Ussery, D. | bioRxiv Science

bioRxiv2026

Taxonomic Resolution of 16S rRNA, FastANI, Mash, and FastAAI across 30,495 Prokaryotic Type-Strain Genomes

Prokaryotic taxonomy now relies on both marker-gene and genome-wide sequence comparisons, but these methods differ in taxonomic range, scalability, and sensitivity to genome quality. Here, we benchmarked four commonly used approaches, including 16S rRNA identity, FastANI, Mash distance, and FastAAI/Jaccard similarity across a dataset of 30,495 prokaryotic type-strain genomes. Type-strain genomes provide nomenclatural anchors for validly named species, making them a useful framework for evaluating how sequence-based methods correspond to current taxonomic assignments. We evaluated method behavior across taxonomic ranks from species to domain and separated initial method failures from threshold-based failures. When clean full-length 16S rRNA sequences were available, same-species comparisons passed the empirical threshold in >97% of cases. However, a usable full-length 16S rRNA sequence was unavailable for 4,551 of the 16,402 same-species comparisons (28%), limiting marker-gene-based analysis. In addition, 16S rRNA identity ranges overlapped across higher taxonomic ranks, limiting the use of universal rank-specific cutoffs. FastANI provided strong species-level resolution, with same-species comparisons passing the empirical threshold in approximately 88% of cases but was less informative at deeper ranks. Mash enabled rapid genome-scale screening, although its distance values require careful interpretation beyond close relatives. FastAAI provided a genome-wide amino-acid signal, with approximately 92% of same-species comparisons passing the empirical threshold and was especially useful for comparisons beyond the species boundary. Overall, no single method performed optimally across all taxonomic levels. These results support a rank-aware benchmarking framework in which 16S rRNA, FastANI, Mash, and FastAAI are interpreted as complementary tools, with attention to genome quality, missing data, and method-specific failure modes.

genomics↗

bioRxiv2017

dBBQs : dataBase of Bacterial Quality scores

BackgroundIt is well-known that genome sequencing technologies are becoming significantly cheaper and faster. As a result of this, the exponential growth in sequencing data in public databases allows us to explore ever growing large collections of genome sequences. However, it is less known that the majority of available sequenced genome sequences in public databases are not complete, drafts of varying qualities. We have calculated quality scores for around 100,000 bacterial genomes from all major genome repositories and put them in a fast and easy-to-use database.\n\nResultsProkaryotic genomic data from all sources were collected and combined to make a non-redundant set of bacterial genomes. The genome quality score for each was calculated by four different measurements: assembly quality, number of rRNA and tRNA genes, and the occurrence of conserved functional domains. The dataBase of Bacterial Quality scores (dBBQs) was designed to store and retrieve quality scores. It offers fast searching and download features which the result can be used for further analysis. In addition, the search results are shown in interactive JavaScript chart framework using DC.js. The analysis of quality scores across major public genome databases find that around 68% of the genomes are of acceptable quality for many uses.\n\nConclusionsdBBQs (available at http://arc-gem.uams.edu/dbbqs) provides genome quality scores for all available prokaryotic genome sequences with a user-friendly Web-interface. These scores can be used as cut-offs to get a high-quality set of genomes for testing bioinformatics tools or improving the analysis. Moreover, all data of the four measurements that were combined to make the quality score for each genome, which can potentially be used for further analysis. dBBQs will be updated regularly and is freely use for non-commercial purpose.

bioinformatics↗