No / Background: Phylogenetic studies of bacteria have been based so far either on a single gene (usually the 16SrRNA) or on concatenated housekeeping genes. For what concerns the genus Mycobacterium these approaches support the separation of rapidly and slowly growing species and the clustering of most species in well-defined phylogenetic groups. The advent of high-throughput shotgun sequencing leads us to revise conventional tax-onomy of mycobacteria on the light of genomic data. For this purpose we investigated 88 newly sequenced species in addition to 60 retrieved from GenBank and used the Average Nucleotide Identity pairwise scores to reconstruct phylogenetic relationships within this genus.Results:Our analysis confirmed the separation of slow and rapid growers and the intermediate position occupied by the M. terrae complex. Among the rapid growers, the species of the M. chelonae-abscessus complex belonged to the most ancestral cluster. Other major clades of rapid growers included the species related to M. fortuitum and M. smegmatis and a large grouping containing mostly environmental species rarely isolated from humans. The members of the M. terrae complex appeared as the most ancestral slow growers. Among slow growers two deep branches led to the clusters of species related to M. celatum and M. xenopi and to a large group harboring most of the species more frequently responsible of disease in humans, including the major pathogenic mycobacteria (M.tuberculosis,M. leprae,M. ulcerans). The species previously grouped in the M. simiae complex were allocated in a number of sub-clades; of them, only the one including the species M. simiae identified the real members of this complex. The other clades included also species previously not considered related to M. simiae. The ANI analysis,in most cases supported by Genome to Genome Distance and by Genomic Signature-Delta Difference, showed that a number of species with standing in literature were indeed synonymous.Conclusions:Genomic data revealed to be much more informative in comparison with phenotype. We believe that the genomic revolution enabled by high-throughput shotgun sequencing should now be considered in order to revise the conservative approaches still informing taxonomic sciences.

Yes / The aim of this study was to clarify the taxonomic status of named species within the genus Mycobacterium. The analysis of genomes belonging to 174 taxa (species or subspecies) of the genus Mycobacterium was conducted using both the Average Nucleotide Identity and the Genome to Genome Distance. A number of synonymous taxa were detected. The list of synonyms includes: two subspecies of M. chelonae (M. chelonae subsp. bovis and M. chelonae subsp. gwanakae), two subspecies of M. fortuitum (M. fortuitum subsp. fortuitum and M. fortuitum subsp. acetamidolyticum), four subspecies of M. avium (M. avium subsp. avium, M. avium subsp. silvaticum, M. avium subsp. paratuberculosis and “M. avium subsp. hominissuis”), two couples of subspecies of M. intracellulare (M. intracellulare subsp. intracellulare/M. intracellulare subsp. paraintracellulare and M. intracellulare subsp. chimaera/M. intracellulare subsp. yongonense), the species M. austroafricanum and M. vanbaalenii, the species M. senegalense and M. conceptionense, the species M. talmoniae and M. eburneum and the species M. marinum, M. ulcerans and M. pseudoshottsii. Furthermore one species were reclassified as subspecies of another mycobacterium: M. lepraemurium was reclassified as a subspecies of M. avium (M. avium subsp. lepraemurium). The updates to nomenclature are proposed basing on the priority of names according the Code of nomenclature of prokaryotes. For two species (M. bouchedurhonense and M. marseillense) the loss of standing in nomenclature is proposed because of unavailability of respective type strains in culture collections.

Bacterial taxonomy is the science of classifying, naming, and identifying bacteria. The scope and practice of taxonomy has evolved through history with our understanding of life and our growing and changing needs in research, medicine, and industry. As in animal and plant taxonomy, the species is the fundamental unit of taxonomy, but the genetic and phenotypic diversity that exists within a single bacterial species is substantially higher compared to animal or plant species. Therefore, the current "type"-centered classification scheme that describes a species based on a single type strain is not sufficient to classify bacterial diversity, in particular in regard to human, animal, and plant pathogens, for which it is necessary to trace disease outbreaks back to their source. Here we discuss the current needs and limitations of classic bacterial taxonomy and introduce LINbase, a Web service that not only implements current species-based bacterial taxonomy but complements its limitations by providing a new framework for genome sequence-based classification and identification independently of the type-centric species. LINbase uses a sequence similarity-based framework to cluster bacteria into hierarchical taxa, which we call LINgroups, at multiple levels of relatedness and crowdsources users' expertise by encouraging them to circumscribe these groups as taxa from the genus-level to the intraspecies-level. Circumscribing a group of bacteria as a LINgroup, adding a phenotypic description, and giving the LINgroup a name using the LINbase Web interface allows users to instantly share new taxa and complements the lengthy and laborious process of publishing a named species. Furthermore, unknown isolates can be identified immediately as members of a newly described LINgroup with fast and precise algorithms based on their genome sequences, allowing species- and intraspecies-level identification. The employed algorithms are based on a combination of the alignment-based algorithm BLASTN and the alignment-free method Sourmash, which is based on k-mers, and the MinHash algorithm. The potential of LINbase is shown by using examples of plant pathogenic bacteria. / Doctor of Philosophy / Life is always easier when people talk to each other in the same language. Taxonomy is the language that biologists use to communicate about life by 1. classifying organisms into groups, 2. giving names to these groups, and 3. identifying individuals as members of these named groups. When most scientists and the general public think of taxonomy, they think of the hierarchical structure of “Life”, “Domain”, “Kingdom”, “Phylum”, “Class”, “Order”, “Family”, “Genus” and “Species”. However, the basic goal of taxonomy is to allow the identification of an organism as a member of a group that is predictive of its characteristics and to provide a name to communicate about that group with other scientists and the public. In the world of micro-organism, taxonomy is extremely important since there are an estimated 10,000,000 to 1,000,000,000 different bacteria species. Moreover, microbiologists and pathologists need to consider differences among bacterial isolates even within the same species, a level, that the current taxonomic system does not even cover. Therefore, we developed a Web service, LINbase, which uses genome sequences to classify individual microbial isolates. The database at the backend of LINbase assigns Life Identification Numbers (LINs) that express how individual microbial isolates are related to each other above, at, and below the species level. The LINbase Web service is designed to be an interactive web-based encyclopedia of microorganisms where users can share everything they know about micro-organisms, be it individual isolates or groups of isolates, for professional and scientific purposes. To develop LINbase, efficient computer programs were developed and implemented. To show how LINbase can be used, several groups of bacteria that cause plant diseases were classified and described.

This study presents the development and evaluation of an automated pipeline for genome comparison, leveraging four bioinformatics tools: alignment-based methods (pyANI, Fas- tANI) and k-mer-based methods (Sourmash, BinDash 2.0). The analysis focuses on high- quality genomic datasets characterized by 100% completeness, ensuring consistency and accuracy in the comparison process. The pipeline processes genomes under uniform con- ditions, recording key performance metrics such as execution time and rank correlations. Initial comparisons were conducted on a subset of five genomes, generating 10 unique pair- wise comparisons to establish baseline performance. This preliminary analysis identified k = 10 as the optimal k-mer size for Sourmash and BinDash, significantly improving their comparability with alignment-based methods. For the expanded dataset of 175 genomes, encompassing (175C2) = 15,225 unique comparisons, pyANI and FastANI demonstrated high similarity values, often exceeding 90% for closely related genomes. Rank correlations, calculated using Spearman's ρ and Kendall's τ , high- lighted strong agreement between pyANI and FastANI (ρ = 0.9630 , τ = 0.8625) due to their shared alignment-based methodology. Similarly, Sourmash and BinDash, both employing k-mer-based approaches, exhibited moderate-to-strong rank correlations (ρ = 0.6967, τ = 0.5290). In contrast, the rank correlations between alignment-based and k-mer-based tools were lower, underscoring methodological differences in genome similarity calculations. Execution times revealed significant contrasts between the tools. Alignment-based meth- ods required substantial computation time, with pyANI taking an average of 1.97 seconds per comparison and FastANI averaging 0.81 seconds per comparison. Conversely, k-mer- based methods demonstrated exceptional computational efficiency, with Sourmash complet- ing comparisons in 2.1 milliseconds and BinDash in just 0.25 milliseconds per comparison, reflecting a difference of nearly three orders of magnitude between the two categories. These results underscore the trade-offs between computational cost and methodological approaches in genome similarity estimation. This study provides valuable insights into the relative strengths and weaknesses of genome comparison tools, offering a comprehensive framework for selecting appropriate methods for diverse genomic research applications. The findings emphasize the importance of param- eter optimization for k-mer-based tools and highlight the scalability of these methods for large-scale genomic analyses. / Master of Science / This study explores the strengths and weaknesses of different tools used to compare genomes, which are the complete set of DNA in living organisms. Comparing genomes allows scientists to understand how different species are related, uncover shared traits, and identify what makes each species unique. The tools we examined fall into two main categories: detailed tools (called alignment-based methods) and faster, more approximate tools (called k-mer- based methods). The detailed tools, such as pyANI and FastANI, compare DNA sequences piece by piece, providing very accurate results. In contrast, the faster tools, such as Sourmash and BinDash, look for patterns in smaller sections of DNA, which makes them much quicker but sometimes less precise. To start, we tested these tools on a small group of genomes to see how they performed. By adjusting a setting in the faster tools, we found that their results became more similar to the detailed tools, improving their reliability. Encouraged by these findings, we expanded the comparison to a much larger dataset of 175 genomes. For this larger dataset, the detailed tools provided highly accurate results but required much more time and computational power. On the other hand, the faster tools completed the comparisons in a fraction of the time, making them ideal for larger datasets where quick results are needed. We also compared how the tools ranked genome similarities and found that tools using similar methods, like pyANI and FastANI, had very consistent rankings. Likewise, the faster tools, Sourmash and BinDash, also agreed with each other. However, the rankings between the two types of tools (detailed versus faster) were less consistent, reflecting their different approaches to genome comparison. This research provides a practical guide for scientists choosing tools to compare genomes. If accuracy and detail are most important, alignment-based tools are the best choice, though they take more time and computational resources. If speed is critical, such as when working with very large datasets, k-mer-based tools offer an excellent alternative. By understanding the strengths and trade-offs of each method, researchers can make informed decisions to suit their specific needs, whether focusing on small, detailed studies or large-scale genome analyses.