related at the phylum level with comparable accuracy to the phylogeny of ribosomal RNA genes, substantially improving on a known limitation of current AAI implementations. Our analysis of the resulting AAI matrices also indicated that bacterial lineages predominantly evolve gradually, rather than showing bursts of diversification, and that AAI thresholds to define classes, orders, and families are generally elusive. Therefore, FastAAI uniquely expands the toolbox for microbiome analysis and allows it to scale to millions of genomes.

Nucleic Acids ResearchVol. 53(8)
Georgia Institute of Technology (US), Universität Innsbruck (AT), Digital Science (United States) (US), Indian Institute of Science Bangalore (IN), Michigan State University (US)
National Science Foundation
Life in Land
Openalex Percentile: Top 0%
Genomics and Phylogenetic Studies
19.34
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