Microbial Taxonomy and Phylogeny: Genomic Foundations for Biomedical Research and Drug Development

Abstract

This article provides a comprehensive overview of the fundamental principles and modern methodologies shaping microbial taxonomy and phylogeny. Tailored for researchers, scientists, and drug development professionals, it explores the revolutionary shift from phenotype-based classification to genome-driven systematics. The content spans foundational concepts, cutting-edge genomic techniques, challenges in classification, and the critical application of these frameworks in validating microbial identity for biomedical and industrial applications. By synthesizing exploratory, methodological, troubleshooting, and validation intents, this article serves as a foundational guide for leveraging microbial systematics in advanced research and development.

From Linnaean Roots to Genomic Revolution: Exploring the Core Principles of Microbial Systematics

In the era of high-throughput sequencing, the scientific disciplines of microbial taxonomy, phylogeny, and systematics have become foundational to modern microbiology research and its applications in drug development and biotechnology. These interconnected fields provide the essential framework for identifying, naming, classifying, and understanding the evolutionary relationships among microorganisms. For researchers and scientists working with microbial biologicals, precise classification is not merely academic—it directly impacts regulatory pathways, risk assessment, and the commercial development of microbial-based products [1]. The rapid expansion of genomic databases has fundamentally transformed our understanding of microbial diversity and evolution, revealing that natural microbial innovation occurs primarily through horizontal gene transfer, blurring traditional distinctions between "natural" and "genetically modified" organisms in regulatory contexts [1]. This technical guide examines the core principles, current methodologies, and practical applications of these organizing sciences within the framework of microbial research.

Core Concepts and Definitions

Taxonomy: The Identification and Classification System

Microbial taxonomy is the theoretical and practical framework for the identification, classification, and nomenclature of microorganisms [2]. It provides a systematic approach to organizing microbial diversity based on shared characteristics and evolutionary relationships. Taxonomy operates within a hierarchical structure that ranges from broad, inclusive categories to specific, exclusive ones, with species as the fundamental unit of classification [3]. A species is typically defined as a collection of microbial strains that share key characteristics and exhibit a high degree of genetic similarity, often quantified through metrics such as Average Nucleotide Identity (ANI) [1]. The naming of microorganisms follows the binomial system of genus and species, such as Staphylococcus aureus and Staphylococcus epidermidis [3].

The practice of microbial taxonomy has evolved significantly from early phenotypic characterizations (morphology, biochemical testing) to modern genome-based classification systems [1]. This shift has been driven by the recognition that phenotypic approaches are limited in their ability to elucidate true evolutionary relationships, as distantly related microbes can share features due to convergent evolution or habitat-specific adaptations [1]. The pangenome concept has further refined our understanding of microbial species by distinguishing between the core genome (genes universal to a lineage) and the accessory genome (variable genes that reflect functional adaptations) [1].

Phylogeny: The Evolutionary History

Phylogeny represents the evolutionary history and relationships among species, genes, and populations, typically visualized through phylogenetic trees [4]. This field uses comparative genomics to reconstruct the evolutionary pathways that have led to the diversification of microbial life. Phylogenetic analyses rely on the comparison of genetic sequences, with the 16S rRNA gene serving as a cornerstone for bacterial and archaeal phylogeny due to its slow evolutionary rate and universal distribution across these domains [1].

The construction of phylogenetic trees involves multiple steps, including sequence alignment, model selection, and tree inference, with the resulting trees being described as either rooted (showing evolutionary direction) or unrooted (showing only relationships) [4]. Key concepts in phylogeny include homologous sequences (genes shared through common ancestry), clades (groups of organisms descended from a common ancestor), and genetic distance (the degree of genetic divergence between taxa) [4]. For microbiomes, phylogenetic trees can be constructed from 16S rRNA sequencing data or whole genome shotgun sequencing, with each approach having distinct strengths and limitations for analyzing microbial community structures and functions [5].

Systematics: The Comprehensive Study of Diversity

Systematics encompasses the comprehensive study of organismal diversity and evolutionary relationships, integrating data from taxonomy, phylogeny, morphology, ecology, and genomics [6]. The field is dedicated to advancing microbial systematics through research, collaboration, and knowledge dissemination, as exemplified by organizations like the Bergey's Manual of Systematic Bacteriology and the International Society for Microbial Systematics (BISMiS) [6] [3].

Microbial systematics employs a polyphasic approach that combines genotypic, phenotypic, and phylogenetic information to classify microorganisms [3]. This integrated methodology is particularly important for reconciling the challenges posed by extensive horizontal gene transfer in microbes, which can result in discordance between evolutionary histories of different genes within the same organism [1]. Systematics provides the theoretical foundation for taxonomic frameworks and nomenclatural systems that enable researchers to communicate consistently about microbial diversity.

Table 1: Comparative Analysis of Core Disciplines in Microbial Organization

Discipline	Primary Focus	Key Methods	Output
Taxonomy	Identification, classification, and nomenclature of microorganisms	Phenotypic characterization, DNA-DNA hybridization, Average Nucleotide Identity (ANI)	Hierarchical classification (species, genus, family, etc.), Binomial names
Phylogeny	Evolutionary history and relationships	Sequence alignment, phylogenetic tree construction, comparative genomics	Phylogenetic trees, evolutionary models, genetic distances
Systematics	Comprehensive study of organismal diversity	Integrated polyphasic approach (genotypic, phenotypic, phylogenetic)	Taxonomic frameworks, nomenclatural systems, evolutionary hypotheses

Methodologies and Experimental Approaches

Taxonomic Classification Techniques

Modern microbial taxonomy employs a suite of genomic tools and computational approaches for taxonomic classification, which are essential for binning and metagenomic analysis [2]. The development of novel algorithms and databases continues to enhance the precision and scalability of microbial classification systems. Current methods include:

• Average Nucleotide Identity (ANI): A standard metric for species delineation based on whole-genome comparisons, with thresholds typically set at 95-96% for species boundaries [1].
• Multi-Locus Sequence Analysis (MLSA): Utilizes sequences of multiple housekeeping genes to provide better resolution than single-gene approaches.
• Pangenome Analysis: Distinguishes between core and accessory genomes to understand the genetic diversity within taxonomic groups [1].
• Digital DNA-DNA Hybridization (dDDH): A computational alternative to traditional laboratory-based DNA hybridization methods.

The Bergey's Manual of Systematic Bacteriology remains a cornerstone resource for prokaryotic taxonomy, providing comprehensive descriptions of bacterial and archaeal taxa [3]. However, the field faces ongoing challenges, including the fact that an estimated 85% of microbial life remains unculturable, limiting phenotypic characterization [1]. For these uncultured lineages, taxonomy must rely solely on sequence-based classifications from metagenome-assembled genomes and single-cell genomics [1].

Phylogenetic Tree Construction

Phylogenetic reconstruction from microbiome data presents distinct challenges and opportunities based on the sequencing approach employed. For 16S rRNA sequencing, established tools leverage the highly conserved nature of this marker gene, while whole-genome shotgun sequencing requires more complex approaches due to the vast diversity of genomic regions [5]. The phylogenetic tree construction workflow typically involves:

• Sequence Acquisition: Obtaining 16S rRNA or whole-genome sequences from microbial isolates or metagenomic samples.
• Multiple Sequence Alignment: Aligning homologous sequences using tools such as PASTA, MUSCLE, or MAFFT [7].
• Model Selection: Identifying the most appropriate evolutionary model for the dataset.
• Tree Inference: Constructing trees using methods like maximum likelihood, Bayesian inference, or neighbor-joining.
• Tree Evaluation: Assessing tree robustness through bootstrap analysis or posterior probabilities.

Recent innovations have demonstrated that citizen science approaches integrated into video games can significantly improve multiple sequence alignment quality, leading to enhanced phylogenetic estimates for microbial communities [7]. Such crowd-sourced approaches have solved millions of alignment puzzles, achieving improvements over state-of-the-art computational methods alone [7].

Figure 1: Workflow for Phylogenetic Tree Construction from Microbial Sequence Data

Integrated Systematic Frameworks

Systematics employs integrated frameworks that combine data from multiple sources to develop robust classifications that reflect evolutionary history. Key approaches include:

• Polyphasic Taxonomy: Combines phenotypic, genotypic, and phylogenetic data to delineate taxonomic groups.
• Phylogenomic Analysis: Uses large-scale genomic datasets to infer evolutionary relationships with greater accuracy than single-gene phylogenies.
• Comparative Genomics: Identifies shared and unique genomic features across taxa to understand functional and evolutionary relationships.

The dynamic nature of microbial genomes, particularly the prevalence of horizontal gene transfer (HGT), presents both challenges and opportunities for systematics. HGT is now recognized as a dominant mechanism of genetic innovation in bacteria and archaea, facilitating the natural exchange of genetic material between distantly related taxa [1]. This reality complicates phylogenetic reconstruction, as different genes within the same organism may have distinct evolutionary histories. Modern systematics must therefore account for these complex patterns of gene flow when reconstructing microbial evolutionary history.

Applications in Research and Drug Development

Microbial Discovery and Characterization

The systematic organization of microbial life enables the discovery and characterization of novel microorganisms with potential applications in drug development and biotechnology. Recent discoveries highlight the importance of robust taxonomic and phylogenetic frameworks:

• Bacteriovorax antarcticus: A novel bacterial predator isolated from Potter Cove, Antarctica, representing a group (BALOs) that is underrepresented in current taxonomic frameworks [8].
• Streptomyces cavernicola, S. solicavernae, S. luteolus: New Streptomyces species discovered in cave soil samples with potential for producing biologically active compounds, including antibiotics [8].
• Methanochimaera problematica: A novel hydrogenotrophic methanoarchaeon isolated from deep-sea cold seep sediments [8].
• Exophiala zingiberis: A novel cellulase-producing black yeast-like fungus isolated from ginger tubers [8].

The integration of citizen science initiatives with professional research has accelerated microbial discovery and classification. Projects like Borderlands Science have engaged millions of participants in solving multiple sequence alignment puzzles, resulting in improved phylogenetic trees for microbiome data [7]. This approach demonstrates how massive public participation can address computational challenges that are intractable for individual researchers or conventional algorithms.

Table 2: Research Reagent Solutions for Microbial Taxonomy and Phylogeny

Reagent/Resource	Function/Application	Example Use Cases
16S rRNA PCR Primers	Amplification of 16S rRNA gene for phylogenetic analysis	Bacterial and archaeal identification, microbial community profiling
Whole Genome Sequencing Kits	Comprehensive genomic data acquisition	Pangenome analysis, phylogenomics, taxonomic delineation
Multiple Sequence Alignment Tools	Alignment of homologous sequences for phylogenetic analysis	PASTA, MUSCLE, MAFFT for tree construction [7]
Phylogenetic Tree Inference Software	Construction of evolutionary trees from sequence data	FastTree, RAxML, MrBayes for phylogenetic estimation [7]
Taxonomic Reference Databases	Reference sequences for taxonomic classification	Greengenes, Rfam, SILVA for sequence placement [7]

Implications for Biotechnology and Regulatory Science

The principles of microbial taxonomy, phylogeny, and systematics have direct implications for biotechnology development and regulatory frameworks. Current risk assessment paradigms for microbial products often intensify scrutiny for organisms classified as genetically modified (GM) or containing novel combinations of genetic material (NCGM) [1]. However, genomic analyses reveal that horizontal gene transfer between different taxa is a natural and frequent occurrence in microbial evolution, suggesting that many microbes could be considered "naturally occurring GM organisms" [1].

This understanding has prompted calls for more science-based regulatory approaches that focus on the actual functions and phenotypic characteristics of microbes rather than their classification as GM or non-GM [1]. Such approaches would better align with the biological realities of microbial evolution and facilitate the development of effective microbial solutions for agricultural, industrial, and therapeutic applications. For drug development professionals, accurate taxonomic identification and phylogenetic placement are essential for understanding the functional potential, safety profile, and ecological roles of microbial isolates.

Future Directions and Challenges

The fields of microbial taxonomy, phylogeny, and systematics continue to evolve rapidly, driven by technological advances and new conceptual frameworks. Future developments include:

• Integration of Machine Learning: Application of artificial intelligence and machine learning approaches to taxonomic classification and phylogenetic inference, potentially bridging paleontology and biology through advanced pattern recognition [9].
• Resolution of the Unculturables: Development of novel cultivation techniques and single-cell genomics approaches to access the vast diversity of currently unculturable microorganisms [1].
• Dynamic Classification Systems: Implementation of more flexible, dynamic taxonomic systems that can accommodate the fluid nature of microbial genomes and extensive horizontal gene transfer [1].
• Standardization and Collaboration: Enhanced international collaboration and standardization in microbial systematics, as promoted by organizations like BISMiS, which holds regular meetings and disseminates knowledge through publications and seminars [6].

The ongoing revision of microbial taxonomy in light of expanding genomic data will continue to reshape our understanding of the tree of life, with implications for all areas of microbiology and microbial biotechnology [1]. As these fields progress, they will provide increasingly powerful frameworks for organizing microbial life and harnessing microbial diversity for the benefit of human health, agriculture, and environmental sustainability.

Figure 2: Interrelationship Between Taxonomy, Phylogeny, Systematics, and Their Applications

The field of microbial taxonomy has undergone a profound transformation, shifting from a foundation built on observable phenotypic characteristics to one rooted in molecular and genomic data. This paradigm shift has fundamentally reshaped how researchers identify, classify, and understand the evolutionary relationships between microorganisms. The initial phenotypic approach, which relied on morphological, biochemical, and physiological characteristics, has been progressively supplemented and ultimately superseded by sequence-based methods that provide a more objective and quantitative framework for microbial classification [10] [1]. This transition began with the adoption of single-marker genes, most notably the 16S ribosomal RNA (rRNA) gene, and has accelerated dramatically with the advent of whole-genome sequencing (WGS) technologies [10] [11]. The resulting genomic taxonomy framework now enables researchers to delineate species with unprecedented precision and reconstruct phylogenetic relationships with greater accuracy, thereby refining our understanding of microbial evolution and diversity [12] [11].

The Era of Phenotypic Classification

Initially, microbial taxonomy was grounded almost exclusively in phenotypic characterizations. These included observable traits such as cellular morphology, Gram staining, biochemical capabilities (e.g., nutrient utilization and metabolic byproducts), growth conditions, and other cultural properties [1] [11]. This polyphasic approach was pragmatic for its time but inherently limited. A significant drawback was that distantly related microbes could share similar phenotypic traits due to convergent evolution or adaptation to similar niches, while closely related organisms might appear dissimilar [10] [1]. This often led to misclassification, as illustrated by the historical grouping of the genus Clostridium, which was united by common morphology and sporulation ability but was later found via molecular methods to represent dozens of phylogenetically distinct groups within the Firmicutes phylum [10]. The heavy reliance on the ability to culture microorganisms in the laboratory created a major bottleneck, leaving the vast majority (>80%) of microbial diversity—often referred to as "microbial dark matter"—unexplored and unclassified [10] [1].

The Molecular Revolution: Single Gene Markers and Beyond

The discovery of the 16S rRNA gene as a phylogenetic marker by Carl Woese in the 1970s marked a pivotal turning point [10] [13]. This gene offered several ideal properties: it is universally present across Bacteria and Archaea, its function is constant, and its sequence contains both highly conserved regions (useful for alignment) and variable regions (useful for distinguishing taxa) [10]. The comparison of 16S rRNA sequences led to the fundamental reorganization of the tree of life into three domains—Bacteria, Archaea, and Eucarya—overturning the previous phenotype-based schema that placed all microbes at the base of the tree [10] [13].

The use of DNA-DNA hybridization (DDH) became the gold standard for delineating bacterial species, with a threshold of ≥70% similarity used to define a species [11]. However, DDH was labor-intensive, difficult to standardize, and not easily scalable. The development of the Polymerase Chain Reaction (PCR) and Sanger sequencing subsequently enabled the wider use of 16S rRNA gene sequencing for microbial identification and phylogenetic inference, forming the backbone of microbial molecular ecology for decades [10]. Despite its revolutionary impact, 16S rRNA gene sequencing had limitations, including poor phylogenetic resolution at the species level, inadequate reference databases, and the susceptibility of the gene to horizontal gene transfer and recombination events, which sometimes obscured true evolutionary relationships [10].

Table 1: Key Transitions in Microbial Taxonomy

Era	Primary Tools & Data	Key Strengths	Major Limitations
Phenotypic	Morphology, biochemistry, growth requirements	Low-tech, functional insights	Low resolution, culture-dependent, subjective
Single-Gene Molecular	16S rRNA sequencing, DDH	Culture-independent, objective, universal marker	Poor species-level resolution, single gene history
Genomic	Whole Genome Sequencing, ANI, dDDH, Core Genome Analysis	High resolution, comprehensive, digital, reproducible	Cost, computational demands, data management

The Genomic Era: A New Taxonomy Framework

The advent of whole-genome sequencing (WGS) has launched microbial taxonomy into a new era, enabling a systematics framework based on the comprehensive information retrieved from complete genomes [11]. This genomic taxonomy is not merely an enriched version of the polyphasic approach but is fundamentally framed on a robust genomic backbone.

Genomic Species Delineation Metrics

A key development has been establishing computational metrics to replace traditional methods like DDH. The most widely adopted of these is Average Nucleotide Identity (ANI), which provides a robust, digital measure of genomic relatedness. Studies have shown that an ANI of approximately 95% corresponds to the traditional 70% DDH threshold for species demarcation [11]. Average Amino Acid Identity (AAI), which calculates the average identity of all orthologous protein-coding genes shared between two genomes, serves a similar purpose for functional genomic relatedness [11]. Complementing these, the Karlin genomic signature (δ), which measures the difference in dinucleotide relative abundance between genomes, provides a species-specific compositional signature reflecting underlying differences in DNA structure and repair mechanisms [11]. Finally, *in silico Genome-to-Genome Distance Hybridization (GGDH or dDDH) calculates genome-to-genome distances based on high-scoring segment pairs (HSPs) from whole-genome comparisons, effectively digitalizing the wet-lab DDH process [11].

Table 2: Genomic Standards for Species and Genus Delineation

Taxonomic Rank	Genomic Standard	Typical Threshold	Method Description
Species	Average Nucleotide Identity (ANI)	>95% [11]	Average nucleotide identity of all orthologous genes shared between two genomes.
	In silico DDH (GGDH/dDDH)	>70% [11]	Computational simulation of DNA-DNA hybridization using genome sequences.
	Karlin Genomic Signature (δ*)	<10 [11]	Measure of dissimilarity in dinucleotide relative abundance between two genomes.
Genus	16S rRNA Gene Identity	>95% [11]	Historically used, but now supplemented by genome-based phylogenies.
	Multilocus Sequence Analysis (MLSA)	Monophyletic Group [11]	Phylogenetic analysis based on concatenated sequences of multiple core protein-coding genes.
	Supertree / Core Genome Phylogeny	Monophyletic Group [11]	Phylogenetic tree constructed from the alignment of all genes in the core genome.

The Pangenome Concept and Phylogenomics

The genomic era has also introduced the pangenome concept, which divides the total gene content of a lineage into the core genome (genes shared by all strains) and the accessory genome (genes present in some but not all strains) [1]. The core genome, comprising genes essential for basic cellular functions, is typically vertically inherited and is therefore highly suitable for constructing robust phylogenies for taxonomic ranking (phylogenomics) [1]. In contrast, the accessory genome, which can constitute over 80% of a lineage's gene content, is often acquired through horizontal gene transfer (HGT) and confers adaptive traits for specific lifestyles [1]. This dynamic nature of microbial genomes, with constant genetic flux through HGT, challenges traditional taxonomic views and risk assessment frameworks that rely on fixed genetic definitions [1].

Advanced Genomic Workflows and Marker Selection

For modern phylogenomic studies, especially those involving non-cultivable microorganisms, the process begins with obtaining genomes from metagenome-assembled genomes (MAGs) or single-cell genomics [12] [10]. Metagenomic binning strategies that leverage differential abundance patterns of populations across multiple samples have proven highly effective, routinely producing high-quality population genomes (>80% complete, <10% contaminated) [10]. These MAGs, however, seldom contain the full genomic repertoire of a population and can lack standard marker genes due to assembly errors, necessitating flexible methods for phylogenetic analysis [12].

To address the limitations of using a fixed set of universal marker genes, advanced computational tools like TMarSel (Tailored Marker Selection) have been developed [12]. This software performs an automated, tailored selection of phylogenetic marker genes from the entire pool of gene families (e.g., from KEGG and EggNOG databases) present in an input genome collection. It builds a copy-number matrix of gene families across genomes and employs an algorithm to iteratively select k markers that maximize the generalized mean number of markers per genome, thereby improving the accuracy of downstream phylogenetic trees, even with taxonomically imbalanced or incomplete MAGs [12]. The selected markers are then used to infer a species tree using summary methods like ASTRAL-Pro2, which can handle multi-copy gene families [12].

Modern Phylogenomics and Taxonomy Workflow

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Essential Reagents and Materials for Genomic Taxonomy

Item	Function/Application
High-Quality DNA Extraction Kits	To obtain pure, high-molecular-weight genomic DNA from microbial cultures or environmental samples for WGS and MAG generation.
Metagenomic DNA Library Prep Kits	For preparing sequencing libraries from complex environmental DNA, enabling the reconstruction of MAGs.
16S rRNA Gene PCR Primers	For amplifying and sequencing the 16S rRNA gene from bacterial and archaeal isolates, providing initial phylogenetic placement.
Whole Genome Sequencing Services	Providing high-throughput sequencing platforms (e.g., Illumina, PacBio, Oxford Nanopore) to generate raw genomic data.
Bioinformatics Software (KEGG, EggNOG)	Databases and tools for functional annotation of open reading frames (ORFs) into gene families, essential for marker selection [12].
Phylogenetic Software (ASTRAL-Pro2)	Summary method software for inferring species trees from a set of gene trees, accounting for gene duplication and loss [12].
Taxonomic Classification Databases (GTDB)	Genome-centric databases providing a standardized microbial taxonomy based on genome phylogeny for accurate classification [14].
Average Nucleotide Identity (ANI) Calculator	Computational tool for calculating ANI between genome pairs to determine species boundaries [11].
Benzyl (4-iodocyclohexyl)carbamate	Benzyl (4-iodocyclohexyl)carbamate|RUO|Cas 16801-63-1
Zinc caprylate	Zinc Caprylate|Research Compound

The historical shift from phenotypic characteristics to molecular and genomic data represents a fundamental maturation of microbial taxonomy into a more objective, quantitative, and robust scientific discipline. This transition, driven by technological advances in sequencing and bioinformatics, has resolved long-standing taxonomic errors and unveiled the vast, previously hidden diversity of the microbial world. The modern framework of genomic taxonomy, with its standardized metrics like ANI and its ability to leverage entire genome sequences for phylogenetics, provides an unprecedented capacity to delineate species and reconstruct evolutionary history. As genomic databases continue to expand and computational methods become more sophisticated, the integration of taxonomic classification with functional and ecological data will further deepen our understanding of microbial evolution and its practical applications in medicine, biotechnology, and environmental science.

This technical guide provides a comprehensive overview of the hierarchical framework of biological classification, with a specialized focus on its application in microbial taxonomy and phylogeny. We detail the core taxonomic ranks—from domain to strain—contextualized within modern molecular methodologies essential for researchers in drug development and microbial science. The document integrates structured data summaries, standard experimental protocols for phylogenetic analysis, and visual workflows to serve as a foundational resource for fundamental research in microbial systematics.

Biological taxonomy is the scientific discipline of classifying organisms into a hierarchical system that reflects evolutionary relationships. The relative or absolute level of a group of organisms (a taxon) in this hierarchy is known as its taxonomic rank [15]. This system organizes life from the most inclusive groups, such as domains, down to the most specific, like species and strains, providing a standardized framework for scientific communication. The science of naming and classifying organisms is rooted in the work of Carl Linnaeus, who established the binomial nomenclature system in the 18th century [16].

Within the context of microbial research, accurate classification is paramount. It enables researchers to identify pathogens, understand microbial ecology in the human microbiome, and trace the origins of antibiotic resistance. The transition from phenomenological classification based on appearance to methods grounded in cladistics and molecular systematics has revolutionized taxonomy, particularly for prokaryotes, which often lack distinguishing morphological traits [15] [17]. For drug development professionals, a precise understanding of this hierarchy is not merely academic; it informs target selection, vaccine development, and the tracking of disease outbreaks at a molecular level.

The Core Taxonomic Ranks

The modern taxonomic system is built upon a series of obligatory ranks. The seven main ranks, from most general to most specific, are: kingdom, phylum/division, class, order, family, genus, and species [15] [16]. The introduction of genetic analysis led to the addition of the domain as the highest rank, a fundamental division that supersedes the kingdom level [16]. The principle underlying this hierarchy is that each subsequent level represents a group of organisms sharing a more recent common ancestor and, consequently, a greater number of shared characteristics.

Table 1: Primary Taxonomic Ranks and Their Characteristics

Rank	Latin Term	Key Characteristics	Microbial Example
Domain	Dominium	Most fundamental cellular organization; separates Archaea, Bacteria, and Eukarya [15] [16]	Bacteria
Kingdom	Regnum	Major divisions within a domain (e.g., metabolic diversity) [18]	Monera (in traditional systems)
Phylum	Phylum	General body plan or fundamental genetic divergence [16]	Firmicutes, Bacteroidetes
Class	Classis	Groups of related orders sharing common traits [18]	Bacilli, Clostridia
Order	Ordo	Groups of related families [16]	Lactobacillales, Bacillales
Family	Familia	Groups of related genera; often has standard suffix (e.g., -aceae) [15]	Lactobacillaceae
Genus	Genus	Group of very closely related species [16]	Lactobacillus, Bacillus
Species	Species	Group of individuals that can interbreed (concept applied conceptually to microbes); basic unit of classification [16]	Lactobacillus acidophilus

As one ascends the taxonomic hierarchy from species to domain, the number of organisms within each group increases, while the number of shared, specific characteristics decreases [18]. The species is the most fundamental unit, classically defined by the ability of members to successfully interbreed and produce fertile offspring—a concept adapted for prokaryotes through genetic and genomic criteria [16].

The Microbial Context: Domains Bacteria and Archaea

The classification of microbes operates within the same hierarchical framework but faces unique challenges due to their lack of complex morphology and the prevalence of horizontal gene transfer. The domain level, proposed by Carl Woese, is critical in microbiology. It separates all life into three groups: Archaea, Bacteria, and Eukarya, based on fundamental genetic and biochemical differences in cellular organization [17] [16]. This division established that the prokaryotes are not a single, monophyletic group, but are split into two fundamentally distinct domains.

Historically, bacterial classification was problematic and relied on phenotypic traits like Gram staining, leading to groups such as Gracilicutes (gram-negative) and Firmacutes (gram-positive) [17]. The advent of molecular phylogenetics, particularly the use of the 16S ribosomal RNA (rRNA) gene as a molecular chronometer, provided a robust, quantitative method for determining evolutionary relationships and assigning taxonomic ranks to microbes [17] [19]. This allows for a classification that more accurately reflects evolutionary history.

Subranks and Strain-Level Classification

To address the need for finer resolution within major ranks, the taxonomic system allows for the creation of subranks. These are denoted by prefixes such as "sub-" or "super-". For example, between the family and genus ranks, one may find subfamily, tribe, and subtribe [15]. In botany, additional secondary ranks like section and series are used [15]. These subranks are essential for organizing biologically complex groups, providing granularity without altering the core seven-tiered system.

For microbial researchers, the most critical level of granularity is below the species, at the strain level. A strain represents a genetic variant or subtype of a species. Strains are often designated by alphanumeric codes and can differ in pathogenic potential, antibiotic resistance, or metabolic capabilities. For instance, Escherichia coli O157:H7 is a specific strain known for causing severe foodborne illness. While not a formal rank in the Linnaean hierarchy, the strain is a functional unit in laboratory and clinical settings, enabling precise communication about microbial isolates for diagnostics and therapy development.

Table 2: Common Subranks and Infraspecific Levels in Taxonomy

Level	Prefix/Suffix	Function	Example
Superfamily	super-	Grouping of related families	In zoology: Canoidea
Subfamily	-oideae (bot.), -inae (zoo.)	Subdivision of a family	Pantherinae (big cats)
Tribe	-eae (bot.), -ini (zoo.)	Subdivision of a subfamily	Heliantheae (sunflowers)
Subspecies	subsp. or ssp.	Geographically isolated variants	Panthera tigris altaica (Siberian tiger) [16]
Strain	N/A	Genetic variant within a species; crucial for microbiology	Lactobacillus acidophilus NCFM

Methodologies for Phylogenetic Analysis in Microbiology

Experimental protocols for determining taxonomic placement and constructing phylogenetic trees for microbes rely heavily on molecular data. The following methodologies are foundational to the field.

16S rRNA Gene Sequencing

Principle: The 16S ribosomal RNA gene is a component of the 30S small subunit of the prokaryotic ribosome. It contains highly conserved regions (for primer binding) and variable regions (for discrimination between taxa), making it an ideal marker gene for identifying and classifying bacteria and archaea [19].

Protocol:

• DNA Extraction: Isolate genomic DNA from a pure bacterial culture or directly from an environmental sample (e.g., gut microbiome).
• PCR Amplification: Amplify the 16S rRNA gene using universal or domain-specific primers targeting the conserved regions.
• Sequencing: Purify the PCR product and perform Sanger sequencing for pure isolates or Next-Generation Sequencing (NGS) for complex communities.
• Bioinformatic Analysis:
  • Quality Filtering: Remove low-quality sequences and primers.
  • Clustering: Cluster sequences into Operational Taxonomic Units (OTUs) or Amplicon Sequence Variants (ASVs) based on similarity (e.g., 97% for species-level OTUs).
  • Alignment: Align sequences against a reference database (e.g., SILVA, Greengenes).
  • Tree Construction: Infer a phylogenetic tree using methods like Maximum Likelihood or Neighbor-Joining.
• Taxonomic Assignment: Assign taxonomy to each sequence or cluster by comparing it to a curated database of known 16S sequences.

Whole-Genome Shotgun (WGS) Metagenomics and Phylogenomics

Principle: This approach involves randomly shearing and sequencing all DNA from a sample, providing access to all genes, not just a single marker. Phylogenomics uses information from multiple genes or entire genomes to infer evolutionary relationships, offering much higher resolution than single-gene analysis [5].

Protocol:

• Library Preparation: Fragment total community DNA, ligate adapters, and prepare a sequencing library without a PCR amplification step targeting a specific gene.
• High-Throughput Sequencing: Sequence the library using an Illumina, Ion Torrent, or other NGS platform.
• Bioinformatic Processing:
  • Assembly: De novo assemble reads into longer contigs, or map reads to a reference database.
  • Binning: Group contigs into Metagenome-Assembled Genomes (MAGs) based on sequence composition and abundance.
  • Gene Calling & Annotation: Predict open reading frames and annotate their function.
• Phylogenetic Tree Construction:
  • Identify a set of single-copy core genes present in the MAGs and reference genomes.
  • Concatenate the protein or nucleotide sequences of these genes.
  • Construct a phylogenetic tree from the concatenated alignment using robust methods like Maximum Likelihood.

The Scientist's Toolkit: Essential Reagents and Materials

Table 3: Key Research Reagents and Solutions for Microbial Phylogenetics

Reagent/Material	Function	Example Use Case
Universal 16S rRNA Primers	PCR amplification of the 16S gene from a broad range of prokaryotes [19]	Initial amplification for community profiling or isolate identification.
DNA Extraction Kits (e.g., for stool, soil)	Standardized protocols for lysing diverse cell types and purifying nucleic acids.	Extracting high-quality, inhibitor-free DNA from complex microbial samples.
High-Fidelity DNA Polymerase	Accurate amplification of DNA templates with low error rates.	Critical for PCR steps prior to sequencing to avoid introduction of errors.
Next-Generation Sequencing Kits	Library preparation and sequencing reagents for platforms like Illumina.	Generating the raw sequence data for WGS metagenomics or 16S amplicon sequencing.
Bioinformatic Databases (e.g., SILVA, GTDB)	Curated collections of reference sequences and taxonomic information.	Taxonomic assignment of query sequences and phylogenetic tree rooting.
Bioinformatic Software (e.g., QIIME 2, Mothur, PhyloPhlAn)	Integrated suites for processing raw sequence data, building OTU/ASV tables, and constructing phylogenetic trees [5].	Performing end-to-end analysis from raw sequences to ecological statistics and phylogenies.
Sodium butane-1-sulfonate hydrate	Sodium butane-1-sulfonate hydrate, MF:C4H11NaO4S, MW:178.18 g/mol	Chemical Reagent
1-Methyl-3-nitro-5-propoxybenzene	1-Methyl-3-nitro-5-propoxybenzene|1881293-62-4	1-Methyl-3-nitro-5-propoxybenzene (CAS 1881293-62-4) is a valuable nitropropoxybenzene derivative for research as a chemical intermediate. For Research Use Only. Not for human or veterinary use.

Current Challenges and Future Directions

Despite advances, microbial taxonomy faces significant challenges. The species concept remains problematic for prokaryotes, leading to the use of operational definitions like the 95-96% average nucleotide identity (ANI) threshold [19]. Furthermore, the vast majority of microbial diversity is uncultured, meaning taxonomic classifications are often based solely on sequence data from environmental samples. The lack of robust tools for constructing phylogenetic trees from WGS data, compared to the well-established 16S pipeline, also presents a hurdle for researchers, particularly those in downstream fields like statistics and machine learning [5].

Future progress will depend on standardizing methods for phylogenomic analysis and integrating them into user-friendly pipelines. The expansion of comprehensive reference databases like the Genome Taxonomy Database (GTDB) is crucial for accurate taxonomic placement. For drug development, the move towards strain-level analysis will be essential for understanding virulence and developing targeted therapies. The integration of phylogenetic information into statistical models of microbiome data is an active area of research, promising improved accuracy in linking microbial communities to host health and disease states [19] [5].

The taxonomy of prokaryotes has long presented a fundamental challenge to microbiologists. Unlike animals and plants, where sexual reproduction provides a natural framework for defining species through genetic cohesion, prokaryotes do not engage in sexual reproduction stricto sensu, making species definition more elusive [20]. This discrepancy has even led some to suggest that bacteria cannot and need not be organized into species, instead representing a series of organisms with different divergence levels reflecting their evolutionary history [20]. However, in practice, microbiologists can consistently recognize and designate bacterial isolates based on phenotypic characteristics, and genomic comparisons reveal that bacteria form clear clusters of highly related individuals rather than showing a scattered distribution [20]. This paradox highlights the complexity of establishing a biologically relevant species concept for prokaryotes that accommodates their unique genomic architectures and evolutionary mechanisms.

The development of prokaryotic taxonomy has been delayed relative to macroscopic organisms, due in part to technical limitations and the historical focus of evolutionary biologists on sexual organisms [20]. Early microbiologists relied exclusively on phenotypic traits to characterize and classify bacteria, similar to approaches used by naturalists for animals and plants [20]. However, the discovery that phenotypic traits could be transmitted horizontally between bacterial cells revealed a profound difference from macroscopic organisms, where traits are almost exclusively inherited vertically [20]. This early observation foreshadowed our current understanding of the extensive role horizontal gene transfer plays in bacterial evolution and the challenges it presents for species definition.

Theoretical Frameworks: From Phenotype to Genotype

Historical Concepts and Their Limitations

Initial approaches to prokaryotic classification relied heavily on phenotypic observations, drawing parallels with early taxonomic methods for animals and plants. This phenotypic approach presented immediate challenges, as demonstrated by the seminal work of Oswald Avery and colleagues, which not only identified DNA as the support of heredity but also showed that phenotypic traits could be transmitted horizontally between bacterial cells [20]. This fundamental difference from macroscopic organisms, where traits are primarily inherited vertically, underscored the need for alternative classification frameworks.

Before the genomic era, species membership was established through DNA-DNA hybridization assays, which compared newly isolated strains to reference strains [20]. The recommended threshold for species membership was set at 70% genomic hybridization [20]. While pragmatic, this approach offered limited insight into the evolutionary processes maintaining species boundaries. The method was technically demanding and not easily scalable, restricting its utility for comprehensive taxonomic studies across diverse prokaryotic lineages.

The Rise of Sequence-Based Thresholds

The emergence of sequencing technologies led to the development of more scalable, sequence-based approaches for species designation. The 16S rRNA subunit, identified as a universal gene shared by all bacteria and archaea, offered the possibility of assessing prokaryotic species membership with a standardized marker across all lineages [20]. Analysis revealed that the 70% identity threshold from DNA-DNA hybridization assays corresponded approximately to 97% identity when using the 16S rRNA subunit [20]. This method became particularly popular with the rise of metagenomic sequencing, enabling taxonomic profiling without cultivation.

A more recent and powerful approach utilizes entire genomes to calculate Average Nucleotide Identity across all shared genes relative to a reference genome [20]. The ANI threshold for species membership has been empirically defined as 95%, based on correlations with established sequence thresholds [20]. This method provides higher resolution than 16S rRNA sequencing and has emerged as a robust standard for species delineation in the genomic era.

Table 1: Comparison of Major Species Delineation Methods in Prokaryotic Taxonomy

Method	Genetic Basis	Threshold	Advantages	Limitations
DNA-DNA Hybridization	Whole-genome similarity	70% hybridization	Established standard; phenotypic correlation	Technically demanding; not scalable
16S rRNA Identity	Single gene sequence	97% identity	Universal marker; enables metagenomic analysis	Limited resolution; conserved nature
Average Nucleotide Identity (ANI)	Whole-genome comparison	95% identity	High resolution; scalable; portable	Requires genome sequencing; computational resources

The Genomic Revolution: Pangenomes and Species Boundaries

The Pangenome Concept

The development of genomic techniques revealed profound differences between prokaryotic genomes and those of animals and plants. Related bacteria can differ dramatically in their gene content, with a typical bacterial species comprising both a set of ubiquitous, highly similar core genes and a set of accessory genes with a scattered distribution [20]. The pangenome represents the total gene diversity of a population, encompassing all distinct orthologs, including both core and accessory genes [20].

Escherichia coli provides a compelling illustration of prokaryotic genomic versatility. The model strain K12 MG1655 contains approximately 4,400 genes, while other strains may contain up to an additional 1,000 genes encoding diverse functions [20]. Comparisons of just 20 E. coli strains reveal a core genome of approximately 2,000 genes, while the pangenome approaches 18,000 genes [20]. Remarkably, over 50% of genes in a single E. coli strain consist of accessory genes lacking orthologs in most other strains. These accessory genes are frequently exchanged between strains and often determine specific lifestyles and ecologies, ranging from environmental to commensal or pathogenic [20].

Resolving Taxonomic Ambiguity Through Genomics

Genomic approaches have revealed instances where phenotype-based classifications misrepresent evolutionary relationships. The case of Shigella provides a particularly illustrative example. This bacterial "genus" comprises four recognized species (S. flexneri, S. boydii, S. sonnei, and S. dysenteriae) grouped based on shared phenotypic properties as obligate pathogens [20]. However, genomic analyses demonstrate that Shigella shares the same core genome as E. coli with >98% sequence identity across core genes, and core-genome phylogenies reveal that Shigella does not form a monophyletic clade [20]. What unites Shigella is the presence of shared virulence genes acquired through horizontal gene transfer, along with characteristic serology and metabolic capabilities [20]. Genomically, Shigella constitutes a subset of E. coli strains with a shared phenotype conferred by independent gains of common accessory genes. While taxonomically still recognized as separate, this example highlights the challenge of reconciling phenotypic and genomic classifications.

Clear Species Boundaries Revealed by Large-Scale Genomic Analysis

Comprehensive analyses of prokaryotic genomes have fundamentally addressed the question of whether genetic continua or clear species boundaries prevail in the microbial world. A landmark study performing high-throughput ANI analysis of 90,000 prokaryotic genomes revealed clear genetic discontinuities, with 99.8% of approximately 8 billion genome pairs conforming to >95% intra-species and <83% inter-species ANI values [21]. This striking pattern demonstrates that despite horizontal gene transfer, discrete clusters of genetically related individuals prevail across diverse prokaryotic lineages.

The development of FastANI, a rapid algorithm for ANI estimation using alignment-free approximate sequence mapping, has enabled this unprecedented scale of analysis [21]. FastANI achieves near-perfect linear correlation with alignment-based ANI methods while being orders of magnitude faster, making large-scale taxonomic analyses feasible [21]. This approach maintains accuracy for both complete and draft genomes, facilitating the classification of metagenome-assembled genomes that may lack universal marker genes [21]. The robustness of these genetic discontinuities, manifested with or without the most frequently sequenced species, provides compelling evidence for the existence of clear species boundaries in prokaryotes.

Figure 1: Workflow for Genomic Species Delineation Using Average Nucleotide Identity

Methodological Framework: Experimental Approaches and Protocols

Average Nucleotide Identity (ANI) Determination Protocol

The ANI method has emerged as a robust standard for species delineation, closely reflecting the traditional concept of DNA-DNA hybridization relatedness while offering portability and reproducibility [21]. The following protocol outlines the key steps for ANI-based species classification:

Sample Preparation and DNA Extraction

• Cultivate microbial isolates under appropriate conditions ensuring purity
• Extract high-quality genomic DNA using standardized kits or protocols
• Assess DNA quality and quantity through spectrophotometry and fluorometry
• For draft genomes, proceed with library preparation and sequencing

Genome Sequencing and Assembly

• Perform whole-genome sequencing using Illumina, PacBio, or Oxford Nanopore platforms
• Ensure sufficient coverage (typically 50-100x) for high-quality assembly
• Assemble reads into contigs using appropriate assemblers (SPAdes, Canu, Flye)
• Assess assembly quality through metrics (N50, completeness, contamination)

FastANI Analysis

• Download and install FastANI from GitHub repository
• Prepare query and reference genomes in FASTA format
• Run FastANI with command: fastANI -q query_genome.fna -r reference_genome.fna -o output_file
• For database comparisons: fastANI -q query_genome.fna -r genome_directory/ -o output_file
• Interpret results: ≥95% ANI indicates species-level relatedness

Validation and Quality Control

• Compare results with alignment-based methods for validation
• Verify anomalous results through genome synteny visualization
• Cross-reference with phenotypic data when available
• For controversial assignments, supplement with phylogenetic analysis of core genes

Research Reagent Solutions for Genomic Taxonomy

Table 2: Essential Research Reagents and Materials for Genomic Taxonomy Studies

Reagent/Material	Function	Examples/Specifications
DNA Extraction Kits	High-quality genomic DNA isolation	DNeasy Blood & Tissue Kit (QIAGEN), Wizard Genomic DNA Purification Kit (Promega)
Library Preparation Kits	Sequencing library construction	Nextera XT DNA Library Prep Kit (Illumina), SMRTbell Express Template Prep Kit (PacBio)
Sequence Assemblers	Genome assembly from sequencing reads	SPAdes (Illumina), Canu (PacBio), Flye (Oxford Nanopore)
ANI Calculation Tools	Fast genome comparison	FastANI, OrthoANI, PYANI
Quality Control Tools	Assessment of genome completeness and contamination	CheckM, BUSCO, QUAST
Culture Media Components	Prokaryotic cultivation for DNA isolation	Tryptic Soy Broth, Luria-Bertani Medium, specific selective media

Integration and Future Directions

Advancing Taxonomy Through Multi-Omic Data Integration

The future of prokaryotic taxonomy lies in the integration of multi-omic data, combining genomic information with transcriptomic, proteomic, and metabolomic profiles to create a comprehensive understanding of microbial diversity and function [22]. The exponential increase in sequenced genomes - with over 1.9 million bacterial genomes now available - provides unprecedented resolution of prokaryotic genetic diversity [22]. This wealth of data enables comparative analyses that reveal evolutionary relationships and functional adaptations across diverse lineages.

Substantial opportunities exist to enhance taxonomic frameworks through improved data standardization and annotation practices [22]. Current challenges include errors in gene annotation, inconsistent metadata collection, and difficulties in cross-platform comparisons [22]. Addressing these limitations through centralized, automated systems for annotation updates and standardized metadata reporting would significantly advance the field. Machine learning and artificial intelligence offer promising approaches for managing the scale and complexity of prokaryotic genomic data, potentially enabling real-time taxonomy updates as new information emerges [22].

Technological Advances and Computational Innovation

The development of novel computational tools has been instrumental in advancing prokaryotic taxonomy. Methods like FastANI have reduced computational barriers to large-scale genomic comparisons, enabling analyses that were previously impractical [21]. These advances are particularly crucial as the number of available genomes continues to grow exponentially, encompassing both cultivated isolates and metagenome-assembled genomes from diverse environments.

Future taxonomic frameworks will likely incorporate functional genomics approaches connecting genotypic diversity to phenotypic traits [22]. Techniques such as RB-TnSeq (randomly barcoded transposon sequencing) and CRISPRi-seq enable high-throughput functional characterization of genes, providing insights into the genetic basis of ecological specialization and adaptation [22]. Integrating these functional data with genomic taxonomy will create a more nuanced understanding of prokaryotic diversity that reflects both evolutionary relationships and ecological roles.

Figure 2: Multi-Dimensional Data Integration for Modern Prokaryotic Taxonomy

The prokaryotic species concept has evolved substantially from its initial reliance on phenotypic observations to contemporary genomic frameworks. The pangenome paradigm, recognizing the fluid nature of prokaryotic genomes with core and accessory components, has transformed our understanding of microbial diversity [20]. Large-scale genomic analyses have demonstrated that despite this fluidity, clear genetic discontinuities exist among prokaryotic populations, supporting the existence of species-like clusters [21]. The development of robust, scalable methods like ANI analysis has provided practical tools for species delineation that reflect both evolutionary relationships and practical taxonomic needs.

Moving forward, the integration of multi-omic data and continued computational innovation will further refine prokaryotic taxonomy [22]. Standardization of data collection, annotation practices, and metadata reporting will enhance the consistency and utility of taxonomic frameworks [22]. These advances will support diverse applications, from clinical diagnostics to environmental monitoring, by providing a more precise and biologically meaningful classification of prokaryotic diversity. The ongoing synthesis of genomic, functional, and ecological perspectives promises to yield an increasingly comprehensive understanding of prokaryotic species, bridging the gap between operational definitions and biological reality.

The three-domain system represents a fundamental paradigm in modern biological classification, categorizing cellular life into Archaea, Bacteria, and Eukarya based on evolutionary relationships [23]. This model, introduced by Carl Woese, Otto Kandler, and Mark Wheelis in 1990, was revolutionary because it split the previously unified prokaryotes into two distinct domains, Archaea and Bacteria, by emphasizing major differences in their 16S rRNA genes, membrane lipid structure, and antibiotic sensitivity [23] [24]. The system refuted the long-held concept of a unified prokaryotic kingdom and proposed that these three lineages arose separately from an ancestral organism with poorly developed genetic machinery, often termed the last universal common ancestor (LUCA) [23] [24]. While this hypothesis is considered by some to be obsolete due to more recent findings suggesting eukaryotes arose from a fusion within Archaea, it remains a critical framework for discussing the fundamentals of microbial taxonomy and phylogeny [23].

Comparative Analysis of the Three Domains

The distinction between the three domains is grounded in a suite of molecular, biochemical, and structural characteristics. The following table provides a detailed comparison of their defining features.

Table 1: Defining Characteristics of the Three Domains of Life

Characteristic	Domain Bacteria	Domain Archaea	Domain Eukarya
Nuclear Membrane	Absent (Prokaryotic)	Absent (Prokaryotic)	Present (Eukaryotic)
Membrane Lipid Structure	Unbranched chains; Ester linkages	Branched hydrocarbon chains; Ether linkages	Unbranched chains; Ester linkages
Cell Wall Composition	Contains peptidoglycan	No peptidoglycan	Variable (e.g., cellulose, chitin) or absent
RNA Markers	Distinct bacterial rRNA	Unique archaeal rRNA; more similar to eukaryotes	Distinct eukaryotic rRNA
Sensitivity to Antibiotics	Sensitive	Not sensitive	Sensitive
Initial Habitat Association	Moderate environments	Extreme environments (e.g., methanogens, halophiles, thermoacidophiles)	Flexible, cooperative colonies
Pathogenic Members	Many known pathogens	Few known pathogens	Includes pathogens

A key piece of evidence supporting this classification comes from comparing the nucleotide sequences of ribosomal RNAs (rRNA), as these molecules are universal and their structure changes very little over time, making them excellent molecular clocks for phylogeny [24]. The three-domain hypothesis posits that Archaea and Eukarya are sister clades, more closely related to each other than to Bacteria [23]. However, a growing body of phylogenomic analyses now suggests that Eukarya may have branched off from within the Archaea, specifically from a group like the Lokiarchaeota, which encodes an expanded repertoire of eukaryotic signature proteins. This has led to the proposal of a competing two-domain system [23].

Experimental Foundations and Key Methodologies

The establishment of the three-domain system was driven by rigorous methodological advances. Below is a detailed protocol for the foundational experiment of rRNA sequencing.

Table 2: Key Research Reagent Solutions for Phylogenetic Analysis

Research Reagent	Function in Analysis
16S/18S rRNA Primers	Target conserved regions of rRNA genes for PCR amplification and sequencing.
PCR Reagents (Polymerase, dNTPs, Buffers)	Amplify specific rRNA gene fragments from genomic DNA extracts.
Agarose Gel Electrophoresis System	Visualize and verify the size and quantity of amplified PCR products.
Sanger Sequencing Kit	Determine the precise nucleotide sequence of the amplified rRNA genes.
Multiple Sequence Alignment Software (e.g., ClustalW, MUSCLE)	Align sequences from different organisms to identify conserved and variable regions.
Phylogenetic Tree Construction Software (e.g., PHYLIP, RAxML)	Infer evolutionary relationships and calculate phylogenetic trees from aligned sequences.

Protocol 1: rRNA Gene Sequencing and Phylogenetic Analysis This methodology was central to Woese's work and remains a gold standard in microbial phylogenetics [23] [24].

• Nucleic Acid Extraction: Isolate total genomic DNA from pure cultures of the target bacterial, archaeal, and eukaryotic cells. Ensure DNA is free of contaminants.
• Polymerase Chain Reaction (PCR): Amplify the 16S rRNA gene (for prokaryotes) or 18S rRNA gene (for eukaryotes) using universal primers that bind to highly conserved regions of the gene.
• Gel Electrophoresis: Confirm the success and specificity of the PCR reaction by running the products on an agarose gel. A single, bright band of the expected size should be visible.
• Sequencing: Purify the PCR product and subject it to Sanger sequencing to determine the exact order of nucleotides.
• Sequence Alignment: Compile the sequences from diverse organisms and use multiple sequence alignment software to line them up, identifying regions of similarity and variation.
• Phylogenetic Tree Construction: Use computational software to analyze the aligned sequences. The software calculates evolutionary distances and constructs a phylogenetic tree, grouping organisms based on the similarities in their rRNA sequences.

The resulting phylogenetic tree visually represents the evolutionary distances between organisms, providing the quantitative data that underpins the three-domain classification. The stark difference in rRNA sequences between Archaea and Bacteria was the definitive evidence that split the prokaryotes [23].

Visualizing Evolutionary Relationships

The following diagram, created using Graphviz, illustrates the phylogenetic relationships as proposed by the three-domain system and the more recent two-domain system, highlighting the evolutionary position of the Last Universal Common Ancestor (LUCA).

Diagram 1: Competing models of life's evolutionary history.

Data Presentation and Synthesis

The three-domain system organizes the previously established kingdoms into a new, phylogenetically grounded hierarchy. The following table synthesizes this classification and provides representative organisms from each group.

Table 3: Taxonomic Classification within the Three Domains

Domain	Representative Kingdoms / Groups	Key Examples	Distinctive Features / Notes
Archaea	Methanogens, Halophiles, Thermoacidophiles	Methanobacterium, Halobacterium	Exotic metabolisms; thrive in extreme environments; no known pathogens [23] [24].
Bacteria	Cyanobacteria, Spirochaetota, Actinomycetota	Synechococcus, Treponema pallidum	Include many pathogens; more extensively studied than Archaea [23].
Eukarya	Protista, Fungi, Plantae, Animalia	Amoeba, Saccharomyces cerevisiae, Homo sapiens	Cells contain a membrane-bound nucleus; all known non-microscopic organisms [23].

The three-domain system has fundamentally reshaped our understanding of life's diversity, providing a robust phylogenetic framework that highlights the profound evolutionary separation between Archaea and Bacteria. Its core principles continue to guide research in microbial taxonomy and evolution. However, the paradigm is dynamic. Genomic evidence increasingly points to a two-domain system, where Eukarya is embedded within the Archaea, suggesting a complex origin involving cellular fusion or endosymbiosis between an archaeal and bacterial species [23]. This ongoing debate underscores that the tree of life is not a static diagram but a hypothesis that is continually tested and refined with new data, driving forward the fundamentals of phylogeny and microbial research.

Genomic Toolkits and Analytical Pipelines: Modern Methods Defining Microbial Classification

The advent of whole-genome sequencing has revolutionized microbial taxonomy, shifting the paradigm from phenotype-based classification to a genome-based phylogenetic framework. Core genomic metrics—Average Nucleotide Identity (ANI), Average Amino acid Identity (AAI), and Genomic GC content—have emerged as the cornerstone for prokaryotic species delineation and phylogeny. These quantitative measures provide a robust, standardized approach to define taxonomic boundaries, refine the tree of life, and uncover true microbial diversity. This in-depth technical guide elucidates the principles, methodologies, and applications of these core metrics, contextualized within the fundamental research on microbial taxonomy and phylogeny. Designed for researchers and scientists, this document provides detailed experimental protocols, data interpretation guidelines, and practical tools to integrate genomic metrics into modern taxonomic workflows.

Microbial systematics is undergoing a profound transformation, driven by the accessibility of whole-genome sequencing. Traditional methods reliant on morphological, physiological, and biochemical characteristics are now supplemented and often replaced by genomic analyses that offer unparalleled resolution [25]. This shift enables a taxonomy based firmly on evolutionary relationships, substantially revising the tree of life by conservatively removing polyphyletic groups and normalizing taxonomic ranks based on relative evolutionary divergence [26]. In this genomic framework, Average Nucleotide Identity (ANI), Average Amino acid Identity (AAI), and Genomic GC content have become indispensable tools. They provide the numerical foundation for species definition, genus delimitation, and the exploration of genomic adaptation, thereby forming the essential toolkit for researchers engaged in microbial taxonomy, drug discovery, and biodiversity studies.

Core Genomic Metrics: Definitions and Thresholds

Average Nucleotide Identity (ANI)

Average Nucleotide Identity (ANI) is a computational substitute for wet-lab DNA-DNA hybridization (DDH). It calculates the average nucleotide identity of orthologous genomic sequences shared between two organisms. A key strength is its correlation with traditional DDH, with an ANI of 95-96% corresponding to the standard 70% DDH threshold for species delineation [27] [28]. Methods like KmerFinder, which examines co-occurring k-mers, have demonstrated high accuracy (93-97%) in species identification using whole-genome data [27].

Average Amino acid Identity (AAI)

Average Amino acid Identity (AAI) extends the concept of identity to the protein level. It measures the average identity of amino acids in orthologous protein-coding genes between two organisms. AAI is particularly valuable for delineating genera and higher taxonomic ranks. Similar to ANI, a cutoff of 95% AAI is often used as a boundary for species definition [29]. Furthermore, genomic studies routinely use digital DNA-DNA hybridization (dDDH), with a value below 70% supporting the designation of distinct species [29].

Genomic GC Content

Genomic GC content—the percentage of guanine (G) and cytosine (C) nucleotides in a genome—is a traditional taxonomic character given new context by genomics. While useful, GC content alone is not a definitive metric for species delineation due to its variability. However, significant differences in GC content can support the separation of taxa, and it is a critical factor in understanding genomic adaptation and bias in sequencing techniques [30] [31]. For instance, the Nesterenkonia genus displays a high genomic GC content range of 64–72%, and within it, the polar-adapted NES-AT subclade shows significantly different GC content, indicating adaptation to extreme environments [32].

Table 1: Standard Thresholds for Genomic Species Delineation

Metric	Species Boundary	Typical Genus-Level Range	Primary Application
Average Nucleotide Identity (ANI)	95-96%	~80-95%	Primary species delineation
Average Amino acid Identity (AAI)	94-95%	~70-95%	Species & genus delineation
digital DNA-DNA Hybridization (dDDH)	70%	<70%	Species delineation (gold standard)
16S rRNA Gene Identity	98.65-99%	~94-98%	Preliminary genus/species screening
Gene Content Dissimilarity	0.2	0.2-0.4	Subspecies & strain classification [28]
3-Chloro-5-(4-fluorophenyl)aniline	3-Chloro-5-(4-fluorophenyl)aniline, MF:C12H9ClFN, MW:221.66 g/mol	Chemical Reagent	Bench Chemicals
N-boc-carbazole-3-carboxaldehyde	N-boc-carbazole-3-carboxaldehyde, MF:C18H17NO3, MW:295.3 g/mol	Chemical Reagent	Bench Chemicals

Experimental and Computational Methodologies

Genome Sequencing, Assembly, and Annotation

A high-quality genome assembly is the foundational requirement for accurate calculation of genomic metrics.

• DNA Extraction & Library Preparation: Use kits designed for Gram-positive or Gram-negative bacteria (e.g., MasterPure Gram Positive DNA Purification Kit) [29]. Assess DNA quality and quantity via Nanodrop spectrophotometry and agarose gel electrophoresis. For library construction, genomic DNA is fragmented (e.g., via Covaris S2 sonication), end-repaired, adenylated, and ligated to sequencing adaptors.
• Sequencing & Assembly: Sequencing is performed on platforms like Illumina NovaSeq or HiSeq X Ten [32] [29]. Adapter sequences and low-quality reads are removed using tools like Trimmomatic [32]. High-quality reads are assembled into scaffolds using assemblers such as SPAdes [32].
• Quality Control & Annotation: Assess assembly completeness and contamination with CheckM [32] [29]. Annotate the assembled genome using pipelines like PROKKA or the RAST (Rapid Annotation using Subsystem Technology) server to identify protein-coding genes, tRNAs, and rRNAs [32] [29].

Figure 1: Workflow for Genome Sequencing and Analysis for Taxonomy.

Calculating Average Nucleotide Identity (ANI) and Average Amino acid Identity (AAI)

ANI Calculation: ANI is typically calculated using tools such as FastANI [32] or the ANI calculator from the enveomics collection [33]. These tools compare two genome sequences by breaking them into fragments, finding the best matches between them, and calculating the average nucleotide identity of these orthologous regions.

AAI Calculation: AAI is computed using the AAI calculator [33] or the AAI-Matrix tool for all-vs-all comparisons within a dataset [33]. This involves comparing the proteomes (predicted protein sequences) of two organisms. Orthologous proteins are identified, and the average identity of their amino acid sequences is calculated.

Table 2: Essential Computational Tools for Genomic Taxonomy

Tool Name	Function	Key Feature	Access
FastANI	ANI calculation	Fast, alignment-free; reference-based	https://github.com/ParBLiSS/FastANI
Enveomics (ANI/AAI)	ANI/AAI calculator	Distribution of identity; fragment-based	http://enve-omics.ce.gatech.edu/ [33]
OrthoFinder	Orthologous groups identification	Infers orthogroups for AAI/phylogeny	https://github.com/davidemms/OrthoFinder
CheckM	Genome completeness/contamination	Uses lineage-specific marker genes	https://github.com/Ecogenomics/CheckM
KmerFinder	Species identification from WGS	k-mer based; high accuracy	https://cge.food.dtu.dk/services/KmerFinder/
MyTaxa	Taxonomy assignment	Handles metagenomic fragments	http://enve-omics.ce.gatech.edu/mytaxa [33]

Accounting for GC Content and Sequencing Bias

GC content can be calculated from the assembled genome using basic bioinformatics scripts or toolkits like seqkit [32]. However, it is crucial to recognize that GC content bias is a major issue in whole-genome sequencing. Regions with extremely high or low GC content are often underrepresented in sequencing data due to challenges in PCR amplification and sequencing enzyme efficiency [34] [31]. This can lead to gaps in coverage and inaccurate GC content measurement.

Mitigation Strategies:

• Library Preparation: Use PCR-free workflows or polymerases engineered for high GC content to reduce amplification bias [31].
• Bioinformatic Correction: Employ tools like Picard or MultiQC to assess coverage uniformity and GC bias. Bioinformatics normalization algorithms can computationally correct for these biases after sequencing [31].

A Practical Case Study: Genomic Delineation ofSchaaliaSpecies

A recent study on oral Actinomyces provides a exemplary model for the application of these core metrics [29]. Strains NCTC 9931 and C24, previously classified as Actinomyces odontolyticus, were re-evaluated using a genomic approach.

• Genome Features: Both strains had a genome size of ~2.3 Mbp and a GC content of 65.5%.
• Phylogenetic Analysis: A core genome SNPs phylogenetic tree was constructed using the PGAP pangenome pipeline, placing NCTC 9931 and C24 within the genus Schaalia but as distinct from known species.
• Overall Genome Relatedness Index: This was the crucial step for species delineation. The study calculated:
  • dDDH: Values below 70% when compared to the nearest type strain (S. odontolytica NCTC 9935^T^).
  • ANI and AAI: Values below 95% when compared to S. odontolytica NCTC 9935^T^.
• Taxonomic Proposal: Based on these genomic metrics, strain NCTC 9931 was proposed as the novel species Schaalia dentiphila sp. nov., and the highly similar yet distinct strain C24 was proposed as a novel subspecies, Schaalia dentiphila subsp. denticola subsp. nov.

This case underscores how ANI, AAI, and dDDH provide the quantitative evidence required for robust taxonomic decisions, even for closely related organisms.

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Kits and Reagents for Genomic Taxonomy Workflows

Reagent / Kit	Function in Workflow	Specific Example / Note
MasterPure Gram Positive DNA Purification Kit	High-quality DNA extraction from bacterial cells.	Critical for difficult-to-lyse Gram-positive bacteria [29].
NEBNext Ultra DNA Library Prep Kit	Preparation of sequencing libraries for Illumina platforms.	Standardized protocol for consistent library construction [32].
Covaris S2 Sonication System	Mechanical fragmentation of genomic DNA.	Provides more uniform fragmentation compared to enzymatic methods, reducing bias [31].
Brain Heart Infusion (BHI) & Yeast Extract	Routine cultivation and maintenance of bacterial strains.	BHYE broth (BHI + Yeast Extract) used for growing Schaalia strains anaerobically [29].
PCR Bias-Reduction Kits	Polymerases and kits designed for uniform amplification.	Kits with enzymes engineered to amplify GC-rich templates improve coverage [31].
Copper neodecanoate	Copper neodecanoate, CAS:68084-48-0, MF:C20H38CuO4, MW:406.1 g/mol	Chemical Reagent
Benzyl 4-acetyl-2-methylbenzoate	Benzyl 4-acetyl-2-methylbenzoate, MF:C17H16O3, MW:268.31 g/mol	Chemical Reagent

The standardization of microbial taxonomy around genome-based phylogeny has fundamentally revised our understanding of the bacterial tree of life [26]. Within this framework, ANI, AAI, and GC content stand as the core genomic metrics for definitive species delineation and phylogenetic placement. While ANI and dDDH provide the primary species boundary definitions, and AAI helps delineate higher taxa, GC content remains a valuable descriptive and diagnostic character. As sequencing technologies evolve and bioinformatic tools become more sophisticated, the precise and quantitative application of these metrics will continue to be paramount for researchers in microbiology, ecology, and drug development, enabling the discovery and correct classification of the vast, uncharted microbial diversity.

The accurate classification and phylogenetic reconstruction of microorganisms are fundamental to advancing research in microbial ecology, pathogenesis, and drug development. For decades, 16S ribosomal RNA (rRNA) gene sequencing has served as the cornerstone of microbial taxonomy and phylogeny. However, the limitations of this single-gene approach in discriminating closely related species have prompted the development of more robust methods. Multilocus Sequence Analysis (MLSA) has emerged as a powerful alternative, leveraging the concatenated sequences of multiple housekeeping genes to provide superior phylogenetic resolution [35]. This technical guide examines the enduring role of 16S rRNA sequencing while highlighting the transformative potential of MLSA in modern microbial systematics.

The 16S rRNA Gene: Workhorse of Microbial Identification

Fundamental Principles and Historical Context

The 16S rRNA gene is approximately 1,500 nucleotides long and is an integral component of the 30S small subunit of prokaryotic ribosomes [36]. Its utility as a phylogenetic marker stems from its universal distribution across bacteria and archaea, combined with a mosaic of highly conserved regions alternating with hypervariable regions. The conserved regions facilitate universal primer binding and alignment across diverse taxa, while the variable regions provide species-specific signature sequences that enable differentiation [37]. Carl Woese and George Fox pioneered the use of 16S rRNA for phylogenetic studies in the 1970s, establishing the three-domain system of life (Bacteria, Archaea, and Eucarya) that revolutionized our understanding of evolutionary relationships [36] [37].

Methodological Approach and Protocol

Standard 16S rRNA gene sequencing for phylogenetic analysis involves several critical steps that must be meticulously optimized for reliable results:

• DNA Extraction and Purification: Cell lysis followed by nucleic acid purification to obtain high-quality, inhibitor-free DNA suitable for amplification [36].
• PCR Amplification: Using universal primers targeting conserved regions flanking the V3-V4 hypervariable regions. A typical reaction includes:
  • Template DNA: 1-10 ng
  • Primers: 0.2-0.5 µM each
  • PCR Mix: Standard Taq polymerase, dNTPs, buffer with MgClâ‚‚
  • Cycling Conditions: Initial denaturation (95°C for 3 min); 30-35 cycles of denaturation (95°C for 30 s), annealing (55°C for 30 s), extension (72°C for 1 min); final extension (72°C for 5 min) [36]
• Sequence Analysis: Purified PCR products are sequenced using Sanger or next-generation sequencing platforms. For Sanger sequencing, the resulting chromatograms must be edited and assembled before comparison with reference databases using tools like BLAST [38] [36].
• Phylogenetic Analysis: Processed sequences are aligned, and phylogenetic trees are reconstructed using neighbor-joining, maximum likelihood, or Bayesian methods [39].

Figure 1: 16S rRNA Gene Sequencing Workflow

Applications and Limitations in Modern Microbiology

16S rRNA sequencing remains indispensable in clinical and environmental microbiology, with several key applications:

• Identification of Non-cultivable Pathogens: The approach successfully identified Tropheryma whipplei as the causative agent of Whipple's disease when conventional culture methods failed [37].
• Clinical Diagnostics: Provides genus-level identification of disease-causing bacteria in over 90% of cases, directly influencing treatment decisions, particularly for immunocompromised patients [37].
• Microbiome Studies: Serves as the primary tool for characterizing microbial community composition in diverse habitats through amplicon sequencing [40].

Despite its utility, 16S rRNA sequencing faces significant limitations. The gene often lacks sufficient evolutionary divergence to discriminate closely related species due to high sequence similarities (>98.65%) between distinct taxonomic groups [39] [36] [41]. This results in unstable phylogenetic topologies with low bootstrap values, particularly for outer branches [39]. Additionally, the presence of multiple heterogeneous copies within a single genome and occasional horizontal gene transfer events can further complicate phylogenetic interpretations [36].

Multilocus Sequence Analysis: Advancing Phylogenetic Resolution

Conceptual Framework and Development

Multilocus Sequence Analysis (MLSA) addresses the limitations of single-gene approaches by analyzing the concatenated sequences of multiple housekeeping genes (HKGs). Originally adapted from Multilocus Sequence Typing (MLST) used in epidemiological studies, MLSA has evolved into a robust taxonomic tool for delineating species boundaries and establishing reliable phylogenetic frameworks [35]. The method is based on the principle that the evolutionary history of multiple essential genes collectively represents the organism's genomic history more accurately than any single marker [39] [35] [41].

MLSA Scheme Design and Implementation

Gene Selection Criteria

The selection of appropriate housekeeping genes is critical for developing a robust MLSA scheme. Ideal candidates exhibit the following properties:

• Ubiquitous distribution across the taxonomic group of interest
• Essential metabolic function to ensure strong selective constraints
• Moderate sequence conservation with sufficient phylogenetic signal
• Low recombination frequency to preserve vertical inheritance patterns
• Physical separation on the chromosome to represent independent loci

For the genus Shewanella, a validated MLSA scheme incorporates six housekeeping genes: gyrA, gyrB, infB, recN, rpoA, and topA [39]. Similarly, studies on Salinivibrio have utilized gyrB, recA, rpoA, and rpoD [41]. The table below summarizes the characteristics of these commonly used genetic markers.

Table 1: Characteristics of Housekeeping Genes Used in MLSA Schemes

Gene	Function	Sequence Length (bp)	Evolutionary Rate	Taxonomic Utility
gyrB	DNA gyrase subunit B	1,110-1,119	High	Genus/species discrimination
rpoA	RNA polymerase alpha subunit	615	Moderate	Species-level phylogeny
rpoD	RNA polymerase sigma factor	Variable	High	Species/complex discrimination
recA	Recombinase A	Variable	Moderate	Species delineation
gyrA	DNA gyrase subunit A	498	High	Species-level discrimination
infB	Translation initiation factor 2	663	Moderate	Genus/species discrimination

Standardized MLSA Protocol

A comprehensive MLSA workflow involves multiple standardized steps to ensure reproducibility and phylogenetic accuracy:

• Gene Amplification and Sequencing: Individual housekeeping genes are amplified using specifically designed primers. Reaction conditions are optimized for each gene target through temperature gradient PCR and primer validation.
• Sequence Editing and Alignment: Raw sequences are edited to remove low-quality regions and aligned using algorithms such as MUSCLE or MAFFT. Coding sequences are translated to amino acids to confirm the absence of pseudogenes.
• Concatenation and Phylogenetic Reconstruction: Edited sequences for each locus are concatenated in a fixed order to create a supermatrix. Phylogenetic trees are reconstructed using maximum likelihood and neighbor-joining methods with appropriate substitution models. Bootstrap analysis (1,000 replicates) typically provides support for branch nodes [39] [41].

Figure 2: Multilocus Sequence Analysis (MLSA) Workflow

Quantitative Advantages of MLSA Over Single-Gene Approaches

The enhanced resolution of MLSA stems from fundamental quantitative advantages in sequence information content. The following table compares the performance metrics between 16S rRNA sequencing and MLSA for the genus Shewanella based on analysis of 59 type strains [39].

Table 2: Performance Comparison Between 16S rRNA and MLSA for Shewanella Phylogenetics

Parameter	16S rRNA Gene	MLSA (6-gene concatenation)
Total Length (bp)	1,434	4,176-4,191
Parsimony Informative Sites	148 (10.3%)	2,046 (48.8%)
Nucleotide Diversity (Pi)	0.043	0.223
Mean Interspecies Similarity	95.0%	77.7%
Similarity Range	89.8-100%	71.1-99.9%
Ka/Ks Ratio	Not Applicable	0.143

The data demonstrate that MLSA provides substantially more phylogenetic information through increased sequence length and a higher proportion of parsimony-informative sites (48.8% vs. 10.3%). The greater nucleotide diversity (0.223 vs. 0.043) and wider similarity range significantly enhance the ability to discriminate between closely related species [39]. The Ka/Ks ratio of 0.143 indicates purifying selection, confirming the appropriateness of these housekeeping genes for robust phylogenetic inference.

Comparative Analysis: Resolution and Reliability in Phylogenetic Studies

Case Studies Across Bacterial Genera

The superior discriminatory power of MLSA has been demonstrated across diverse bacterial genera, resolving taxonomic relationships that remained ambiguous with 16S rRNA sequencing alone:

• Genus Shewanella: MLSA of six housekeeping genes revealed twelve distinct monophyletic clades and identified misclassified species, including Shewanella upenei as a synonym of S. algae and Shewanella pacifica as a synonym of Shewanella japonica [39].
• Genus Salinivibrio: MLSA based on four protein-coding genes (gyrB, recA, rpoA, rpoD) clearly differentiated strains into four phylogroups, one representing a novel species. The concatenated sequence similarity of 97% correlated well with the 70% DNA-DNA hybridization threshold for species delineation [41].
• Family Vibrionaceae: MLSA provided significantly improved resolution compared to 16S rRNA gene analysis, which often fails to differentiate among closely related members of this family [41].

Concordance with Genomic Standards

MLSA demonstrates strong concordance with whole-genome sequence analyses, validating its position as a robust phylogenetic method that bridges single-gene and comprehensive genomic approaches:

• Topological Congruence: Phylogenies based on MLSA show nearly identical topology to trees constructed from core genome sequences, with only minor differences in poorly supported branches [39].
• Species Delineation Correlation: MLSA similarity thresholds consistently correlate with established species boundaries defined by DNA-DNA hybridization (DDH). For Salinivibrio, a 97% concatenated sequence similarity cutoff corresponds to the 70% DDH species threshold [41].
• Population Genetics: The multi-gene approach of MLSA provides insights into population structure and evolutionary relationships that are consistent with more computationally intensive genomic analyses [39].

Table 3: Essential Research Reagents for Phylogenetic Reconstruction Methods

Reagent/Resource	Function	Application Examples
Universal 16S rRNA Primers	Amplification of conserved regions flanking V1-V9 variable regions	27F/1492R; 515F/806R (V4 region)
Housekeeping Gene Primers	Taxon-specific amplification of MLSA loci	gyrB, rpoA, rpoD, recA genus-specific primers
High-Fidelity DNA Polymerase	Accurate amplification with minimal error rates	PCR of target genes for sequencing
DNA Sequencing Kit	Cycle sequencing for Sanger platforms	BigDye Terminator chemistry
Sequence Database	Reference repository for comparative analysis	NCBI, SILVA, RDP, GTDB
Sequence Alignment Software	Multiple sequence alignment for phylogenetic analysis	MUSCLE, MAFFT, ClustalW
Phylogenetic Analysis Package	Tree reconstruction and evolutionary analysis	MEGA, PHYLIP, RAxML

Future Directions: Integration with Next-Generation Sequencing

The field of microbial phylogenetics is rapidly evolving with the integration of next-generation sequencing (NGS) technologies. While Sanger sequencing remains the standard for single-gene approaches, NGS platforms enable more comprehensive analyses:

• Full-Length 16S rRNA Sequencing: Long-read technologies (Nanopore, PacBio) permit complete 16S rRNA gene sequencing, overcoming the limitations of short-read partial gene analysis [38].
• Metagenome-Assembled Genomes (MAGs): Advanced binning algorithms applied to complex environmental samples are recovering thousands of previously undescribed microbial genomes, dramatically expanding phylogenetic databases [40].
• Hybrid Approaches: Combining 16S rRNA for initial classification with MLSA for finer resolution provides a balanced strategy for comprehensive phylogenetic analysis [41].

Recent studies demonstrate that NGS-based 16S rRNA sequencing outperforms Sanger sequencing in clinical diagnostics, with positivity rates of 72% versus 59% respectively, and superior detection of polymicrobial infections [38]. The ongoing development of long-read sequencing technologies and associated bioinformatics tools will further enhance our ability to recover high-quality genomes from complex environments, providing unprecedented insights into microbial diversity and evolution [40].

Both 16S rRNA sequencing and Multilocus Sequence Analysis play complementary but distinct roles in modern microbial phylogenetics. While 16S rRNA remains a valuable tool for initial genus-level identification and analysis of diverse microbial communities, MLSA provides significantly enhanced resolution for species delineation and phylogenetic reconstruction of closely related taxa. The integration of these methods with next-generation sequencing technologies and whole-genome approaches will continue to advance our understanding of microbial evolution and diversity, with profound implications for clinical diagnostics, drug development, and environmental microbiology. As the field progresses, MLSA is poised to become the standard for robust taxonomic classification, particularly for problematic genera where single-gene approaches prove inadequate.

The rapid expansion of publicly available genome sequences presents an unprecedented opportunity to resolve long-standing questions in evolutionary biology. For microbial taxonomy and phylogeny, the construction of a robust Tree of Life has been a central goal, yet it is fraught with methodological challenges. The evolutionary history of any genome is not strictly hierarchical but includes elements of gene duplication, gene loss, and horizontal gene transfer (HGT), which can confound phylogeny reconstruction [42]. Historically, single-gene trees, particularly those of the 16S rRNA gene, served as proxies for organismal phylogeny. However, the genome era necessitates methods that can synthesize information from across the entire genome.

This technical guide focuses on the integration of whole-genome sequencing (WGS) and supertree analysis as a powerful approach for building a robust genomic backbone. This backbone is essential for a wide array of applications, from assigning taxonomy to metagenomic data and inferring co-speciation events to identifying ecological trends [43]. We outline the theoretical underpinnings, provide detailed experimental and computational protocols, and discuss state-of-the-art tools that enable researchers to infer accurate species trees in the face of complex genomic data.

Theoretical Foundation: From Gene Trees to Species Trees

A fundamental concept in phylogenomics is the distinction between a gene tree and a species tree. A gene tree represents the evolutionary history of a single gene, which can differ from the species tree due to incomplete lineage sorting, gene duplication, and HGT [42]. The multispecies coalescent (MSC) model provides a theoretical framework for understanding these discordances.

Two primary paradigms exist for inferring species trees from genomic data:

• Supermatrix Approach (Concatenation): This method involves concatenating alignments of multiple genes into a single, large alignment from which a species tree is inferred. It assumes all genes share the same evolutionary history, an assumption often violated in prokaryotes [43].
• Supertree Approach: This method involves inferring trees for individual genes and then amalgamating these gene trees into a single species tree. This approach can better accommodate the mosaic of histories across a genome but requires methods robust to discordance [42] [43].

For prokaryotes, where HGT is common, the supertree approach, particularly with methods that account for discordance, is often favored. The goal is not to produce a tree that reflects the history of every gene but to establish a dominant vertical signal that can serve as a reference framework [44] [43].

Whole-Genome Sequencing: Experimental Protocols and Data Generation

The quality of any phylogenetic inference is contingent on the quality of the input genomic data. The following section details the protocols for generating high-quality genome assemblies.

Sample Preparation and Sequencing

A combination of long-read and short-read sequencing technologies is recommended for generating complete, chromosome-level assemblies.

• DNA Extraction: Use kits designed for high-molecular-weight DNA to minimize shearing. Purity and integrity should be verified using agarose gel electrophoresis and spectrophotometry (e.g., Nanodrop).
• Library Preparation and Sequencing:
  • PacBio CLR Sequencing: Provides long reads (average length >10 kb) crucial for spanning repetitive regions and resolving complex genomic structures. Typical coverage: ≥50x [45].
  • Illumina Whole-Genome Short-Read (WGS-SR) Sequencing: Provides high-accuracy short reads for polishing the long-read assembly. Typical coverage: ≥50x [45].
  • Illumina Omni-C Sequencing: A chromatin conformation capture technique that generates data for scaffolding assemblies to chromosome-level. Typical coverage: ≥50x [45].
  • RNA Sequencing (RNAseq): Provides data for genome annotation. Typical coverage: ≥20x [45].

Table 1: Example Sequencing Data Output for a Chromosome-Level Assembly (Styela plicata) [45]

Sequencing Technology	Raw Data (Gb)	Filtered Data (Gb)	Mean Coverage
PacBio CLR	180.00	46.17	78X
Illumina WGS-SR	30.08	24.75	58X
Illumina Omni-C	47.58	45.12	N/A
Illumina RNAseq	33.01	16.08	N/A

Genome Assembly and Annotation

The process of converting raw sequencing reads into an annotated genome involves several steps, as visualized below.

Diagram 1: Genome assembly and annotation workflow.

• Quality Control: Use tools like FastQC to assess read quality. Adapters and low-quality bases should be trimmed using tools like Trimmomatic.
• De Novo Assembly: Assemble long-reads (PacBio/Oxford Nanopore) using assemblers like Flye or Canu to generate an initial draft assembly.
• Polishing: Polish the long-read assembly using high-accuracy short-reads (Illumina) with tools like Pilon to correct indels and base errors.
• Scaffolding: Use Omni-C or similar data with a scaffolder like HiRise to order and orient contigs into chromosomes, resulting in a chromosome-level assembly [45].
• Genome Annotation:
  • Repeat Masking: Identify and mask repetitive elements using RepeatModeler and RepeatMasker.
  • Gene Prediction: Predict protein-coding genes using a combination of ab initio predictors and evidence from RNAseq alignments and protein homologs. Tools like Funannotate (for eukaryotes) or Prokka (for prokaryotes) automate this pipeline [45].
  • Functional Annotation: Assign functions to predicted genes by comparing them to databases like Pfam, InterPro, and NCBI's non-redundant database.

Supertree Analysis: Computational Methodologies for Phylogenomic Inference

With annotated genomes, the next step is to infer the species phylogeny. The following section details the supertree approach.

Ortholog Identification and Alignment

The first step is to identify a set of conserved, single-copy genes present across all taxa of interest.

• Ortholog Identification: Use a tool like OrthoFinder or a custom greedy algorithm based on BLASTP to cluster genes into orthologous groups. A typical approach involves using an E-value cutoff of 10⁻⁸ and removing clusters with more than one sequence per species (paralogs) [42] [43].
• Multiple Sequence Alignment: For each orthologous cluster, perform a multiple sequence alignment using a tool like MUSCLE or MAFFT.
• Alignment Refinement: Trim poorly aligned regions and gaps from the alignments using a tool like Gblocks. This step is critical for reducing noise [42].

Table 2: Exemplar Workflow for Ortholog Identification in 22 Archaeal Genomes [42]

Analysis Step	Input	Tool/Method	Output
Gene Family Identification	22 Archaeal Proteomes	Greedy BLASTP Algorithm (E-value < 10⁻⁸)	14,673 Gene Families
Paralogue Removal	14,673 Families	Remove families with >1 gene/species	13,018 Single-Copy Families
Informative Gene Selection	13,018 Families	Retain families with ≥4 species	1,154 Phylogenetically Informative Families
Alignment & Refinement	1,154 Families	ClustalW + Gblocks	594 High-Quality Alignments

Single-Gene Tree Inference

For each of the refined alignments, infer a phylogenetic tree using a statistically robust method.

• Model Selection: Use software like ModelTest (for DNA) or ProtTest (for proteins) to determine the best-fit substitution model for each alignment.
• Tree Building: For each gene alignment, infer a Maximum Likelihood (ML) tree using software like RAxML or IQ-TREE. It is good practice to perform non-parametric bootstrapping (e.g., 100 replicates) to assess the confidence of each node in the gene tree [43].

Supertree Construction from Gene Trees

The final step is to combine the individual gene trees into a single species tree. Several methods exist, with a key distinction being how they handle discordance.

• Bayesian Concordance Analysis (e.g., BUCKy): This method estimates a "primary concordance" tree from a collection of gene trees. It is agnostic to the cause of incongruence (HGT, ILS, etc.) and accounts for uncertainty in individual gene tree topologies [43].
• Discordance-Aware Coalescent Methods (e.g., ASTRAL, ASTRAL-Pro): These methods are statistically consistent under the multi-species coalescent model and are highly accurate. ASTRAL-Pro can handle multi-copy genes, which eliminates the error-prone step of requiring strict single-copy orthologs [46].

A modern alternative to this multi-step process is to use a fully automated pipeline like ROADIES. This tool randomly samples loci of a fixed length from input genome assemblies, infers gene trees from these loci, and then uses ASTRAL-Pro to infer the species tree. This "Reference-free, Orthology-free, Annotation-free" approach eliminates the need for gene annotation and orthology inference, saving significant time and computational resources [46].

Diagram 2: Two computational paths for species tree inference.

The Scientist's Toolkit: Essential Research Reagents and Software

Table 3: Key Research Reagent Solutions for WGS and Supertree Analysis

Item Name	Function/Application	Technical Specifications
PacBio Sequel II/Revio System	Long-read sequencing for high-quality genome assemblies.	Read lengths >10 kb, high consensus accuracy.
Illumina NovaSeq 6000 System	High-throughput short-read sequencing for assembly polishing and RNAseq.	Output up to 6 Tb, read lengths 150-300 bp.
Dovetail Omni-C Kit	Generation of chromatin interaction data for genome scaffolding.	Enables chromosome-level scaffolding.
Flye	Software for de novo assembly of long reads.	Resolves complex repeats, produces high-quality drafts.
Funannotate	Integrated pipeline for eukaryotic genome annotation.	Combies gene prediction, functional annotation, and classification.
OrthoFinder	Software for comparative genomics and orthology inference.	Accurately infers orthologs and gene trees.
RAxML-NG	Tool for phylogenetic inference under Maximum Likelihood.	Handles large datasets and provides bootstrapping.
ASTRAL-Pro	Software for species tree estimation from multi-copy gene trees.	Accounts for gene duplication and loss; discordance-aware.
ROADIES	Fully automated pipeline for species tree inference from genome assemblies.	Reference-free, orthology-free, and annotation-free [46].
(R)-1-Cyclobutylpiperidin-3-amine	(R)-1-Cyclobutylpiperidin-3-amine, MF:C9H18N2, MW:154.25 g/mol	Chemical Reagent

The integration of high-quality whole-genome sequencing and discordance-aware supertree analysis provides a robust framework for building a reliable genomic backbone. This guide has outlined the critical steps, from generating chromosome-level assemblies to inferring a species tree that captures the dominant vertical signal amidst the complexity of gene-level evolution. As sequencing technologies continue to advance and computational methods become more sophisticated and automated, the scientific community is poised to resolve ever-deeper branches in the Tree of Life, fundamentally advancing our understanding of microbial taxonomy and phylogeny.

Genome-to-Genome Distance Calculator (GGDC) and Digital DDH

The classification and identification of microorganisms have been fundamentally transformed by the advent of genomic sequencing data. Traditional wet-lab DNA-DNA hybridization (DDH), once the gold standard for prokaryotic species delineation, has been largely supplanted by in silico, genome-based computational methods that offer greater precision, reproducibility, and scalability [47]. Among these, the Genome-to-Genome Distance Calculator (GGDC) represents a cornerstone methodology for estimating DDH values computationally, facilitating a robust, sequence-based approach to microbial taxonomy and phylogeny [48].

This technical guide provides an in-depth examination of the GGDC methodology and its role in digital DDH. It is situated within the broader context of a paradigm shift in microbial systematics, where genome-scale data is continually refining our understanding of phylogenetic relationships and taxonomic boundaries [49] [47]. The subsequent sections will detail the underlying principles, experimental protocols, and practical applications of the GGDC, providing researchers with the frameworks necessary to implement these analyses in their own work.

The Shift to Genome-Based Taxonomy

Microbial taxonomy is undergoing a profound evolution, moving from phenotypic assessments and single-gene analyses (e.g., 16S rRNA) toward comprehensive genome-based classifications [47]. This transition is driven by the recognition that a multi-gene approach provides a more accurate reflection of evolutionary history and species boundaries.

• The Limitations of Traditional DDH: The historical DDH method measured the overall sequence similarity between two genomes under standardized hybridization conditions. While it established a pragmatic species boundary (typically 70% similarity), it was laborious, difficult to standardize, and provided low resolution for clarifying relationships at higher taxonomic ranks [47].
• The Rise of Digital Alternatives: The explosion of microbial genome sequencing created both a need and an opportunity for computational replacements. Methods like Average Nucleotide Identity (ANI) and digital DDH, as implemented by the GGDC, have emerged as the new standards. These in silico techniques leverage entire genome sequences, offering high throughput, objective reproducibility, and the ability to be re-evaluated as databases grow [49].
• Integration with Modern Taxonomy: Genome-based taxonomy now often relies on a consensus of methods. The ANI (with a species threshold of ≥95%) and the digital DDH (with a species threshold of ≥70%) are frequently used in conjunction with phylogenetic analyses of core genes to establish a coherent and stable taxonomic framework [50] [47].

The GGDC Methodology: Principles and Workflow

The GGDC is a sophisticated algorithm that translates the wet-lab DDH process into a computational model. Its core principle involves comparing two genome sequences to estimate the digital DDH value and associated confidence intervals, providing a probabilistic assessment of whether two organisms belong to the same species [48].

Core Computational Workflow

The following diagram illustrates the primary workflow for conducting a digital DDH analysis using the GGDC.

Detailed Methodological Steps

• Genome Input and Preprocessing: The process begins with the submission of two genome sequences. The GGDC accepts data in the form of FASTA files or GenBank accession numbers. Using FASTA files is generally recommended for speed, as retrieving data from GenBank can be slow [48]. The tool then performs an all-against-all comparison of the genomic sequences.

• High-Scoring Segment Pairs (HSP) Identification: The GGDC uses the BLAST algorithm to identify all High-Scoring Segment Pairs (HSPs) between the two genomes. These HSPs represent local regions of significant sequence alignment. The tool carefully filters these alignments to ensure high quality, discarding those with low complexity or that may be repetitive in nature.

• Distance Calculation Using Models: The GGDC employs not one, but three distinct formulas (Model 1, 2, and 3) to calculate the digital DDH value from the HSP data. Each model makes different assumptions, providing a robust estimation framework [48].

  • Model 1 is based on the identity of the HSPs.
  • Model 2 considers the length of the HSPs relative to the total genome length.
  • Model 3 is a hybrid approach that incorporates both identity and coverage information. The use of multiple models allows researchers to assess the consistency of the result and provides a more nuanced interpretation, especially for borderline cases.

• Statistical Evaluation and Confidence Intervals: A critical feature of the GGDC is its provision of confidence intervals for the estimated DDH values. This is achieved through a resampling method (e.g., bootstrapping), which assesses the reliability of the point estimate. The calculator also provides a probability value indicating the likelihood that the two strains belong to the same species.

GGDC in the Broader Genomic Distance Landscape

The GGDC is one of several powerful tools available for genomic comparison. Understanding its position relative to other methods is key for selecting the appropriate tool for a given research question.

Table 1: Comparison of Genome-Based Taxonomic and Distance Tools

Tool	Core Methodology	Primary Output	Key Application	Typical Species Threshold
GGDC [48]	Alignment-based (BLAST); digital simulation of DDH	Digital DDH value & probability	Species delineation, replacement for wet-lab DDH	≥70%
FastANI [51]	Alignment-free; uses Mash for ANI approximation	Average Nucleotide Identity (ANI)	Rapid species-level comparison, large-scale genomics	≥95%
Mash [52]	Alignment-free; MinHash sketching of k-mers	Mash distance (correlates with 1-ANI) & P-value	Ultra-fast clustering, metagenomic sample comparison	Distance ≤0.05 (≈ ANI ≥95%)
dna2bit [51]	Alignment-free; feature hashing & Hamming distance	Bit distance (correlates with 1-ANI)	High-speed analysis of SAGs and large metagenomes	N/A

The relationship between these different tools and their outputs can be conceptualized within a unified framework for genomic analysis, as shown below.

A Practical Protocol for Digital DDH Analysis

This section provides a step-by-step experimental protocol for researchers to perform a digital DDH analysis using the GGDC web server.

Research Reagent Solutions

Table 2: Essential Materials and Computational Tools for GGDC Analysis

Item	Function / Description	Example / Source
Genomic Sequences	Input data for comparison; can be draft or complete genomes.	Isolate genomes, Metagenome-Assembled Genomes (MAGs), Single-Amplified Genomes (SAGs) from NCBI or in-house sequencing.
FASTA File Format	Standard text format for representing nucleotide sequences.	The required input format for the GGDC web server.
GGDC Web Server	The online platform that performs the digital DDH calculation.	Publicly accessible at: https://ggdc.dsmz.de [48]
BLAST+ Suite	The underlying alignment software used by GGDC for HSP identification.	Integrated into the GGDC backend; no direct user action required.
TYGS (Type Strain Genome Server)	A complementary service from DSMZ for complete genome-based taxonomy.	Used for polyphasic taxonomic studies and generating publication-ready trees [48].

Step-by-Step Procedure

• Data Preparation:

  • Obtain the genome sequences for the two organisms you wish to compare. Ensure the sequences are in FASTA format.
  • For public genomes, you may use their GenBank accession numbers. However, for faster processing, especially with larger genomes or drafts, it is highly recommended to download the FASTA files and upload them directly [48].

• GGDC Submission:

  • Navigate to the GGDC 3.0 website (https://ggdc.dsmz.de).
  • On the submission form, either upload your prepared FASTA files for both genomes or enter their valid GenBank accession numbers.
  • It is advisable to use the default parameters for a standard analysis unless you have a specific reason to modify them.

• Job Execution and Results Retrieval:

  • Submit the job. The processing time will depend on genome size, complexity, and server load.
  • Once completed, the results page will display the digital DDH estimates from all three models, along with their corresponding confidence intervals and the probability that the two strains belong to the same species.

• Results Interpretation:

  • A digital DDH value of ≥70% is typically interpreted as indicating that the two genomes belong to the same species [52].
  • Examine the results from all three models. Consistent high values across models strengthen the conclusion.
  • Always consider the confidence intervals. A wide interval may suggest the result is not highly reliable, potentially due to low genome quality or high fragmentation.
  • For a comprehensive taxonomic assignment, integrate the GGDC results with other genomic data, such as ANI values and phylogenetic analysis of core genes [49] [47].

The Genome-to-Genome Distance Calculator has cemented its role as an indispensable tool in modern microbial taxonomy. By providing a robust, reproducible, and high-throughput digital replacement for wet-lab DDH, it has empowered researchers to delineate species boundaries with unprecedented precision and scale. Its integration into a multi-faceted taxonomic framework—alongside ANI, pangenomics, and core genome phylogenies—is driving a more accurate and stable understanding of microbial diversity and evolution. As genomic databases continue to expand, the principles and practices of digital DDH, as embodied by the GGDC, will remain fundamental to the ongoing effort to map the microbial tree of life.

Metagenomic and Metatranscriptomic Approaches for Unculturable Microbial Biodiversity

The vast majority of microbial life on Earth remains unexplored, representing a significant frontier in biological science. Although culture-independent metagenomic DNA sequence analyses have provided an extensive understanding of microbial diversity, it is estimated that uncultured genera and phyla could comprise 81% and 25%, respectively, of microbial cells across Earth's microbiomes [53]. This uncultured majority is often referred to as microbial "dark matter." Traditional isolation techniques have successfully cultivated representatives from only a limited number of bacterial phyla, predominantly Bacteroidetes, Proteobacteria, Firmicutes, and Actinobacteria, leaving entire lineages inaccessible for direct study [53]. This limitation presents a substantial knowledge gap in microbial taxonomy, phylogeny, and our fundamental understanding of global biogeochemical processes.

The advent of metagenomics and metatranscriptomics has initiated a paradigm shift in microbial ecology, enabling researchers to explore the genetic and functional potential of microbial communities without the need for cultivation [53] [54]. Metagenomics involves the study of the collective genomes of microorganisms from an environment, allowing for the reconstruction of Metagenome-Assembled Genomes (MAGs) and providing insights into the taxonomic composition and metabolic capabilities of a community [53]. Metatranscriptomics, a complementary approach, identifies and quantifies the mRNA transcripts present in a microbial community, thereby revealing the actively expressed genes and biological pathways under specific environmental conditions [54]. Together, these approaches provide a powerful suite of tools to bring uncultured microbes into taxonomic and phylogenetic focus, moving beyond mere diversity catalogs to elucidate the functional roles and ecological contributions of the uncultured microbial majority [55] [56].

Fundamental Concepts and Taxonomic Significance

Defining the Uncultured Majority: From VBNC to PDNC

The challenge of microbial uncultivability has been historically summarized by the "Great Plate Count Anomaly," which observed that the number of microbial cells visible under a microscope vastly exceeds the number of colonies that can be grown on culture plates [55]. This concept has been refined with modern sequencing. A key development is the distinction between Viable But Nonculturable (VBNC) cells and Phylogenetically Divergent Noncultured Cells (PDNC) [55]. VBNC cells are dormant microorganisms that may resume growth under appropriate conditions. In contrast, PDNC represent lineages divergent at the order level or higher with no cultured representatives; their uncultivability may stem from fundamental biological constraints, such as obligate syntrophy, extreme oligotrophy, or exceptionally slow growth rates that preclude standard isolation techniques [55]. A meta-analysis suggests the median percentage of cultured cells from diverse environments is as low as 0.5%, a figure that updates the traditional estimate of 1% and underscores the dominance of PDNC in most non-human environments [55].

The Critical Role of Cultivation in the “Omics” Age

Despite the power of culture-independent methods, obtaining cultivated isolates remains a critical objective in microbial taxonomy and phylogeny [53]. Pure cultures are essential for several reasons:

• Experimental Validation: Cultures allow for direct experimental testing of metabolic and physiological functions inferred from genomic data, confirming the activities of novel genes and improving the accuracy of gene annotations [53].
• Formal Taxonomic Description: According to the International Code of Nomenclature of Prokaryotes, only cultures can serve as official 'type material' for the formal description of new species [53]. The lack of cultured representatives thus creates a disparity between sequence-based diversity surveys and formal taxonomic frameworks, though efforts are underway to integrate uncultured organisms into existing nomenclatural systems [53].
• Reference Genomes: Isolates provide high-quality reference genomes that significantly improve the interpretation of microbial community functions in metagenome and metatranscriptome studies [53].
• Biotechnological Applications: Cultured isolates are invaluable resources for applications in probiotics, biocontrol agents, and industrial processes [53].

Consequently, metagenomic and metatranscriptomic data are increasingly being used to guide targeted cultivation strategies, creating a virtuous cycle where sequence data informs isolation efforts, and isolates, in turn, refine 'omics'-based interpretations [53].

Methodological Frameworks: From Sampling to Data Generation

Experimental Workflow: An Integrated Pipeline

The following diagram illustrates the comprehensive integrated workflow for a study combining metagenomics and metatranscriptomics, from initial sample collection through to final biological interpretation.

Key Sequencing Technologies and Their Applications

Table 1: Comparison of Key Technologies in Microbial Biodiversity Studies

Technology	Target	Key Output	Strengths	Limitations
16S/18S/ITS Amplicon Sequencing [54]	Hypervariable regions of ribosomal RNA genes	Taxonomic profile (community composition)	Cost-effective; standardized protocols; well-established databases	Limited taxonomic resolution (often to genus level); primer bias; no functional information
Shotgun Metagenomics [53] [54]	All genomic DNA in a sample	Catalog of genes and genomes (MAGs); functional potential	Reveals taxonomic composition and metabolic potential; can reconstruct genomes	High computational demand; DNA extraction bias; does not measure activity
Metatranscriptomics [54] [57]	mRNA transcripts from a community	Gene expression profile; active biological pathways	Identifies actively expressed genes; reveals community response to environment	mRNA instability; challenging RNA extraction; high host/bacterial rRNA background

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 2: Essential Research Reagents and Solutions for Meta-Omics Studies

Reagent/Material	Function	Technical Considerations
Nucleic Acid Extraction Kits (e.g., for soil, water, host-associated samples)	Simultaneous co-extraction of DNA and RNA from complex samples	Must be optimized for sample type; critical for obtaining representative, high-quality nucleic acids without bias [58]
rRNA Depletion Probes	Selective removal of abundant ribosomal RNA from total RNA extracts	Essential for enriching messenger RNA (mRNA) in metatranscriptomic studies; can be taxon-specific [54]
Reverse Transcriptase Enzymes	Synthesis of complementary DNA (cDNA) from mRNA templates	High fidelity and processivity are crucial for accurate representation of transcript abundance [54]
Library Preparation Kits (for Illumina, PacBio, Oxford Nanopore)	Preparation of sequencing-ready libraries from DNA or cDNA	Choice affects insert size, coverage uniformity, and potential biases; must be compatible with sequencing platform [58]
Universal Primers for 16S/18S/ITS rRNA genes [54]	Amplification of taxonomic marker genes	Design impacts which taxa are amplified; "universal" primers can still have amplification biases against certain taxa
Functional Gene Probes (e.g., for dsrAB, aprBA [57])	Targeted capture or amplification of specific metabolic genes	Allows for focused study of particular microbial guilds (e.g., sulfate-reducers, nitrifiers)

Data Analysis and Bioinformatics: From Raw Sequences to Biological Insight

Bioinformatics Workflow for Taxonomic and Functional Profiling

The analysis of metagenomic and metatranscriptomic data involves a multi-step bioinformatic pipeline, with tool selection greatly impacting the reliability of results [54]. Key steps include:

• Preprocessing and Quality Control: Tools like FastQC generate quality reports, followed by adapter trimming and quality filtering using tools like Trimmomatic or Cutadapt to remove low-quality sequences [54].
• Taxonomic Profiling: Two main approaches are employed: (1) Read-based classification using tools like Kraken2 and Bracken that assign taxonomy to individual reads by comparing them to reference databases; and (2) Assembly-based approaches that first assemble reads into longer contigs before binning them into MAGs using tools like MetaBAT2 and MaxBin [53] [54].
• Functional Annotation: Predicted protein-coding sequences are compared against databases such as KEGG, eggNOG, and COG to infer metabolic potential [53] [54]. For metatranscriptomic data, reads are mapped back to assembled contigs or reference genomes to quantify gene expression levels [57].
• Metabolic Pathway Reconstruction: Tools like MetaCyc and KEGG Mapper are used to reconstruct complete metabolic pathways from annotated genes, providing a systems-level view of community metabolism [53].

Metabolic Pathway Prediction and Reconstruction from Metagenomic Data

A critical step in metagenomic analysis is the prediction and reconstruction of metabolic pathways from sequence data, which provides testable hypotheses about the ecological roles of uncultured microorganisms [53]. This process typically involves:

• Gene Calling and Annotation: Open reading frames (ORFs) are predicted from assembled contigs, and their functions are annotated using homology searches against public databases [53].
• Pathway Hole Filling: The presence of key enzymes in a pathway is assessed. Missing enzymes (pathway holes) may indicate either gaps in knowledge, limitations of annotation tools, or genuine biological differences in metabolic strategies [53].
• Comparative Genomics: MAGs from uncultured lineages are compared with genomes of cultivated relatives to identify unique metabolic features and adaptations [57]. For example, comparative genomic analysis of Acidobacteria-related SRMs revealed they possessed more genes encoding glycoside hydrolases, oxygen-tolerant hydrogenases, and cytochrome c oxidases than their Deltaproteobacteria counterparts, suggesting different survival strategies in oxic/hypoxic environments [57].

The following diagram illustrates the logical process of predicting the functional metabolism of an uncultured microorganism from its genomic data, leading to informed cultivation strategies.

Case Study: Sulfate-Reducing Bacteria in an Acidic Mine Wasteland

A compelling example of the integrated application of metagenomics and metatranscriptomics is found in a study of sulfate-reducing bacteria (SRMs) in a revegetated acidic mine wasteland—a constantly oxic/hypoxic terrestrial environment [57]. This research provides a model methodology for investigating uncultured microbial groups in their ecological context.

Experimental Protocol: Genome-Centric Meta-Omics

• Sample Collection and Nucleic Acid Extraction: Soil samples were collected from the revegetated mine wasteland, which had Eh values ranging from ~180–680 mV, representing constantly oxic/hypoxic conditions. Simultaneous DNA and RNA was co-extracted from these environmental samples. RNA was treated with DNase to remove genomic DNA contamination, followed by cDNA synthesis [57].
• Sequencing, Assembly, and Binning: Shotgun metagenomic sequencing was performed on the DNA extracts. Sequence assembly generated contigs, which were binned into 982 medium- to high-quality MAGs (completeness >50%, contamination <10%). From these, 16 reductive dsrAB-containing MAGs were identified—12 from Acidobacteria and 4 from Deltaproteobacteria, including three putative new genera [57].
• Metatranscriptomic Activity Assessment: RNA sequencing was conducted to profile the community transcriptome. Reads were mapped to the recovered MAGs to determine which metabolic pathways were actively expressed in situ. The analysis revealed that 15 of the 16 SRM MAGs were transcriptionally active, with one acidobacterial MAG dominating the SRM transcript pool [57].

Key Findings and Technical Insights

The combined meta-omics approach yielded several critical insights that would have been impossible through cultivation alone:

• Novel Lineages and Metabolic Adaptations: The discovery of SRMs within Acidobacteria (subdivision 1) expanded the known diversity of sulfate-reducing microorganisms beyond the well-studied Deltaproteobacteria. Comparative genomics showed that the acidobacterial SRMs possessed different antioxidant enzyme complements (e.g., more cytochrome c oxidases) compared to the deltaproteobacterial SRMs (which had more superoxide reductases), suggesting lineage-specific adaptations to oxidative stress [57].
• In Situ Activity and Viral Interactions: Metatranscriptomics confirmed the in situ activity of these novel SRMs, with expression of genes involved in sulfate reduction, oxidative stress response, and organic matter competition. Furthermore, viral genome sequences (prophages) were detected in several MAGs, encoding auxiliary functions such as glycoside hydrolysis and antioxidation, suggesting that viruses may influence the ecological fitness of these uncultured SRMs [57].

Challenges and Future Perspectives

While metagenomic and metatranscriptomic approaches have dramatically advanced our understanding of unculturable microbial biodiversity, several challenges remain:

• Technical and Computational Limitations: Incomplete DNA extraction, sequencing biases, and the computational complexity of assembling genomes from complex communities can lead to fragmented MAGs and incomplete metabolic reconstructions [53] [54]. The sheer volume and complexity of sequence data also present significant challenges in processing, analysis, and visualization [59].
• Functional Interpretation and Validation: A significant proportion of genes in metagenomic datasets are of unknown function, and inferring phenotype from genotype remains challenging [53]. Furthermore, mRNA abundance does not always correlate directly with protein activity or metabolic flux, necessitating complementary approaches like metaproteomics and metabolomics for full functional insight [54].
• Database and Annotation Biases: Functional annotation relies heavily on reference databases that are skewed toward genes from cultured microorganisms, potentially leading to misinterpretation of genes from novel, uncultured lineages [53] [55].

Future progress will depend on the development of more sophisticated computational tools, the expansion of reference databases with genomes from uncultured lineages, and the continued integration of multiple 'omics' approaches (metaproteomics, metabolomics) with innovative culturing strategies to bridge the gap between sequence-based prediction and experimental validation [53] [55]. As these technologies mature, they will undoubtedly unravel further secrets of the uncultured microbial world, fundamentally enriching our understanding of microbial taxonomy, phylogeny, and the ecological rules governing the planet's dominant life forms.

Resolving Taxonomic Ambiguity: Addressing Misclassification and Polyphyletic Groups

Identifying and Correcting Misclassified Genomes in Public Databases

Public genomic databases are foundational resources for modern biological research, enabling advancements in comparative genomics, drug discovery, and phylogenetic studies. However, these repositories suffer from a critical issue: taxonomic misclassification of genomic data. Such errors propagate through downstream analyses, compromising scientific validity across microbiology, clinical diagnostics, and therapeutic development [60]. Misclassifications arise primarily from user submission errors during database deposition, contamination in biological samples, and limitations in computational annotation tools [60] [61]. One comprehensive analysis of the non-redundant (NR) protein database identified over two million potentially misclassified proteins, representing approximately 7.6% of sequences with conflicting taxonomic assignments [60]. This technical guide examines the sources, detection methods, and correction protocols for genome misclassification within the fundamental context of microbial taxonomy and phylogeny, providing researchers with actionable methodologies to ensure data integrity.

Root Causes and Impacts of Genome Misclassification

Genome misclassification stems from three interconnected sources with distinct mechanisms:

• User Submission Errors: Public databases like NCBI rely on researcher-provided metadata during sequence deposition without robust validation mechanisms. Inaccurate organism identification at the point of submission permanently embeds errors that propagate through derived analyses [60]. A documented case involved a clinical Candida albicans sample misidentified as Naumovozyma dairenensis in whole-genome shotgun submissions, requiring extensive phylogenetic analysis to rectify [61].

• Sample Contamination: Biological samples often contain undetected microbial contaminants that become incorporated into sequence data. Common contamination sources include soil bacteria in plant tissue samples, human DNA in bacterial isolates, and vector/adapter sequences from library preparation [60]. NCBI recommends contamination screening tools like VecScreen, yet contaminated sequences persistently enter public databases.

• Computational Annotation Errors: Homology-based annotation tools can erroneously transfer taxonomic labels across evolutionarily related but distinct organisms, particularly when reference databases contain pre-existing errors [60]. Such computational misannotations are self-perpetuating, as incorrectly labeled sequences become references for future annotations.

Impacts on Biological Research

The ramifications of misclassified genomes extend across multiple research domains:

• Phylogenetic Studies: Mislabeled sequences introduce topological errors in evolutionary trees, distorting evolutionary relationships and divergence time estimates [62].
• Comparative Genomics: Invalid taxonomic assignments lead to incorrect conclusions about gene family distributions, horizontal gene transfer events, and genotype-phenotype correlations [63].
• Drug Discovery: Misidentified microbial targets can divert therapeutic development efforts toward irrelevant pathways, particularly in antibiotic and vaccine development [64].
• Microbiome Research: Contaminated reference genomes skew taxonomic profiling in metagenomic studies, altering ecological interpretations and disease associations [65].

Table 1: Documented Misclassification Cases and Their Impacts

Database	Misclassification Type	Documented Impact	Reference
NCBI NR Database	2,238,230 misclassified proteins	Error propagation in homology searches	[60]
RefSeq Bacterial Genomes	2,250 genomes contaminated with human sequences	Compromised comparative genomics	[60]
Whole-Genome Shotgun Submissions	Clinical C. albicans as N. dairenensis	Invalid phylogenetic placement	[61]
Influenza Virus Database	Host species misassignment	Impaired zoonotic transition prediction	[66]

Detection Methods for Misclassified Genomes

Phylogenetic Marker Analysis

Single-gene phylogenetic analysis using conserved marker genes provides an efficient initial screening method for taxonomic misassignment.

Experimental Protocol:

• Marker Gene Selection: Identify appropriate phylogenetic markers for the taxonomic group of interest:

  • Bacteria: 16S rRNA, rpoB, gyrB
  • Fungi: ITS, LSU, RPB2, TEF1-α [61]
  • Universal: Ribosomal proteins, elongation factors

• Sequence Extraction and Alignment:

  • Extract corresponding gene sequences from target genomes via BLAST search against reference databases
  • Perform multiple sequence alignment using MAFFT or MUSCLE with default parameters
  • Trim alignment to remove poorly aligned regions using TrimAl or Gblocks

• Phylogenetic Tree Construction:

  • Implement maximum-likelihood analysis with RAxML or IQ-TREE under appropriate substitution models
  • Assess branch support with 1000 bootstrap replicates
  • Include reference sequences from verified taxa as phylogenetic anchors

• Taxonomic Discordance Assessment:

  • Identify sequences that cluster outside their designated taxonomic group with high bootstrap support (>90%)
  • Flag sequences with unexpectedly long branch lengths indicating potential misalignment or misidentification [61]

Table 2: Phylogenetic Markers for Taxonomic Validation

Taxonomic Group	Primary Marker	Secondary Markers	Discordance Threshold
Bacteria	16S rRNA	rpoB, gyrB, dnaK	>3% sequence divergence from type strain
Fungi	ITS	LSU, RPB2, TEF1-α	Non-monophyletic with conspecifics
Archaea	16S rRNA	rpoB, EF-2, ATPase	>5% sequence divergence
Metagenomic bins	Universal single-copy genes	CheckM completeness	Contamination >5%

Whole-Genome Similarity Measures

Comparative genomic approaches provide higher resolution than single-gene methods for detecting misclassifications.

Average Nucleotide Identity (ANI) Analysis:

• Genome Preparation:

  • Download assembled genomes in FASTA format
  • Remove plasmid sequences if not relevant to taxonomy
  • Mask repetitive regions using RepeatMasker

• ANI Calculation:

  • Use OrthoANI or FastANI for genome-wide similarity calculations
  • Implement BLAST-based or k-mer-based approaches depending on required precision/speed balance
  • Calculate pairwise ANI values against type strain genomes where available

• Interpretation:

  • ANI values <95% typically indicate different species
  • ANI values <98% typically indicate different subspecies
  • Flag genomes with ANI values inconsistent with their taxonomic assignment [63]

Machine Learning Approaches

Advanced computational methods leverage pattern recognition to identify misclassified sequences.

Deep Learning for Host Prediction:

• Data Preparation:

  • Curate training dataset with verified taxonomic labels
  • For nucleotide sequences: use codon-based encoding (1-61 indices)
  • For protein sequences: use amino acid encoding (1-20 indices)
  • Apply sequence length normalization through padding/truncation

• Model Architecture:

  • Implement bidirectional LSTM or Transformer architectures
  • Include embedding layers to capture sequence patterns
  • Utilize dropout layers (typically 0.2-0.5) to prevent overfitting
  • Configure final dense layer with softmax activation for multi-class classification

• Training and Validation:

  • Employ k-fold cross-validation (typically k=10) for robust performance estimation
  • Use early stopping based on validation loss to prevent overfitting
  • Balance datasets through undersampling or oversampling techniques [66]

• Misclassification Detection:

  • Identify sequences with low prediction confidence (low probability scores)
  • Flag sequences where predicted taxonomy conflicts with original assignment
  • Analyze misclassified sequences for potential zoonotic transitions or taxonomic ambiguities [66]

Correction Protocols for Misclassified Genomes

Heuristic Taxonomic Reconciliation

Automated approaches combine multiple evidence sources to propose corrected taxonomic assignments.

Workflow Implementation:

• Evidence Aggregation:

  • Collect taxonomic annotations from curated (RefSeq, SwissProt) and uncurated (GenBank) sources
  • Extract taxonomic provenance and frequency statistics for each annotation
  • Generate sequence similarity clusters at 95% identity threshold

• Consensus Determination:

  • Apply majority voting weighted by database credibility
  • Resolve conflicts using phylogenetic placement in reference trees
  • Propose taxonomic assignments based on combined evidence [60]

• Validation:

  • Assess precision/recall using simulated datasets with known errors
  • Report precision >97% and recall >87% as performance benchmarks [60]

Figure 1: Workflow for detection and correction of misclassified genomes. The multi-evidence approach integrates phylogenetic, genomic, and computational methods to achieve taxonomic consensus.

Metadata Curation Tools

Automated curation systems streamline the identification of metadata inconsistencies in genomic databases.

AutoCurE Implementation for Local Databases:

• Database Download:

  • Retrieve bacterial genomes from NCBI FTP site (all.fna.tar.gz)
  • Download corresponding genome reports from NCBI Genome

• Inconsistency Flagging:

  • Compare genome folder names with official genome reports
  • Verify consistency between filename accession numbers and internal sequence accessions
  • Identify archaeal genomes misplaced in bacterial directories
  • Flag discrepancies in BioProject/UID identifiers
  • Detect non-reference assembly files (contigs, scaffolds) mislabeled as complete genomes [63]

• Correction Protocol:

  • Generate report statements for each flagged inconsistency
  • Manually verify flagged genomes against current NCBI entries
  • Submit correction requests to database curators with supporting evidence
  • Maintain version-controlled local database with documented corrections

Table 3: AutoCurE Flagging Categories and Resolution Actions

Flag Category	Detection Method	Resolution Action
Genome Name Mismatch	Comparison of folder name vs. genome report	Rename to match official nomenclature
Archaea in Bacteria	Taxonomic assignment check	Move to appropriate archaeal directory
Accession Inconsistency	File name vs. internal accession comparison	Verify correct assembly version
Missing BioProject	UID comparison between sources	Add missing metadata from current record
Non-reference Assembly	Detection of non-NC_ accessions	Reclassify as draft genome
Missing Chromosome	Presence of only plasmid files	Flag as incomplete genome

Table 4: Key Research Reagent Solutions for Misclassification Analysis

Tool/Resource	Type	Function	Application Context
AutoCurE [63]	Automated curation tool	Identifies metadata inconsistencies	Local database quality control
CheckM [65]	Bioinformatics tool	Assesses genome completeness/contamination	Metagenomic bin validation
Kraken [65]	Taxonomic classifier	k-mer based taxonomic assignment	Shotgun metagenomic sequencing
MetaPhlAn2 [65]	Profiling tool	Clade-specific marker gene analysis	Taxonomic profiling of microbiomes
RDP Classifier [65]	Bayesian classifier	16S rRNA taxonomic assignment	Amplicon sequencing studies
PhyloPhlAn [62]	Phylogenetic placement	Places genomes in reference phylogeny	Phylogenetic validation
DADA2 [65]	Pipeline	Amplicon sequence variant inference	High-resolution marker gene analysis
Bowtie2/BWA [61]	Read aligner	Maps sequences to reference genomes	Contamination detection
GATK [61]	Variant caller	Identifies SNPs/indels	Strain-level validation
NCBI Datasets [63]	Data repository	Source of genomic sequences and metadata	Reference data acquisition

Robust identification and correction of misclassified genomes represents an essential quality control process in genomic science. Integrating phylogenetic validation, whole-genome similarity measures, and machine learning approaches provides a multi-layered defense against taxonomic errors. The protocols and methodologies outlined in this technical guide equip researchers with standardized approaches to detect anomalies, propose corrected classifications, and contribute to improving data quality in public repositories. As genomic databases continue exponential growth, implementing rigorous taxonomic validation will become increasingly critical for maintaining the integrity of biological research across microbial ecology, infectious disease monitoring, and drug discovery pipelines. Future developments in artificial intelligence and expanded reference databases promise enhanced capabilities for automated taxonomic curation, potentially reducing the current estimated 4-8% misclassification rate in major genomic resources [60] [66].

The massive accumulation of genome sequences in public databases has revolutionized microbial taxonomy, shifting the paradigm from single-gene analyses to genome-level phylogenetic reconstructions [67]. This transition, while providing unprecedented resolution for delineating taxonomic boundaries, introduces significant challenges for achieving consistency and reproducibility across studies. Diverse evolutionary forces—including recombination, horizontal gene transfer, and varying evolutionary rates—endow many genomic loci with undesirable properties for phylogenetic reconstruction [67]. When undetected, these factors can produce erroneous or strongly supported yet biased phylogenetic estimates, particularly problematic when inferring species trees from concatenated datasets [67]. The field consequently requires robust, standardized bioinformatic workflows that systematically identify and filter out such problematic markers while providing reproducible protocols for tree inference. This technical guide examines the critical role of specialized software tools, with a focused examination of GET_PHYLOMARKERS, in establishing standardized pipelines for achieving phylogenomic consistency in microbial geno-taxonomy.

GET_PHYLOMARKERS: A Core Tool for Standardized Phylogenomic Analysis

GETPHYLOMARKERS is an open-source software package specifically designed to address the critical need for reproducibility in phylogenomics by selecting optimal phylogenetic markers and inferring robust genome trees from core-genome alignments or pan-genome matrices (PGM) [67] [68]. Its development stems from the recognition that undetected problematic loci can severely compromise phylogenetic accuracy. The pipeline integrates seamlessly with the homologous cluster analysis provided by GETHOMOLOGUES, another established tool in microbial pan-genomics [67].

The software's primary function is to identify high-quality, single-copy orthologous gene clusters and apply sequential filters to exclude loci with evolutionary histories that could mislead species tree estimation. The key filtering criteria are designed to remove sequences with the following characteristics:

• Recombinant sequences: Identified using algorithms like PhiPack.
• Anomalous phylogenetic signals: Those producing aberrant tree topologies.
• Poorly resolved trees: Markers providing insufficient phylogenetic information.

After filtering, GET_PHYLOMARKERS performs multiple sequence alignments and computes maximum likelihood (ML) phylogenies for individual markers in parallel on multi-core computers, significantly accelerating computational runtime [67]. Finally, it estimates a species tree from the concatenated set of top-ranking alignments using either FastTree or IQ-TREE, with the latter set as the default due to its superior performance in benchmark analyses [67]. This comprehensive and opinionated workflow ensures that researchers follow a consistent, validated path from raw genomic data to phylogenetic inference.

Table 1: Core Functional Components of GET_PHYLOMARKERS

Component	Function	Algorithm Options
Ortholog Cluster Input	Processes homologous clusters from GET_HOMOLOGUES	OrthoMCL, COGtriangles
Sequence Alignment	Aligns nucleotide or protein sequences	MUSCLE, MAFFT
Recombination Filter	Identifies and excludes recombinant alignments	PhiPack
Single-Gene Tree Evaluation	Filters anomalous or poorly resolved phylogenies	FastTree, IQ-TREE
Species Tree Inference	Estimates final genome phylogeny	IQ-TREE (default), FastTree
Pan-Genome Phylogeny	Infers trees from pan-genome matrices	Maximum Likelihood, Parsimony

A Standardized Workflow for Robust Phylogenomic Inference

The power of GET_PHYLOMARKERS lies in its structured, multi-stage workflow that systematically processes homologous gene clusters into a high-confidence species tree. The following diagram and protocol outline the standardized steps for achieving reproducible phylogenomic analysis.

Detailed Experimental Protocol

Step 1: Input Data Preparation and Ortholog Identification

• Genome Assembly & Annotation: Begin with high-quality, assembled genome sequences, ideally annotated using a consistent, current methodology to minimize annotation errors that plague genomic databases [22]. Standardize file formats (FASTA for genomes, GFF for annotations).
• Orthologous Cluster Generation: Run GET_HOMOLOGUES on the genome set to identify clusters of homologous sequences. Standard parameters typically include a minimum coverage and identity threshold (e.g., 75%), using algorithms like OrthoMCL or COGtriangles [67]. The output is a comprehensive catalog of gene families.

Step 2: Core Marker Selection and Alignment

• Extract Single-Copy Core Genes: Filter the homologous clusters to retain only those present as single-copy in all genomes under study. This defines the core genome.
• Multiple Sequence Alignment (MSA): Align the nucleotide or amino acid sequences for each core gene cluster using a robust aligner such as MUSCLE or MAFFT, which are executed in parallel by GET_PHYLOMARKERS to leverage multi-core computing infrastructure [67].

Step 3: Rigorous Marker Filtering

• Recombination Detection: Test each individual gene alignment for evidence of recombination using the PhiPack algorithm integrated within GET_PHYLOMARKERS. Exclude alignments that return a significant p-value (e.g., p < 0.05) for the Phi test [67].
• Single-Gene Tree Assessment: Infer a maximum likelihood tree for each remaining alignment. Filter out genes that produce anomalous topologies (e.g., extreme branch lengths) or poorly resolved trees (e.g., with low bootstrap support at key nodes).

Step 4: Species Tree Inference

• Alignment Concatenation: Compile the filtered, high-quality alignments into a single, supermatrix (concatenated alignment). Ensure correct positional correspondence across partitions.
• Partitioning and Model Selection: For DNA alignments, implement a partition scheme that allows different evolutionary models for different genes. Use model testing tools (e.g., ModelFinder in IQ-TREE) to find the best-fit model for each partition.
• Tree Reconstruction and Support: Reconstruct the species tree under the Maximum Likelihood criterion using IQ-TREE. Perform branch support analysis with at least 1000 ultrafast bootstrap replicates to assess clade confidence [67].

Step 5 (Alternative): Pan-Genome Phylogeny

• Pan-Genome Matrix (PGM) Construction: Use GET_HOMOLOGUES to generate a binary PGM, where rows represent genomes and columns represent homologous clusters, with values indicating presence (1) or absence (0).
• Tree Inference from PGM: Infer a phylogenetic tree directly from the PGM using either Maximum Parsimony or Maximum Likelihood methods implemented in GET_PHYLOMARKERS, providing an evolutionary perspective based on gene content [67].

Validation and Application: A Case Study in Bacterial Taxonomy

The practical utility of this standardized workflow was demonstrated through a critical geno-taxonomic revision of the genus Stenotrophomonas [67] [68]. The analysis of 170 publicly available genomes and 10 new Mexican environmental isolates revealed the power of combining core-genome and pan-genome approaches.

Researchers applied GET_PHYLOMARKERS to this dataset, identifying 20 distinct genomic groups within the S. maltophilia complex (Smc) at a core-genome average nucleotide identity (cgANIb) threshold of 95.9% [67]. These groups were perfectly consistent with strongly supported clades on both core- and pan-genome trees. Furthermore, the analysis identified 14 misclassified genome sequences in the RefSeq database, 12 of which were erroneously labeled as S. maltophilia [68]. This case study highlights how standardized phylogenomic workflows are indispensable for accurate microbial classification and for identifying and correcting errors in public databases.

Table 2: Key Findings from the Stenotrophomonas Case Study Using GET_PHYLOMARKERS

Analysis Metric	Result	Taxonomic Implication
Number of Genomes Analyzed	180 (170 RefSeq + 10 new isolates)	Comprehensive taxonomic sampling
Core-Genome ANI Threshold (cgANIb)	95.9%	Threshold for species-like cluster demarcation
Genomic Groups in Smc	20	Revealed extensive unrecognized diversity
Misclassified RefSeq Genomes	14	Corrected database inaccuracies
Misclassified as S. maltophilia	12	Refined understanding of pathogen distribution

Essential Research Reagent Solutions for Phylogenomics

Implementing a standardized phylogenomic pipeline requires both software tools and conceptual "reagents" – standardized datasets and protocols that ensure consistency across studies. The following toolkit is essential for robust microbial taxonomy research.

Table 3: Essential Research Reagent Solutions for Phylogenomic Studies

Tool/Resource	Type	Function in Workflow
GET_HOMOLOGUES	Software	Identifies robust clusters of homologous sequences from genome data; generates the input clusters for GET_PHYLOMARKERS and the Pan-Genome Matrix [67].
IQ-TREE	Software	Performs fast and effective Maximum Likelihood phylogenetic inference with model selection; the default tree estimator in GET_PHYLOMARKERS [67].
High-Quality Genome Assemblies	Data	The fundamental input data; requires standardized assembly and annotation to ensure comparability and minimize errors from outdated or inconsistent annotations [22].
Core-Genome Average Nucleotide Identity (cgANIb)	Metric/Standard	An Overall Genome Relatedness Index (OGRI) used for quantitative species delimitation and validating phylogenetic groups identified by the workflow [67].
Pan-Genome Matrix (PGM)	Data Structure	A binary matrix representing the presence/absence of gene families across genomes; serves as an alternative data source for inferring evolutionary relationships based on gene content [67].

Standardization is the cornerstone of reproducible science, and this is particularly true for the computationally intensive field of microbial phylogenomics. Tools like GET_PHYLOMARKERS provide a critical framework for achieving this standardization by implementing rigorous, automated workflows for phylogenetic marker selection and tree inference. By filtering out problematic loci, leveraging robust algorithms for tree reconstruction, and offering multiple analytical pathways (core-genome and pan-genome), such pipelines ensure that taxonomic conclusions are based on reliable, high-quality phylogenetic signals. As genomic databases continue to expand at an accelerating pace, the adoption and continued development of these standardized workflows will be essential for achieving a consistent, accurate, and evolutionarily meaningful classification of microbial diversity.

Navigating Horizontal Gene Transfer and Genomic Plasticity in Phylogenetic Trees

Horizontal Gene Transfer (HGT) represents a fundamental mechanism challenging traditional vertical inheritance models in microbial evolution. This technical guide examines how HGT-induced genomic plasticity complicates phylogenetic reconstruction and explores sophisticated computational and experimental methodologies to detect transfer events. By synthesizing current research on quantification methods, transfer mechanisms, and analytical frameworks, we provide microbial taxonomists and pharmaceutical researchers with advanced tools to reconcile phylogenetic conflicts. Our analysis demonstrates that integrating HGT understanding with traditional taxonomy enables more accurate evolutionary modeling, particularly relevant for tracking antibiotic resistance and virulence factors in drug development contexts.

Horizontal Gene Transfer (HGT), defined as the non-genealogical transmission of genetic material between organisms, has fundamentally reshaped our understanding of microbial evolution and challenged the core assumptions underlying phylogenetic tree construction [69]. While classical phylogenetic methods operate under a paradigm of strictly vertical inheritance, empirical genomic evidence reveals that extensive DNA sharing across taxonomic boundaries creates complex evolutionary networks, particularly in prokaryotes but also in eukaryotic lineages [69]. This genomic plasticity introduces significant incongruities when different gene sequences yield conflicting phylogenetic histories, thereby complicating attempts to reconstruct a universal Tree of Life [69].

The implications for microbial taxonomy are profound, necessitating a conceptual shift from tree-like to web-like evolutionary models. For drug development professionals, this paradigm is particularly crucial when tracking the dissemination of antibiotic resistance genes and virulence determinants, which frequently traverse species boundaries via diverse HGT mechanisms [70]. Understanding these dynamics requires interdisciplinary approaches combining computational biology, experimental genetics, and evolutionary theory to decipher patterns of gene sharing and their functional consequences across microbial populations.

Quantifying the Impact: Statistical Scope of HGT

The extent of HGT varies substantially across taxonomic groups and evolutionary timeframes. Comparative genomic analyses reveal that between 1.6% and 32.6% of genes in any given microbial genome have been acquired via HGT, with the cumulative impact throughout prokaryotic lineages reaching 81% ± 15% when considering entire evolutionary histories [69]. This substantial variation reflects methodological differences in detection approaches and taxon-specific biological factors influencing transfer rates.

Table 1: Estimated Horizontal Gene Transfer Rates Across Bacterial Species

Organism	Transfer Mechanism	Gene/Marker	Transfer Efficiency (Events/Donor)
Staphylococcus aureus	Lateral transduction (by phage 80α)	Chromosomal CadR	5.18 × 10⁻²
Staphylococcus aureus	Generalized transduction (by phage 80α)	Chromosomal CadR	2.70 × 10⁻⁴
Staphylococcus aureus	Conjugation (SaPI1)	Mobile genetic element	2.10 × 10⁰
Salmonella enterica	Lateral transduction (by phage P22)	Chromosomal tetR	1.20 × 10⁻²
Salmonella enterica	Conjugation (pOU1114 plasmid)	Mobile genetic element	4.30 × 10⁻²

Recent investigations reveal that certain HGT mechanisms, particularly lateral transduction, can mobilize chromosomal genes at rates exceeding those of classical mobile genetic elements like plasmids and transposons [71]. This remarkable efficiency demonstrates that core chromosomal genes can exhibit mobility previously attributed only to dedicated mobile genetic elements, fundamentally challenging the distinction between stable chromosomes and portable genetic cargo.

Molecular Mechanisms of Horizontal Gene Transfer

Established Transfer Pathways

Bacteria utilize three primary mechanisms for horizontal gene acquisition, each with distinct molecular underpinnings and phylogenetic implications:

• Transformation: The uptake and incorporation of environmental DNA, a process facilitated by specialized competence machinery that varies across bacterial taxa. This mechanism represents a direct route for acquiring genetic material from distantly related organisms, potentially introducing significant phylogenetic incongruence.

• Conjugation: Direct cell-to-cell DNA transfer through specialized conjugative machinery, typically mediating the movement of plasmids and integrative conjugative elements (ICEs). This process often exhibits taxonomic specificity but can bridge considerable phylogenetic distances, creating networks of gene sharing [71].

• Transduction: Virus-mediated DNA transfer through bacteriophages, comprising three distinct subtypes with varying phylogenetic impacts:

  • Generalized Transduction: Packaging of random host DNA fragments into viral capsids during the lytic cycle, typically transferring modest DNA segments (up to 100 kb) at relatively low frequencies [71].
  • Specialized Transduction: Transfer of specific bacterial DNA regions adjacent to prophage integration sites through imprecise excision, mobilizing limited genomic regions but with high specificity.
  • Lateral Transduction: An exceptionally efficient mechanism whereby in-situ replication of prophages followed by processive DNA packaging enables transfer of extensive chromosomal regions (up to several hundred kb) at frequencies surpassing other HGT mechanisms [71].

Lateral Transduction: An Efficient HGT Mechanism

Lateral transduction represents the most powerful DNA transfer mechanism identified, with demonstrated capabilities to mobilize substantial chromosomal segments at rates exceeding classical conjugation [71]. The molecular process unfolds through sequential stages:

• Prophage Induction: Temperate phages initiate replication while remaining integrated in the bacterial chromosome.
• In-situ Replication: Multiple copies of the prophage genome are produced while maintaining chromosomal integration.
• Processive DNA Packaging: The phage terminase complex initiates packaging at the native pac site but continues processively through adjacent bacterial chromosomal DNA.
• Capsid Filling: Sequential filling of multiple capsids with bacterial DNA generates transducing particles containing extensive chromosomal regions.
• Gene Transfer: Injection of bacterial DNA into recipient cells enables homologous recombination and stable inheritance.

This mechanism facilitates the transfer of chromosomal genes at frequencies up to 1,000-fold higher than generalized transduction, effectively blurring the distinction between mobile genetic elements and core chromosomal genes [71].

Computational Detection and Reconciliation Methods

Phylogenetic Inference Approaches

Phylogenetic methods identify HGT by detecting significant conflicts between gene trees and established species phylogenies. These approaches employ sophisticated statistical frameworks:

• Likelihood-based Methods: Compare alternative phylogenetic topologies using maximum likelihood or Bayesian inference to identify significantly better-fitting trees that suggest historical transfer events.
• Distance-based Methods: Analyze deviations in genetic distance patterns between taxa that indicate non-vertical inheritance.
• Consensus-based Approaches: Identify genes with phylogenetic histories significantly discordant from the majority of other genes in the genome.

These methods require high-quality multiple sequence alignments and robust reference species trees, which themselves may be confounded by pervasive HGT [70]. Computational limitations remain significant, particularly when analyzing large genomic datasets across multiple taxa.

Parametric Compositional Methods

Parametric methods detect recently transferred genes through statistical anomalies in sequence composition:

• Nucleotide Composition Analysis: Identifies genes with significantly different GC content from genomic averages [70].
• Oligonucleotide Frequency Analysis: Detects deviations in k-mer usage patterns (particularly tetranucleotides) from genomic signatures [70].
• Codon Usage Bias: Identifies genes with atypical synonymous codon preferences relative to host genome patterns.

These approaches effectively identify recent transfers but lose sensitivity over evolutionary time due to amelioration—the gradual process whereby foreign DNA acquires the compositional characteristics of the recipient genome [69] [70]. This molecular "erosion" of foreign signatures means parametric methods primarily detect evolutionarily recent HGT events.

Table 2: Comparison of HGT Detection Methodologies

Method Type	Key Principles	Detection Timeframe	Strengths	Limitations
Phylogenetic	Gene tree/species tree conflict	Ancient and recent	Evolutionary context, identifies donors	Computationally intensive, requires multiple genomes
Parametric	Sequence composition deviation	Primarily recent	Single-genome application, fast	Misses ameliorated transfers, false positives from regional variation
Hybrid	Combines phylogenetic and parametric signals	Broad timeframe	Increased detection power	Complex implementation, integration challenges

Experimental Validation Protocols

Bacteriophage Transduction Assay

Direct experimental validation of HGT mechanisms provides crucial ground-truthing for computational predictions. The following protocol outlines a standardized bacteriophage transduction approach:

Donor Strain Preparation

• Propagate bacteriophage on donor strain carrying selectable marker (antibiotic resistance gene on plasmid or chromosome).
• Infect early exponential-phase donor culture in GM17 + 10 mM CaClâ‚‚ at MOI (multiplicity of infection) ≈ 1.
• Incubate at 30°C until visible cell lysis occurs (1-2 hours).
• Add 1 ml of exponentially growing culture to infected donor to boost phage titer and transducing particle formation.
• Repeat previous step twice at one-hour intervals.
• Centrifuge at 3,500 × g for 10 minutes and filter-sterilize supernatant through 0.45 μm filter.
• Determine phage titer on recipient strain (≥10⁹ PFU/ml required for transduction experiments).

Recipient Infection and Transductant Selection

• Dilute overnight recipient culture to 2% in fresh GM17 + 10 mM CaClâ‚‚.
• Incubate at 30°C until OD₆₀₀ ≈ 0.3.
• Harvest cells by centrifugation at 11,000 × g for 5 minutes at 4°C.
• Resuspend in 300 μl ice-cold 10 mM MgSOâ‚„.
• Add phage lysate to achieve desired MOI, then add CaClâ‚‚ to 10 mM final concentration.
• Incubate at room temperature for 15 minutes for phage adsorption.
• Plate on GM17 agar with appropriate antibiotics for transductant selection.
• Incubate 48 hours at 30°C and count transductant colonies.
• Calculate transduction frequency as CFU of transductants per CFU of donor [72].

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Research Reagents for HGT Experimental Analysis

Reagent/Category	Specific Examples	Application in HGT Research
Selectable Markers	Antibiotic resistance genes (cadmium, tetracycline)	Tracking transferred DNA in experimental evolution and transduction studies [71]
Phage Vectors	Staphylococcus phage 80α, φ11; Salmonella phage P22	Lateral transduction studies, generalized transduction controls, molecular tool delivery
Bacterial Strains	Staphylococcus aureus, Salmonella enterica, Drosophila symbionts	Model organisms for mechanistic studies, evolutionary experiments, and genetic tool development
Sequence Analysis Tools	Tophat fusion search, oligonucleotide frequency algorithms, phylogenetic reconciliation software	Computational detection of transfer events, phylogenetic conflict analysis, compositional anomaly identification [70] [73]
Culture Media & Supplements	GM17 medium, CaClâ‚‚ supplementation, antibiotic selection plates	Standardized growth conditions for transduction experiments, selection of transconjugants

Implications for Microbial Taxonomy and Phylogenetic Framework

The pervasive nature of HGT necessitates fundamental revisions in microbial taxonomy and phylogenetic methodology. Several integrative approaches have emerged to reconcile these challenges:

Phylogenetic Reconciliation Methods

• Network Approaches: Represent evolutionary relationships as networks rather than strictly bifurcating trees, explicitly modeling conflicting signals as potential HGT events.
• Supermatrix Methods: Concatenate large sets of "core" genes with minimal historical transfer to infer robust species phylogenies.
• Gene Clustering Algorithms: Identify sets of genes with congruent phylogenetic histories, suggesting vertical inheritance or coordinated transfer.

Taxonomic Framework Adjustments Modern microbial classification systems increasingly incorporate HGT awareness through:

• Explicit acknowledgment of hybrid taxonomic origins in gene content
• Distinction between core and accessory genome components in species definitions
• Integration of gene content-based metrics with sequence similarity measures

These approaches recognize that HGT is not merely noise in phylogenetic reconstruction but rather a fundamental evolutionary process shaping microbial genomes and functional capabilities [69]. This perspective is particularly valuable for drug development professionals tracking the dissemination of resistance determinants across taxonomic boundaries.

Navigating horizontal gene transfer and genomic plasticity requires sophisticated integration of computational prediction, experimental validation, and theoretical frameworks that accommodate both vertical and horizontal evolutionary processes. The methodological advances summarized in this guide provide microbial taxonomists with powerful tools to resolve phylogenetic conflicts and develop more accurate evolutionary models.

For drug development applications, understanding HGT mechanisms and patterns enables more effective tracking of resistance gene dissemination and virulence factor acquisition across clinical isolates. Emerging technologies in long-read sequencing, single-cell genomics, and CRISPR-based tracking promise to further illuminate the dynamics of genomic plasticity in diverse microbial communities.

Future research directions should focus on integrating multi-omic datasets to connect HGT patterns with functional consequences, developing standardized HGT quantification metrics for taxonomic applications, and creating unified frameworks that reconcile network-based and tree-based evolutionary models. These advances will continue to transform our understanding of microbial evolution and enhance our ability to manage clinically important bacterial populations.

The landscape of microbial taxonomy is in a state of continual and necessary evolution. What was once a classification system rooted primarily in phenotypic observations has been fundamentally transformed by molecular sequencing technologies, revealing that many historically established genera represent phylogenetically incoherent groupings. The process of bacterial nomenclature change has evolved in complexity over time and continues to be an iterative process that is not without challenges [74]. These reclassifications are not merely academic exercises; they have profound implications for clinical microbiology, infectious disease treatment, antimicrobial stewardship, and public health reporting. When microorganisms are inaccurately grouped, it obscures understanding of their pathogenic potential, antimicrobial susceptibility patterns, and ecological roles. The importance and feasibility of such changes vary among basic researchers, clinical microbiologists, and clinicians, yet updated clinical laboratory accreditation requirements now state that clinical laboratories must update their reporting practices in the case of clinically relevant nomenclature changes [74]. This whitepaper explores prominent case studies of major bacterial genera that have undergone significant taxonomic rearrangement, examining the methodologies driving these changes and their practical consequences for the scientific and clinical communities.

Fundamental Drivers: The Shift from Phenotypic to Genomic Taxonomy

Modern bacterial taxonomy has transitioned from a system based on observable characteristics to one grounded in evolutionary relationships revealed through genetic analysis. This shift began with DNA-DNA hybridization (DDH), which established a gold standard for species delineation (≥70% DDH similarity) [11]. However, DDH was labor-intensive and difficult to standardize. The advent of whole-genome sequencing (WGS) launched microbial taxonomy into a new era, enabling the development of robust, reproducible genomic criteria for classification [11].

Core genomic concepts now underpin taxonomic decisions:

• Average Nucleotide Identity (ANI) and Average Amino Acid Identity (AAI): These metrics calculate the average identity of all orthologous genes or proteins shared between two genomes. Strains from the same microbial species typically share >95% ANI and AAI [11].
• In silico Genome-to-Genome Hybridization (GGDH): This digital replacement for DDH calculates similarity based on high-scoring segment pairs. A similarity of >70% in silico GGDH correlates with the traditional DDH threshold for species boundaries [11].
• Genomic Signature (δ*): This measure of dinucleotide relative abundance is species-specific. A Karlin genomic signature of <10 is expected for strains within the same species [11].
• Phylogenetic Concordance: Species of the same genus should form monophyletic groups in robust analyses based on 16S rRNA gene sequences, multilocus sequence analysis (MLSA), and supertree approaches using core genomes [11].

The following table summarizes the key genomic thresholds that have become the underpinning of modern microbial species delineation.

Table 1: Genomic Standards for Microbial Species and Genus Delineation

Genomic Criteria	Threshold for Same Species	Methodological Basis
Average Nucleotide Identity (ANI)	>95%	Calculation of average nucleotide identity of all shared orthologous genes between two genomes [11].
Average Amino Acid Identity (AAI)	>95%	Calculation of average amino acid identity of all shared orthologous proteins between two genomes [11].
In silico Genome-to-Genome Hybridization (GGDH)	>70% similarity	Digital replacement of DDH using genome-to-genome distance calculation based on high-scoring segment pairs [11].
Karlin Genomic Signature (δ*)	<10	Measure of dinucleotide relative abundance differences between two genomes; a species-specific signature [11].
16S rRNA Gene Identity	>98% (for same genus)	Traditional phylogenetic marker; necessary but not sufficient for species-level classification [11].

Case Study 1: The Paradigmatic Restructuring of the GenusClostridium

The Problem of Phylogenetic Incoherence

The genus Clostridium, as historically defined, represents a quintessential example of taxonomic heterogeneity. Initially proposed by Prazmowski in 1880 with C. butyricum as the type species, it became a "catch-all" repository for Gram-positive, spore-forming, anaerobic organisms [75]. This phenotypically based classification resulted in a group of approximately 228 species and subspecies with staggering phylogenetic diversity, evidenced by a Guanine-Cytosine (G+C) content ranging from 21% to 54%—a range considered too extensive for a coherent single genus [75]. Early molecular studies using 16S rRNA gene sequencing revealed that the genus was paraphyletic, with many species showing closer evolutionary relationships to other genera than to the type species, C. butyricum [75].

Genomic Resolution and Reclassification Proposals

Comprehensive phylogenetic analyses led to the identification of distinct clusters, with Clostridium cluster I recognized as the true Clostridium sensu stricto as it contains the type species [75]. This reclassification was further supported by the discovery of unique conserved indels in three highly conserved proteins (DNA gyrase A, ATP synthase beta subunit, and ribosomal protein S2) that were exclusive to cluster I species [75].

The reclassification involved:

• Restriction of Clostridium: The proposal to restrict the genus Clostridium to C. butyricum and other cluster I species.
• Transfer of Non-Cluster I Species: Numerous species outside cluster I were moved to new or existing genera. For example, C. histolyticum, C. limosum, and C. proteolyticum were proposed for transfer to a novel genus, Hathewaya [75].
• Incorporation of Other Genera: Some species from genera like Eubacterium and Sarcina were found to fall within cluster I and were transferred to Clostridium sensu stricto [75].

Clinical and Research Impact

This restructuring posed significant challenges for the medical community. Clostridium difficile, a major nosocomial pathogen, falls outside cluster I (in cluster XI) and should, taxonomically, be moved to a new genus [75]. However, due to the profound clinical implications and the risk of confusion—a situation addressed by the Bacteriological Code under "perilous names" (nomina periculosa)—the name Clostridium difficile has been retained to prevent risks to health and life that could arise from a name change [75]. This case highlights the delicate balance between scientific accuracy and practical utility in clinical settings.

Case Study 2: Clinically Driven Nomenclature Changes

TheAggregatibacterReclassification Saga

The evolution of the name for the organism now known as Aggregatibacter actinomycetemcomitans illustrates how taxonomic changes can directly affect laboratory practices and clinical guidance. This organism was initially classified as Actinobacillus actinomycetemcomitans, then moved to the genus Haemophilus, before finally being placed in the novel genus Aggregatibacter in 2006 [74].

Each taxonomic shift triggered changes in clinical guidelines:

• As Haemophilus: The Clinical and Laboratory Standards Institute (CLSI) included susceptibility testing recommendations in the M100 document, specifying the use of Haemophilus test medium [74].
• As Aggregatibacter: Recommendations were moved to the HACEK organism section of the CLSI M45 document. The recommended culture medium was changed, and the number of antibiotics with available interpretive criteria was significantly reduced [74].

This reclassification profoundly impacted laboratory protocols, affecting susceptibility testing methods, media selection, and the interpretation of results, thereby directly influencing patient treatment decisions [74].

TheOchrobactrumtoBrucellaReclassification

Another clinically significant change was the reclassification of some Ochrobactrum species to the genus Brucella [74]. This had immediate and critical implications for laboratory safety and patient management:

• Laboratory Safety: Brucella species are highly pathogenic and require Biosafety Level 3 (BSL-3) precautions due to the risk of aerosol transmission. Laboratories had to immediately update their protocols for handling these organisms when they were reclassified [74].
• Therapeutic Implications: Treatment for brucellosis (caused by Brucella species) typically involves a prolonged course of doxycycline and rifampin. In contrast, Ochrobactrum infections may be treated with imipenem, fluoroquinolones, trimethoprim-sulfamethoxazole, or aminoglycosides, and multidrug resistance has been described [74].

Table 2: Clinical Impact of Bacterial Reclassification: Two Case Studies

Reclassification Event	Key Laboratory & Diagnostic Impacts	Therapeutic & Stewardship Implications
Reclassification to Aggregatibacter	Shift in CLSI guidelines from M100 to M45 document; change in recommended culture medium; reduced list of antibiotics with interpretive criteria [74].	Affected choice of empiric and directed antibiotics; required updates to antimicrobial stewardship protocols [74].
Reclassification of Ochrobactrum to Brucella	Immediate change in biosafety requirements from BSL-2 to BSL-3; update to laboratory safety protocols and personal protective equipment (PPE) standards [74].	Drastic change in recommended antimicrobial therapy from regimens for Ochrobactrum to standard brucellosis treatment [74].

Methodologies Enabling Modern Taxonomic Rearrangement

Advanced Sequencing and Genome Assembly

The explosion in metagenomic sequencing has dramatically accelerated the discovery and classification of novel microbes. Recent studies of gut microbiota from high-altitude mammals have utilized co-assembly binning strategies on massive datasets (e.g., 1,412 samples producing 33.52 Tb of raw data) to reconstruct 14,062 high-confidence species-level genome bins (SGBs) [14]. Remarkably, over 88% of these SGBs represented potentially novel species, expanding known microbial databases by over 81% for the dominant phylum, Bacillota A [14]. This approach relies on clustering metagenome-assembled genomes (MAGs) based on Average Nucleotide Identity (ANI) of ≥95% to define an SGB, a threshold that aligns with the genomic species definition [14] [11].

Sequencing and Analysis of Marker Genes

While whole-genome sequencing is the gold standard, the sequencing of full-length 16S rRNA genes remains a valuable tool for taxonomic studies, especially for novel organisms without reference genomes. Compared to short-read sequencing of partial variable regions (e.g., V3-V4), full-length 16S sequencing provides superior resolution at the species level [76].

Research has demonstrated that full-length 16S sequencing (sFL16S) yields significantly higher alpha-diversity indices (Observed OTUs, Chao1, Shannon) and classifies a greater number of bacterial taxa compared to the V3V4 method [76]. This is because the full-length sequence provides sufficient evolutionary information to distinguish between closely related species with high sequence similarity, thereby overcoming the misidentification issues common with partial gene sequencing [76].

The following diagram illustrates the core workflow for genomic taxonomic reclassification, integrating both whole-genome and marker gene approaches.

Diagram 1: Genomic Taxonomy Workflow

The Scientist's Toolkit: Essential Reagents and Computational Solutions

The implementation of genomic taxonomy requires a suite of wet-lab reagents and dry-lab computational tools.

Table 3: Research Reagent Solutions for Genomic Taxonomy Studies

Item / Solution	Function in Taxonomic Reclassification
High-Quality Metagenomic DNA Extraction Kits	Ensures integrity and purity of microbial genomic DNA, which is critical for accurate sequencing and downstream analysis [76].
LoopSeq 16S Microbiome Kits (sFL16S)	Enables full-length 16S rRNA gene sequencing on Illumina platforms using synthetic long-read technology with barcoding for improved species-level resolution [76].
Whole-Genome Sequencing Kits	Provides library preparation solutions for various platforms (Illumina, PacBio, Oxford Nanopore) to generate data for ANI, AAI, and pan-genome analysis [14].
Tetranucleotide Frequency-based Binning Tools (e.g., MetaBAT2)	Facilitates the reconstruction of Metagenome-Assembled Genomes (MAGs) from complex microbial communities based on sequence composition and abundance [14].
Genome-To-Genome Distance Calculator (GGDC)	Computes digital DNA-DNA hybridization (dDDH) values between genome pairs for species delimitation based on high-scoring segment pairs [11].
Average Nucleotide Identity (ANI) Calculators (e.g., OrthoANI)	Calculates the average nucleotide identity of orthologous genes shared between two genomes to assess species boundaries (≥95% = same species) [11].
Genome Taxonomy Database (GTDB) Toolkit	Provides a standardized bacterial and archaeal taxonomy based on genome-scale phylogeny, crucial for consistent classification and identification of novel taxa [14].

The case studies of Clostridium, Aggregatibacter, and other genera underscore that taxonomic rearrangement is a fundamental and ongoing process in microbiology, driven by irreproachable genomic data. The shift from phenotype-based to genome-based classification has provided a robust, evolutionary framework that reveals the true relationships between microorganisms. While these changes present logistical challenges for clinical laboratories, diagnostic manufacturers, and information systems, they are essential for accurate communication, effective antimicrobial stewardship, and appropriate patient care. As sequencing technologies continue to advance and costs decline, the pace of taxonomic discovery and revision will only accelerate. The future of microbial taxonomy lies in the widespread adoption of genomic standards, the development of computational tools that make these analyses accessible, and a collaborative spirit between taxonomists, clinical microbiologists, and clinicians to ensure that our microbial language evolves to reflect biological reality, thereby enhancing both scientific understanding and clinical outcomes.

The integration of advanced microbial taxonomy and phylogenetics into the Quality by Design (QbD) framework represents a transformative approach to pharmaceutical development and contamination control. This technical guide elucidates how cutting-edge molecular techniques and data analysis methods provide unprecedented resolution of microbial populations, enabling proactive risk management and quality assurance throughout the product lifecycle. By embedding microbial taxonomy fundamentals into QbD principles, pharmaceutical scientists can develop more robust manufacturing processes, enhance contamination control strategies (CCS), and establish meaningful critical quality attributes (CQAs) based on sound scientific understanding of microbial ecology, phylogeny, and population dynamics.

Quality by Design is a systematic approach to pharmaceutical development that begins with predefined objectives and emphasizes product and process understanding and control based on sound science and quality risk management [77] [78]. The conventional QbD framework encompasses several key elements: quality target product profile (QTPP), critical quality attributes (CQAs), critical process parameters (CPPs), risk assessment, design space, control strategy, and lifecycle management [78]. Traditionally, microbiological aspects have been incorporated through compendial testing and environmental monitoring programs, often with limited taxonomic resolution.

The advent of advanced microbial taxonomy has revolutionized this paradigm by providing powerful tools for understanding microbial phylogeny and population dynamics at unprecedented resolution. Modern microbial taxonomy leverages high-throughput sequencing technologies, phylogenetic analysis, and quantitative bioinformatics to characterize microbial communities with exceptional precision [22] [79] [80]. The integration of these approaches enables a more scientific foundation for contamination control, particularly for sterile products and non-sterile products with specific microbiological quality requirements.

This synergy allows pharmaceutical developers to move beyond simply detecting contamination events to understanding their origins, predicting risks, and designing processes that are inherently robust against microbial variability. By applying phylogenetic principles, scientists can trace contamination sources, understand adaptation mechanisms of microbes in manufacturing environments, and design targeted control strategies that address the most relevant microbial threats based on their taxonomic classification and physiological characteristics.

Fundamentals of Microbial Taxonomy and Phylogeny in Pharmaceutical Context

Core Taxonomic Principles

Modern microbial taxonomy for pharmaceutical applications relies on several fundamental principles that enable precise identification and classification:

• 16S rRNA Gene Sequencing: This established method provides the gold standard for bacterial identification and phylogenetic placement through amplification and sequencing of the highly conserved 16S ribosomal RNA gene [7] [81] [79]. Different hypervariable (V) regions of this gene (V3, V4, etc.) offer varying levels of taxonomic resolution and can introduce bias in community analyses, making selection of appropriate regions a critical methodological consideration [81].

• Phylogenetic Analysis: Microbial phylogeny estimation through multiple sequence alignment (MSA) enables understanding of evolutionary relationships between microbial isolates [7]. Advanced methods such as those implemented in the Borderlands Science project have demonstrated that improved MSAs simultaneously enhance microbial phylogeny estimations and UniFrac effect sizes, providing more accurate taxonomic placement [7].

• Quantitative Approaches: Moving beyond relative abundance measurements, quantitative methods incorporating spike-in standards and digital PCR allow for absolute quantification of microbial populations, eliminating compositional effects that can distort community analyses [79] [80]. These approaches provide more accurate assessments of microbial load, which is critical for risk assessment in pharmaceutical settings.

Advanced Taxonomic Methodologies

Table 1: Advanced Methodologies in Microbial Taxonomy and Their Applications

Methodology	Technical Approach	Pharmaceutical Application
Spike-in Standard Sequencing	Addition of known quantities of artificial 16S rRNA genes to enable absolute quantification	Accurate bioburden assessment in raw materials and process samples [79]
Multi-omic Data Integration	Combined analysis of genomic, transcriptomic, proteomic, and metabolomic data	Understanding microbial functionality in contaminated products [22]
Single-Cell Analysis	Sequencing or microscopy of individual microbial cells	Resolution of minority populations in heterogeneous contaminants [22]
Quantitative Microbial Population Study	Large-scale sampling with quantitative PCR and sequencing	Geographical mapping of microbial distribution in manufacturing facilities [80]

The field continues to evolve with emerging technologies that enhance taxonomic resolution. Single-cell sequencing and super-resolution microscopy now enable the study of microbial physiology at unprecedented levels [22]. Additionally, functional genomics approaches connect genomic content with phenotypic traits, allowing prediction of microbial behavior in pharmaceutical environments based on taxonomic classification [22].

Microbial Taxonomy in Contamination Control Strategy

Foundations of Contamination Control Strategy

A Contamination Control Strategy (CCS) is a systematically planned set of controls for microorganisms, endotoxins, and particles, derived from current product and process understanding that assures process performance and product quality [82] [83]. The European Medicines Agency (EMA) explicitly requires a comprehensive, documented CCS for sterile products, emphasizing the need for a holistic approach that integrates all aspects of contamination control [82].

Traditional CCS has relied largely on environmental monitoring through colony counting and compendial testing methods with limited taxonomic resolution. The integration of advanced microbial taxonomy transforms this approach by enabling:

• Precise Identification of contaminants to the species or strain level, facilitating accurate root cause analysis
• Phylogenetic Tracking of microbial isolates to determine contamination sources and pathways
• Risk-Based Categorization of microorganisms based on their taxonomic identity and associated physiological traits
• Quantitative Population Analysis to understand microbial ecology in manufacturing environments

Implementing Taxonomy-Enhanced CCS

Table 2: Taxonomy-Enhanced Contamination Control Elements

CCS Element	Traditional Approach	Taxonomy-Enhanced Approach
Environmental Monitoring	Colony forming units (CFUs) with limited identification	16S rRNA sequencing with phylogenetic analysis of isolates [7] [79]
Water System Control	Endotoxin and CFU monitoring	Quantitative microbial community analysis with spike-in standards [79]
Personnel Monitoring	CFU counts from contact plates	Taxonomic profiling to identify personnel-specific microbial signatures [82]
Raw Material Testing	Compendial microbiological quality tests	Quantitative microbial population study to assess risk based on taxonomic composition [80]
Cleaning Validation	Total organic carbon and CFU	Taxonomic analysis of residues to verify removal of specific microbial groups

The application of taxonomic principles enables a more scientific and risk-based approach to CCS. For instance, quantitative microbial population studies have demonstrated that microbial communities vary significantly across different geographical locations [80]. This understanding can inform the design of facility-specific control strategies based on the local microbial ecology.

Furthermore, particle-attached versus free-living microbial classifications have implications for contamination control [79]. Understanding that different microbial taxa exhibit preferences for these lifestyles can guide the design of sanitization procedures and facility flows. For example, organisms typically found in particle-attached communities may require different control measures than free-living microorganisms.

Experimental Protocols for Taxonomic Analysis

16S rRNA Gene Amplicon Sequencing with Quantitative Standards

Purpose: To quantitatively characterize microbial communities in pharmaceutical samples with high taxonomic resolution.

Materials and Reagents:

• DNA extraction kit (e.g., QIAGEN DNeasy Blood & Tissue Kit) [80]
• PCR reagents for 16S rRNA amplification
• Spike-in standards (artificial 16S rRNA sequences at known concentrations) [79]
• Illumina MiSeq platform with V3-V4 region primers [80]
• Bioinformatics tools for phylogenetic analysis (e.g., FastTree) [7]

Procedure:

• Sample Collection: Collect environmental samples (air, surface, water) or product samples using appropriate sterile techniques.
• DNA Extraction: Extract genomic DNA incorporating a known concentration of spike-in standard to enable absolute quantification [79].
• Library Preparation: Amplify the V3-V4 hypervariable region of the 16S rRNA gene using platform-specific primers [81] [80].
• Sequencing: Perform sequencing on Illumina MiSeq or comparable platform with sufficient depth (typically 50,000-100,000 reads per sample).
• Bioinformatic Analysis:
  • Perform multiple sequence alignment using state-of-the-art methods [7]
  • Generate phylogenetic trees with tools such as FastTree [7]
  • Calculate quantitative abundances using spike-in standards for normalization [79]
  • Conduct diversity analyses (alpha and beta diversity) and differential abundance testing

Quality Controls:

• Include extraction negatives and PCR negatives to monitor contamination
• Use positive controls with known microbial communities
• Implement sequence quality filtering and chimera removal
• Apply rigorous statistical thresholds for taxonomic assignment

Microbial Phylogeny and Source Tracking

Purpose: To establish phylogenetic relationships between microbial isolates for contamination source investigation.

Materials and Reagents:

• Multiple sequence alignment tools (e.g., PASTA, MUSCLE, MAFFT) [7]
• Phylogenetic tree construction software (e.g., FastTree) [7]
• Reference databases (e.g., Greengenes, Rfam) [7]

Procedure:

• Sequence Alignment: Perform multiple sequence alignment of 16S rRNA sequences using optimized methods that improve phylogenetic estimation [7].
• Tree Construction: Infer phylogenetic trees using maximum likelihood methods.
• Source Analysis: Compare phylogenetic placement of contaminant isolates with environmental isolates to identify potential sources.
• Statistical Validation: Assess tree robustness with bootstrap analysis and compare with UniFrac distances to quantify phylogenetic differences [7].

QbD Implementation Framework with Microbial Taxonomy

Defining Microbiologically-Relevant CQAs

Within the QbD framework, Critical Quality Attributes (CQAs) are physical, chemical, biological, or microbiological properties or characteristics that should be within an appropriate limit, range, or distribution to ensure the desired product quality [77] [78]. The integration of microbial taxonomy enables a more scientific approach to defining microbiologically-relevant CQAs:

• Taxonomy-Based Bioburden Limits: Establishing acceptance criteria for non-sterile products based on the taxonomic identity of contaminants rather than just total counts
• Pathogen Risk Assessment: Identifying specific taxonomic groups with pathogenic potential or ability to compromise product stability
• Process Capability Indices: Defining the capability of manufacturing processes to control specific microbial taxa based on their phylogenetic characteristics

The Quality Target Product Profile (QTPP) for sterile products should include taxonomic considerations, particularly for products vulnerable to specific contaminants. For instance, the QTPP might specify control of spore-forming organisms based on their taxonomic classification and associated resistance properties.

Design of Experiments for Microbial Control

Design of Experiments (DoE) is a structured, organized method for determining the relationship between factors affecting a process and the output of that process [78]. When applied to microbial control, DoE can be used to:

• Identify Critical Process Parameters that affect the survival of specific microbial taxa
• Establish Design Spaces for sterilization processes that account for the phylogenetic diversity of potential contaminants
• Optimize Cleaning Procedures targeted at specific taxonomic groups based on their physiological characteristics

Common experimental designs for microbial taxonomy studies include full factorial designs to evaluate multiple factors simultaneously and response surface methodologies to model complex relationships between process parameters and microbial outcomes [78].

Research Reagent Solutions for Taxonomic Studies

Table 3: Essential Research Reagents for Microbial Taxonomy Studies

Reagent / Solution	Function	Application Example
16S rRNA PCR Primers	Amplification of target gene for sequencing	Taxonomic classification of isolates [81] [80]
Spike-in Standards	Absolute quantification of microbial abundance	Quantitative bioburden assessment [79]
DNA Extraction Kits	Isolation of high-quality genomic DNA	Sample preparation for sequencing [80]
Sequence Alignment Tools	Multiple sequence alignment for phylogenetic analysis	Microbial phylogeny estimation [7]
Bioinformatics Pipelines	Processing and analysis of sequencing data	Taxonomic classification and diversity analysis [7] [80]

The integration of advanced microbial taxonomy and phylogenetics into the QbD framework represents a paradigm shift in pharmaceutical quality assurance. This approach moves beyond traditional compendial methods to establish a scientifically rigorous foundation for contamination control based on comprehensive understanding of microbial ecology, evolution, and population dynamics. By leveraging cutting-edge taxonomic methods—including high-resolution phylogenetic analysis, quantitative microbiome profiling, and multi-omic integration—pharmaceutical scientists can develop more robust manufacturing processes, implement more effective contamination control strategies, and ultimately assure higher levels of product quality and patient safety.

The future of pharmaceutical microbiology lies in the deeper integration of these taxonomic principles with quality systems, enabling predictive rather than reactive approaches to microbial quality assurance. As sequencing technologies continue to advance and computational methods become more sophisticated, the application of microbial taxonomy in pharmaceutical development will undoubtedly expand, further strengthening the scientific foundation of quality assurance in the pharmaceutical industry.

Benchmarking Taxonomic Frameworks: From Gold Standards to Novel Proposals

For decades, DNA-DNA hybridization (DDH) has served as the gold standard for species delineation in prokaryotic taxonomy, providing a critical operational definition for the basic unit of microbial diversity [84]. This methodology, rooted in the physicochemical properties of DNA reassociation, established a quantitative framework for determining genetic relatedness between bacterial strains, with a 70% DDH similarity threshold universally accepted as the primary criterion for species boundaries [11]. The technique fundamentally evaluates the extent and stability of hybrid double-stranded DNA formed from denatured mixtures of genomic DNA from different organisms under stringent conditions that permit only renaturation of highly complementary sequences [84].

Despite its foundational status, DDH presents significant limitations that have prompted the microbial taxonomy community to seek genomic alternatives. The method is technically demanding, restricted to specialized laboratories, suffers from poor reproducibility between experiments and laboratories, and generates data that are non-cumulative—each new comparison requires direct experimentation with reference strains [84]. These limitations, combined with the increasing accessibility of whole genome sequencing, have catalyzed a paradigm shift toward sequence-based taxonomic classification while maintaining the conceptual framework established by DDH [11]. This transition requires precise correlation between traditional DDH values and genomic metrics to maintain continuity in microbial classification systems that now encompass thousands of historically described species.

Core Principles and Methodologies of DNA-DNA Hybridization

Fundamental Mechanisms and Parameters

DNA-DNA hybridization techniques leverage the intrinsic property of single-stranded DNA to reassociate with complementary strands, a process governed by both the similarity of DNA base compositions and the degree of sequence complementarity between organisms [84]. The methodological spectrum of DDH encompasses two primary strategies: free-solution methods and solid-phase bound DNA methods, with variations primarily in the type of DNA label employed and the measurement technique [84]. Despite this diversity, all approaches share the common goal of measuring either the extent of hybridization or the thermal stability of the resulting hybrid DNA duplexes.

Two principal parameters are derived from DDH experiments, each providing complementary information about genomic similarity:

• Relative Binding Ratio (RBR): This measurement quantifies the amount of double-stranded hybrid DNA formed between two strains relative to the homologous reference DNA reaction (set at 100%) [84]. The RBR reflects the proportion of conserved genomic sequences between organisms and is highly dependent on the stringency of hybridization conditions, typically performed at 25-30°C below the melting point of the native reference DNA.

• Thermal Melting Stability (ΔTm): This parameter measures the difference in melting temperatures between homologous DNA duplexes and heterologous hybrid duplexes [84]. The ΔTm value reflects the thermal stability of hybrid DNA, with less stable hybrids (indicating greater sequence divergence) melting at lower temperatures. This approach is considered more reliable than RBR as it is less affected by variations in DNA quality and quantity.

Table 1: Major DNA-DNA Hybridization Methodologies and Their Characteristics

Method Type	Specific Technique	Measurement	Key Features
Free-solution	Hydroxyapatite	RBR	Uses radioactive isotopes; separates single and double-stranded DNA
Free-solution	Spectrophotometric	RBR, ΔTm	Label-free; measures reassociation kinetics
Free-solution	Fluorimetric	ΔTm	No label required; based on fluorescence signals
Bound DNA	Membrane filters	RBR, ΔTm	Radioactive or non-radioactive labels
Bound DNA	Microtiter plate	RBR	Uses photobiotin or digoxigenin labels

Standard Experimental Protocol: Hydroxyapatite Method

The hydroxyapatite method represents one of the most established approaches for determining DDH values, providing both RBR and ΔTm measurements through a standardized protocol [84]:

• DNA Extraction and Purification: Genomic DNA is extracted from pure cultures of reference and test strains using standard enzymatic or chemical methods, followed by purification to remove proteins, RNA, and other contaminants.

• DNA Labeling: The reference DNA is labeled with a radioactive isotope (e.g., ³²P) or non-radioactive markers such as digoxigenin or biotin, while test DNA remains unlabeled.

• Denaturation: Mixtures containing fixed amounts of labeled reference DNA and unlabeled test DNA are denatured by heating to approximately 100°C for 5-10 minutes to completely separate DNA strands.

• Reassociation: The denatured DNA mixtures are incubated at optimal renaturation temperatures (typically 25-30°C below the Tm of the reference DNA) for a period sufficient to allow reassociation (usually 16-24 hours).

• Hydroxyapatite Chromatography: The reaction mixture is passed through hydroxyapatite columns, which bind double-stranded DNA while allowing single-stranded DNA to pass through. The amount of bound hybrid DNA is quantified by measuring the radioactivity or enzyme activity associated with the labeled DNA.

• Data Calculation: The RBR is calculated as the percentage of labeled reference DNA bound to hydroxyapatite in heterologous reactions compared to homologous control reactions.

• Thermal Stability Analysis (for ΔTm): For methods measuring thermal stability, the temperature is gradually increased, and the amount of DNA dissociated at each temperature point is measured to determine the melting profile and calculate ΔTm.

Diagram 1: DNA-DNA Hybridization Experimental Workflow. The flowchart illustrates the key steps in the hydroxyapatite method for DDH, showing the progression from bacterial cultures to the calculation of RBR and ΔTm values.

Quantitative Correlations Between DDH and Genomic Metrics

Average Nucleotide Identity (ANI) as a Digital Replacement

The correlation between DDH values and genome sequence-derived parameters has been rigorously quantified, establishing ANI as the most accurate computational replacement for wet-lab DDH [85]. Comparative studies analyzing 124 DDH values for 28 strains with available genome sequences revealed a close mathematical relationship between these metrics, with the traditional 70% DDH species threshold corresponding precisely to 95% ANI [85]. This correlation maintains the conceptual framework of the DDH-based species definition while overcoming its methodological limitations through sequence-based computation.

The ANI approach calculates the average nucleotide identity of orthologous genes shared between two genomes, typically using BLAST-based algorithms (ANIb) or MUMmer-based approaches (ANIm) for whole-genome alignment [11]. This method provides several advantages over DDH: results are highly reproducible, calculations can be performed in silico using public genome databases, and the technique requires only approximately 20% genome coverage for reliable comparisons [84]. The robust correlation between these methods has established ANI as the modern gold standard for prokaryotic species delineation in the genomic era.

Expanded Correlation Framework with Genomic Metrics

Beyond ANI, research has identified correlations between DDH and additional genomic parameters, creating a comprehensive framework for digital taxonomy:

• Conserved DNA: The 70% DDH threshold corresponds to approximately 69% conserved DNA across entire genomes, reflecting the proportion of shared genomic content between related strains [85].

• Conserved Genes: When analysis is restricted to the protein-coding region of genomes, 70% DDH corresponds to approximately 85% conserved genes between a pair of strains, revealing substantial gene content diversity within currently recognized species boundaries [85].

• 16S rRNA Gene Identity: While traditionally used for preliminary phylogenetic placement, 16S rRNA gene identity shows only an approximate correlation with DDH values, with >98% 16S identity generally corresponding to the possibility of belonging to the same species, though significant exceptions exist [11]. Strains with >97% 16S rRNA gene identity can demonstrate DDH values below 70% due to the accumulation of divergent genes and sequence mismatches [86].

• In Silico Genome-to-Genome Hybridization: Digital alternatives to DDH, such as the Genome-to-Genome Distance Calculator (GGDC), use high-scoring segment pairs (HSPs) to infer intergenomic distances, effectively reproducing DDH results with computational efficiency [11].

Table 2: Correlation Between DDH Values and Genomic Metrics for Species Delineation

Metric	Calculation Method	Species Threshold	Advantages
DNA-DNA Hybridization	Experimental reassociation	70%	Established gold standard
Average Nucleotide Identity (ANI)	BLAST or MUMmer comparison	95%	Highly reproducible, in silico
Conserved DNA	Whole-genome alignment	69%	Reflects overall genome similarity
Conserved Genes	Protein-coding sequence comparison	85%	Focuses on functional elements
16S rRNA Identity	Sequence alignment	98-99%	Rapid preliminary assessment
In Silico GGDH	Genome-to-Genome Distance Calculator	70% similarity	Digital replacement for DDH

Molecular Basis of DDH-Genomic Correlations

The relationship between DDH values and genomic sequence similarity stems from the fundamental principle that hybridization efficiency is ultimately governed by the degree of sequence complementarity between genomes [86]. Research indicates that between closely related strains, the presence of even a limited number of highly divergent genes (<55% identity) and the accumulation of mismatches between otherwise conserved genes can substantially reduce DDH signals [86]. This explains why strains with high 16S rRNA gene identity (>97%) may still exhibit DDH values below the 70% species threshold, as localized genomic divergence significantly impacts overall hybridization efficiency.

Microarray-based studies have further elucidated that a DDH signal intensity exceeding 40% indicates that two genomes share at least 30% conserved genes with greater than 60% sequence identity [86]. This relationship demonstrates that DDH values reflect both the proportion of shared genes and their degree of sequence conservation, providing a composite measure of genomic relatedness that correlates with multiple sequence-derived parameters.

Diagram 2: Correlation Relationships Between DDH and Genomic Metrics. The diagram illustrates the quantitative relationships between traditional DDH values and modern genomic parameters used for microbial species delineation.

The Scientist's Toolkit: Essential Reagents and Methods

Table 3: Essential Research Reagents and Methods for DDH and Genomic Taxonomy

Reagent/Method	Function/Application	Specific Examples
Hydroxyapatite	Chromatographic separation of single and double-stranded DNA	Bio-Gel HTP Hydroxyapatite
DNA Labeling Systems	Tagging reference DNA for detection	Digoxigenin, Biotin, ³²P radioisotope
Restriction Endonucleases	DNA fragmentation for analysis	EcoRI, HindIII, other sequence-specific enzymes
Microarray Platforms	High-throughput hybridization analysis	Whole-genome microbial arrays
-	Computational Tools	Genome comparison and analysis	BLAST, MUMmer, GGDC, TMarSel
DNA Denaturation Agents	Strand separation for hybridization	Sodium hydroxide, heat treatment
-	Hybridization Buffers	Controlled stringency conditions	Saline-sodium citrate (SSC) buffers

Modern Genomic Approaches Replacing Traditional DDH

Tailored Marker Gene Selection for Phylogenomics

The transition from DDH to genomic taxonomy has prompted development of sophisticated computational tools for phylogenetic analysis. TMarSel (Tailored Marker Selection) represents one such advancement, enabling automated selection of phylogenetic marker genes tailored to specific input genomes, particularly valuable for analyzing metagenome-assembled genomes (MAGs) that often lack standard marker genes [12]. This approach systematically evaluates the phylogenetic signal from the entire gene family pool, moving beyond the traditional restriction to universal orthologous genes (present in ≥90% of genomes and single-copy in ≥95% of genomes), which comprise only about 1% of microbial gene families [12].

Unlike fixed marker sets that bias representation toward well-characterized taxa, tailored selection identifies markers from functionally diverse categories including metabolism, cellular processes, and environmental information processing, in addition to traditional housekeeping functions [12]. This expanded marker selection demonstrates improved phylogenetic accuracy across both whole genomes and MAGs, effectively addressing the taxonomic imbalance and incomplete genomic data that frequently challenge microbial phylogenomics.

High-Throughput Thermodynamic Measurements

Recent technological advances have enabled high-throughput analysis of DNA folding thermodynamics, providing insights relevant to understanding the biophysical principles underlying DDH. The Array Melt technique repurposes Illumina sequencing flow cells to measure the equilibrium stability of millions of DNA hairpins simultaneously through fluorescence-based quenching signals [87]. This approach has generated unprecedented datasets (27,732 sequences with two-state melting behaviors) that improve predictive models of DNA hybridization thermodynamics.

These advancements address a fundamental limitation of traditional nearest-neighbor models that struggle to accurately capture the diversity of DNA secondary structural motifs, including mismatches and bulges that influence hybridization efficiency [87]. The improved thermodynamic parameters derived from high-throughput measurements enable more effective in silico design of hybridization probes and enhance our understanding of the sequence determinants that govern DNA-DNA hybridization, bridging experimental observations with computational predictions.

The legacy of DNA-DNA hybridization as the gold standard for microbial species delineation persists not as a laboratory technique but as a conceptual framework that has successfully transitioned to the genomic era. The precise quantitative correlations established between DDH values and genomic metrics like ANI have enabled a seamless transition from experimental to computational taxonomy while maintaining continuity with historically defined species boundaries. This correlation framework ensures that the extensive existing taxonomy remains relevant while incorporating the advantages of genomic approaches: reproducibility, cumulative data generation, and computational accessibility.

As microbial taxonomy continues to evolve, the principles established by DDH—that species represent groups of strains with substantial genomic similarity—remain foundational. The development of increasingly sophisticated tools for genome comparison and phylogenetic analysis builds upon this legacy, expanding our understanding of microbial diversity while honoring the operational species concept that has guided prokaryotic taxonomy for decades. The gold standard has thus not been abandoned but rather transformed, its essential principles preserved in the digital language of genomic sequences.

The accurate classification of microorganisms is a cornerstone of microbiology, with profound implications for research, drug development, and clinical diagnostics. For decades, microbial taxonomy relied predominantly on phenotypic characteristics and limited genetic information. However, the advent of high-throughput sequencing has catalyzed a paradigm shift, enabling the development of genome-based classification systems such as the Genome Taxonomy Database (GTDB). This in-depth technical guide examines the fundamental differences between the GTDB and traditional classification schemas, providing researchers and drug development professionals with a comprehensive framework for understanding modern microbial taxonomy and phylogeny fundamentals.

The GTDB represents a standardized genome-based taxonomy that addresses longstanding inconsistencies in bacterial and archaeal classification. By leveraging whole-genome sequences and phylogenetic principles, the GTDB offers a robust framework that diverges significantly from traditional, often phenotype-heavy approaches documented in foundational microbiological resources [88]. This analysis explores the technical foundations, methodological approaches, and practical implications of both systems within the broader context of microbial taxonomy and phylogeny research.

Foundations of Traditional Microbial Classification

Traditional microbial classification systems have primarily relied on phenotypic characteristics and limited genetic markers to delineate taxonomic groups. These approaches, developed over more than a century of microbiological research, form the basis of classification schemas still used in many clinical and applied settings.

Core Principles and Methodologies

• Morphological Characteristics: Traditional classification begins with observable traits including cell shape (cocci, bacilli, vibrio, spirilla), colonial morphology, Gram-staining properties, and structural features such as spores or capsules [89]. These characteristics provide initial taxonomic grouping but lack sufficient resolution for precise species identification.

• Biochemical Profiling: Microorganisms are distinguished based on metabolic capabilities, including carbon source utilization, enzyme activities, and metabolic byproducts. Tests for catalase, oxidase, fermentation patterns, and nitrate reduction form the backbone of this approach [88] [89].

• Growth Requirements: Classification incorporates optimal growth parameters including temperature ranges (psychrophiles, mesophiles, thermophiles), oxygen requirements (obligate aerobes, anaerobes, facultative anaerobes), and pH preferences [89].

• Serological and Phage Typing: Strain differentiation employs antigenic properties (O, H, and K antigens) and susceptibility to specific bacteriophages, particularly valuable for epidemiologic surveillance of pathogens like Salmonella and Staphylococcus aureus [88] [89].

Limitations of Traditional Approaches

While these methods established the foundation of microbial taxonomy, they present significant limitations. Phenotypic plasticity means environmentally influenced traits may not reflect evolutionary relationships [88]. The subjective weighting of characteristics lacks consistent principles, and different taxonomic schemes often produce conflicting classifications [88]. Additionally, limited resolution prevents accurate distinction between closely related species, as biochemical profiles may be identical despite genomic divergence [90]. Perhaps most importantly, these approaches provide incomplete evolutionary insights, as phenotypic similarity does not necessarily reflect phylogenetic relatedness [88].

The Genome Taxonomy Database (GTDB): A Genomic Framework

The Genome Taxonomy Database represents a fundamental restructuring of microbial taxonomy based on phylogenetic principles applied to whole-genome data. Developed by the Australian Centre for Ecogenomics, GTDB provides a standardized, phylogenetically consistent taxonomy for bacteria and archaea [91].

Philosophical and Technical Foundations

GTDB addresses key limitations in traditional classification by establishing an objective genomic framework that:

• Uses reference trees inferred from concatenated sets of 120 single-copy proteins for Bacteria and 53 marker proteins for Archaea [92] [91]
• Implements automated species clustering using Average Nucleotide Identity (ANI) criteria with a 95% circumscription radius [92]
• Applies rank normalization to ensure consistent taxonomic levels across the microbial tree of life [91]
• Incorporates quality-controlled genomes from both cultured organisms and metagenome-assembled genomes (MAGs) [92]

Methodological Workflow

The GTDB classification pipeline follows a rigorous computational workflow:

• Genome Quality Control: Assembled genomes must meet strict quality thresholds including CheckM completeness >50%, contamination <10%, and quality score >50 [92].

• Marker Gene Identification: A conserved set of single-copy marker genes are identified using HMMER against Pfam and TIGRFAM models [92].

• Multiple Sequence Alignment: Concatenated marker genes are aligned and filtered to remove poorly aligned regions [92].

• Phylogenetic Inference: Reference trees are constructed using FastTree (Bacteria) and IQ-TREE (Archaea) with appropriate evolutionary models [92] [91].

• Taxonomic Assignment: Genomes are placed within the reference tree using GTDB-Tk, which calculates relative evolutionary divergence and applies rank normalization [92].

Comparative Analysis: Methodological Differences

The fundamental differences between GTDB and traditional classification systems span philosophical foundations, technical approaches, and practical implementation.

Table 1: Core Methodological Differences Between Classification Systems

Aspect	Traditional Systems	GTDB
Primary Data Source	Phenotypic traits, biochemical patterns, limited genetic markers [88]	Whole genome sequences, protein-coding genes [92]
Basis of Classification	Practical utility, phenotypic similarity [88]	Evolutionary relationships, phylogenetic principles [91]
Species Delineation	Biochemical profiles, DNA-DNA hybridization (<70% cutoff) [88] [93]	Average Nucleotide Identity (≥95% cutoff) [92]
Resolution Capability	Limited to species/subspecies level [90]	High resolution to strain level [94]
Handling of Uncultured Taxa	Limited to non-existent	Comprehensive inclusion via MAGs [91]
Automation Potential	Low, requires expert interpretation [88]	High, computational pipeline [92]

Species Concept and Delineation

The definition of microbial species represents perhaps the most significant distinction between classification approaches:

• Traditional Concept: Historically defined by a polythetic approach combining phenotypic characteristics and DNA-DNA hybridization (DDH), with ≥70% similarity indicating conspecificity [88] [93]. This method is labor-intensive and difficult to standardize.

• GTDB Concept: Employs Average Nucleotide Identity with a 95% cutoff, calculated from pairwise comparisons of all orthologous genes between genomes [92] [93]. This approach is automated, reproducible, and precisely quantifiable.

The GTDB methodology has revealed significant inaccuracies in traditional classifications. For example, in the Klebsiella pneumoniae species complex, GTDB accurately distinguishes between closely related species (K. pneumoniae, K. quasipneumoniae, and K. variicola) that are frequently misidentified by traditional biochemical tests and even by some modern genetic methods [94].

Taxonomic Representation and Coverage

As of Release 10-RS226 (April 2025), GTDB provides taxonomic classification for 732,475 genomes, representing 136,646 bacterial and 6,968 archaeal species clusters [95]. This extensive coverage includes numerous previously uncultivated lineages that were absent from traditional classification systems, substantially expanding our view of microbial diversity.

Table 2: Quantitative Comparison of Taxonomic Representation in GTDB Release 10

Taxonomic Rank	Bacteria	Archaea
Phylum	169	20
Class	571	63
Order	1,976	171
Family	5,311	603
Genus	27,326	2,079
Species	136,646	6,968

Experimental Protocols for Taxonomic Classification

GTDB-Tk Classification Workflow

The GTDB provides GTDB-Tk as a standardized toolkit for classifying genomes according to its taxonomy. The following protocol details the experimental and computational workflow:

Diagram: GTDB-Tk Classification Workflow

Procedure:

• Genome Assembly: Sequence and assemble microbial genomes using preferred platforms (Illumina, PacBio, or Oxford Nanopore) [93].
• Quality Control: Assess genome quality using CheckM to ensure completeness >50%, contamination <10%, and quality score >50 [92].
• Marker Gene Identification: Identify 120 (bacterial) or 53 (archaeal) single-copy marker genes using HMMER v3.1b1 against Pfam v33.1 and TIGRFAMs v15.0 databases [92].
• Multiple Sequence Alignment: Concatenate marker genes and align using MAFFT or similar tools. Filter alignment to remove columns with >50% gaps or amino acids present in <25% or >95% of taxa [92].
• Phylogenetic Placement: Infer phylogenetic position using FastTree v2.1.10 (Bacteria) under WAG model or IQ-Tree v1.6.9 (Archaea) under PMSF model [92].
• Taxonomic Assignment: Classify genomes using GTDB-Tk, which places genomes in the reference tree and assigns taxonomy based on relative evolutionary divergence [92].

Traditional Polyphasic Taxonomy Protocol

Traditional classification employs a polyphasic approach integrating multiple data types:

Diagram: Traditional Polyphasic Classification Approach

Procedure:

• Culture Isolation: Purify strains using appropriate growth media and conditions [88] [89].
• Morphological Analysis: Examine cell morphology, Gram-staining characteristics, motility, and colonial morphology on various media [89].
• Biochemical Characterization: Perform standardized tests (API, Biolog) including carbon source utilization, enzyme activities (oxidase, catalase), and metabolic capabilities [88] [89].
• 16S rRNA Gene Sequencing: Amplify and sequence the 16S rRNA gene, compare to databases (e.g., SILVA, Greengenes) for preliminary identification [96].
• DNA-DNA Hybridization: For closely related strains with high 16S similarity (>97%), perform DDH to confirm species boundaries (≥70% similarity indicates conspecificity) [88] [93].
• Data Integration: Synthesize all phenotypic and genotypic data to assign taxonomic classification, often requiring expert interpretation [88].

Case Study: Bacillus Velezensis Classification

A recent study examining nine Bacillus strains isolated from Brazilian soil illustrates the practical differences between classification approaches [93]. When analyzed using MALDI-TOF MS and biochemical profiling, the strains were identified as B. velezensis with some ambiguity toward B. amyloliquefaciens.

Genomic analysis using GTDB methodologies revealed a more complex picture:

• ANI values of 95% to 98.04% with B. velezensis NRRL B-41580 confirmed classification within this species [93]
• Digital DNA-DNA hybridization (dDDH) showed 89.3% to 91.8% similarity in the Type Strain Genome Server (TYGS) [93]
• Phylogenomic analysis clearly distinguished strains BAC144 and BAC1273, which clustered with Bacillus amyloliquefaciens subsp. plantarum FZB42 (now reclassified as B. velezensis) [93]
• Pangenome analysis revealed an open pangenome, highlighting genetic diversity within the species that was not apparent from phenotypic methods alone [93]

This case study demonstrates how GTDB provides resolution beyond traditional methods, accurately delineating taxonomic boundaries that phenotypic approaches struggle to distinguish.

Research Reagent Solutions and Computational Tools

Implementation of genomic taxonomy requires specific research reagents and computational resources. The following toolkit details essential components for taxonomic research.

Table 3: Essential Research Reagent Solutions for Taxonomic Studies

Tool/Reagent	Application	Function
GTDB-Tk [92]	Genomic taxonomy	Standardized genome classification against GTDB
CheckM [92]	Quality control	Assesses genome completeness and contamination
FastTree [92]	Phylogenetics	Infers approximate maximum-likelihood trees
IQ-TREE [92]	Phylogenetics	Infers maximum-likelihood phylogenies
Prodigal [92]	Gene prediction	Identifies protein-coding sequences
HMMER [92]	Sequence analysis	Identifies marker genes using profile HMMs
MALDI-TOF MS [93]	Traditional identification	Rapid identification based on protein mass fingerprints
API/Biolog Kits [88] [89]	Biochemical profiling	Standardized phenotypic characterization
ThruPLEX DNA-Seq Kit [93]	Library preparation	Constructs sequencing libraries for NGS

Implications for Research and Drug Development

The transition to genome-based classification systems has profound implications for microbial research and therapeutic development:

Research Applications

• Comparative Genomics: GTDB enables robust comparative studies across evolutionarily meaningful groups, facilitating identification of lineage-specific genetic elements [94].
• Metagenomic Analysis: Standardized taxonomy allows consistent classification of metagenome-assembled genomes, enabling more accurate ecological interpretations [91].
• Evolutionary Studies: Phylogenetically consistent taxonomy provides proper framework for studying gene family evolution, horizontal gene transfer, and functional diversification [97].

Drug Development and Clinical Applications

• Pathogen Identification: Accurate species delineation is critical for understanding pathogenicity profiles and antimicrobial resistance patterns [94]. For example, distinguishing between Klebsiella species is essential as they differ in disease severity, antibiotic resistance, and clinical outcomes [94].
• Bioprospecting: Phylogenetically informed sampling strategies enhance discovery of novel bioactive compounds from microbial sources [93].
• Diagnostic Development: Species-specific marker genes identified through genomic analyses enable targeted diagnostic assays for clinically relevant pathogens [94].

The comparative analysis of GTDB and traditional classification schemas reveals a fundamental transformation in microbial taxonomy. The GTDB framework provides a standardized, phylogenetically consistent approach that addresses longstanding limitations of phenotype-based systems. While traditional methods retain utility for certain applications, particularly in clinical settings where rapid phenotypic identification is practical, genomic approaches offer unprecedented resolution, consistency, and scalability.

The adoption of GTDB represents more than a technical advancement—it constitutes a conceptual shift in how we define, classify, and understand microbial diversity. For researchers and drug development professionals, familiarity with both systems is essential, as the field transitions toward genomic taxonomy while maintaining connections to established literature and diagnostic practices. As microbial genomics continues to evolve, the integration of phylogenetic principles with functional annotation promises to further refine our understanding of the microbial world, with significant implications for therapeutic development, diagnostic medicine, and fundamental biological research.

The delineation of species and genus ranks represents a fundamental challenge in microbial taxonomy. In the genomic era, the adoption of quantitative thresholds has brought unprecedented standardization to this field. The 95% Average Nucleotide Identity (ANI) and 70% digital DNA-DNA hybridization (dDDH) benchmarks have emerged as the central criteria for prokaryotic species boundaries [98] [99] [85]. These genomic metrics have effectively replaced wet-lab DDH methods, providing reproducible, cumulative data that facilitates direct comparison between laboratories [99]. This technical guide examines the evidentiary foundation for these thresholds, details standardized protocols for their calculation, and explores their application within modern microbial taxonomy and phylogeny research frameworks.

Historical Foundation and Threshold Validation

The Transition from Wet-Lab to Digital Taxonomy

The 70% DDH threshold served as the gold standard for species delineation for nearly five decades, based on its ability to define coherent genomic groups (genospecies) that generally corresponded with phenotypic classifications [99]. However, this method presented significant limitations: technical complexity, inability to build cumulative databases, and considerable experimental error [99] [85].

The correlation between DDH values and whole-genome sequence similarities was quantitatively established by Goris et al. (2007), who demonstrated that the 70% DDH species demarcation threshold corresponds to 95% ANI [85]. This foundational study analyzed 124 DDH values for 28 strains with available genome sequences, revealing a close mathematical relationship between DDH values and ANI that provided the empirical basis for current standards.

Empirical Validation of Species Boundaries

Subsequent research has refined these boundaries, with Richter and Rosselló-Móra (2009) recommending an ANI boundary of 95-96% after correlating ANI values with DDH values across diverse bacterial groups [98] [99]. This range accounts for methodological variations and biological complexities observed across different prokaryotic lineages.

Table 1: Established Genomic Thresholds for Species Delineation

Metric	Threshold	Corresponding Value	Primary Reference
DDH (wet-lab)	70%	N/A	Wayne et al., 1987
ANI (in silico)	95-96%	70% DDH	Goris et al., 2007; Richter & Rosselló-Móra, 2009
dDDH (in silico)	70%	95% ANI	Meier-Kolthoff et al., 2013

Recent applications demonstrate the resolving power of these thresholds. In the order Rhizobiales, ANI analysis of 520 genome sequences revealed multiple synonymies and misclassified genomes, including the reclassification of Aminobacter ciceronei as A. heintzii [98]. Similarly, studies on Streptomyces have identified novel strains using the 95% ANI threshold, with one plant-associated strain showing ANI values "considerably lower than the recommended threshold values" when compared to closely related type strains [100].

Computational Methodologies and Protocols

ANI Calculation: Core Algorithms and Implementation

ANI calculation involves a pairwise comparison of two genomes to determine the average nucleotide identity of their shared coding regions. Two primary computational approaches have been established:

• ANIb (BLAST-based): Fragments the query genome into consecutive 1,020 nt fragments, which are aligned against the reference genome using BLASTn [98]. The ANI is calculated as the mean identity of all BLASTn matches that show >30% overall sequence identity over an alignable region of at least 70% of their length [98].

• ANIm (MUMmer-based): Utilizes the MUMmer software package with efficient suffix trees to rapidly align sequences containing millions of nucleotides [99]. This method demonstrates significantly faster processing times while maintaining precision comparable to BLAST-based approaches [99].

The JSpecies software package provides a biologist-oriented interface for calculating both ANIb and ANIm, along with tetranucleotide signature correlation indices that complement ANI analysis [99].

Digital DDH Calculation Methods

Digital DDH employs the Genome BLAST Distance Phylogeny (GBDP) approach to approximate experimental DDH values. Formula d0 and d6 most closely mirror the properties of experimentally determined DDH values [98]. The dDDH method establishes a 70% boundary for species delineation based on comprehensive comparisons with in vitro DDH [98].

Table 2: Standardized Protocols for Genomic Metrics Calculation

Method	Tool	Key Parameters	Output Interpretation
ANIb	JSpecies	Fragment size: 1020 nt; Min alignment: 70%; Min identity: 30%	≥95% = conspecific
ANIm	JSpecies	MUMmer alignment algorithm	≥95% = conspecific
dDDH	GGDC/GBDP	Formula d0 or d6	≥70% = conspecific
FastANI	FastANI	Kmer size: 16; Min fraction: 50%	≥95% = conspecific

For yeast taxonomy, where ANI application is emerging, recent evidence suggests that FastANI effectively distinguishes between strains belonging to different species with boundaries at 94-96%, demonstrating its utility beyond prokaryotes [101].

Workflow Visualization: Genomic Species Delineation

The following diagram illustrates the standardized workflow for prokaryotic species delineation using genomic metrics:

Experimental Design and Quality Control

Genome Sequence Quality Standards

Robust taxonomic assignment requires stringent quality control measures for genome sequences:

• Minimum Sequencing Coverage: A coverage of ≥12× has been established as necessary to maintain essential genomic features and typing accuracy in PromethION nanopore-based sequencing [102].

• Type Strain Verification: Cross-referencing with the Straininfo bioportal and List of Prokaryotic Names with Standing in Nomenclature (LPSN) is essential, as studies indicate less than 30% of sequenced genomes labeled as type strains actually represent the genuine type material [99].

• Completeness Assessment: Tools like BUSCO assess genome completeness and contamination, particularly important for draft genomes [101].

Table 3: Essential Resources for Genomic Taxonomy Research

Resource Category	Specific Tools/Reagents	Function/Purpose
Wet-Lab Materials	High Pure PCR Template Preparation Kit (Roche)	High-quality DNA extraction for WGS
	Sensititre Custom Plates (Thermo Fisher)	Phenotypic validation via MIC assays
	Tryptic Soy Agar +5% sheep blood	Standardized bacterial cultivation
Bioinformatics Tools	JSpecies	ANIb/ANIm & tetranucleotide analysis
	FastANI	Rapid alignment-free ANI calculation
	GGDC (GBDP)	digital DDH calculation
	antiSMASH	Secondary metabolite cluster analysis
	ProKlust (R package)	Downstream analysis of large ANI matrices
Reference Databases	LPSN (List of Prokaryotic Names)	Nomenclature validation
	Straininfo Bioportal	Type strain verification
	NCBI Genome Database	Reference genome sequences

Applications and Advanced Implementation

Strain Typing with Enhanced Resolution

While ANI and dDDH were developed for species delineation, they show promise for high-resolution strain typing. Recent research on Escherichia coli clinical isolates established refined cut-offs of 99.3% ANI and 94.1% dDDH for discriminative strain resolution, potentially offering superior resolution compared to traditional MLST schemes [102].

Phylogenomic Integration

Core gene analysis provides a complementary approach for robust phylogenetic placement. The 20 validated bacterial core genes (VBCG) tool uses genes with high phylogenetic fidelity to reconstruct evolutionary relationships with improved accuracy [103]. This integration of ANI thresholds with phylogenomic approaches represents the current gold standard in microbial systematics.

The following diagram illustrates the relationship between different genomic analysis methods and their taxonomic applications:

Beyond Prokaryotes: Application to Yeast Taxonomy

The application of ANI analysis has expanded to eukaryotic microorganisms. Recent research demonstrates that FastANI effectively distinguishes yeast species with clear boundaries at 94-96% identity, providing a rapid method for species attribution based on whole genome sequences [101]. This approach has proven particularly valuable for identifying hybrid strains within the Saccharomyces genus, where traditional barcoding methods face limitations due to intragenomic variations [101].

Limitations and Future Perspectives

Critical Considerations for Threshold Application

While the 95% ANI and 70% dDDH thresholds provide robust guidelines, several limitations warrant consideration:

• Universal Applicability: These thresholds were calibrated primarily against Enterobacteriaceae, and independent evolutionary trajectories may result in species cohesion at different similarity levels for certain taxonomic groups [98].

• Alignment Fraction Requirements: ANI values must be interpreted alongside alignment coverage metrics (≥50-70% recommended) to ensure sufficient genomic overlap, particularly when horizontal gene transfer may create artificially high identities in limited shared regions [98].

• Database Accuracy: The problem of misidentified genomes in public databases necessitates careful verification against type strains to ensure taxonomic conclusions are valid [99].

Emerging Methodological Developments

Future directions in genomic taxonomy include:

• Integration of Phenotypic Data: Modern taxonomy increasingly combines genomic thresholds with phenotypic characteristics through polyphasic approaches [56].

• High-Throughput Sequencing Platforms: Optimized protocols for platforms like PromethION nanopore sequencing are expanding access to WGS-based taxonomy for clinical and industrial applications [102].

• Phylogenomic Validation: Tools like VBCG that select core genes based on phylogenetic fidelity rather than mere presence are improving the accuracy of evolutionary placement [103].

The establishment of quantitative thresholds, specifically 95% ANI and 70% dDDH, has transformed microbial taxonomy by providing standardized, reproducible criteria for species delineation. These genomic metrics have successfully replaced traditional DDH methods, enabling cumulative databases and direct interlaboratory comparisons. When implemented with appropriate quality controls and integrated with phylogenomic approaches, these thresholds provide a powerful framework for taxonomic identification that serves diverse fields from clinical microbiology to bioprospecting. As sequencing technologies continue to evolve and databases expand, these standards will continue to form the foundation of prokaryotic systematics while accommodating methodological refinements and taxonomic discoveries.

The field of bacterial classification is undergoing a profound transformation, driven by the advent of high-throughput sequencing technologies and the systematic application of genomic data. The recent expansion to 99 formally recognized bacterial phyla is not merely a numerical increase; it represents a fundamental shift in our understanding of life's diversity and evolutionary history. This taxonomic revolution has been catalyzed by the move from phenotype-based classification to a comprehensive genome-based phylogenetic framework, allowing researchers to finally construct a stable and natural hierarchy that reflects true evolutionary relationships [104] [105].

This expansion is particularly significant as it incorporates vast numbers of previously uncultured prokaryotes discovered through metagenomic sequencing, revealing an astonishing breadth of microbial diversity that was largely inaccessible through traditional culturing methods [105]. The establishment of a standardized taxonomy has created a unified language for researchers, enabling robust comparison across studies and accelerating discoveries in fields ranging from drug development to ecosystem ecology. By framing this taxonomic expansion within the context of microbial taxonomy and phylogeny fundamentals, this review aims to provide researchers with the methodological foundation necessary to navigate and contribute to this rapidly evolving landscape.

The Evolution of Bacterial Taxonomy: From Phenotypes to Genomes

The history of prokaryotic taxonomy reveals a continual refinement of methods and principles, each technological advance providing deeper insights into microbial relationships. The journey began with phenotypic classification, pioneered by Ferdinand Cohn in the 1870s, who used morphological traits to define six bacterial genera [104]. This approach was later systematized through the first edition of Bergey's Manual of Determinative Bacteriology in 1923, which established identification keys based on morphology, culturing conditions, and pathogenic characteristics [105]. The 1960s saw the introduction of numerical taxonomy, which employed mathematical methods to compare dozens of phenotypic properties quantitatively, though it still lacked a rigorous evolutionary framework [104] [105].

A critical turning point came with the introduction of molecular techniques. The 1960s brought DNA guanine and cytosine content (GC mol%) and DNA-DNA hybridization (DDH) for species delineation [104]. However, the most transformative advancement was the use of small subunit ribosomal RNA (16S rRNA) as a molecular chronometer by Carl Woese and colleagues [105]. This approach revealed the tripartite division of life into Archaea, Bacteria, and Eukarya and provided the first objective evolutionary framework for microbial classification [104] [105]. The 16S rRNA gene was also instrumental in highlighting the enormous diversity of uncultured microorganisms through environmental sequencing [105].

The current era of genome-based classification represents the most significant paradigm shift. As sequencing technologies advanced, the field transitioned from single-gene analyses to comprehensive whole-genome comparisons [105]. Genome sequences provide substantially greater phylogenetic resolution than the 16S rRNA gene (which represents only about 0.05% of an average prokaryotic genome) for both ancient and recent evolutionary relationships [105]. This transition has enabled the development of robust phylogenetic frameworks using either supertree approaches (combining independent gene trees) or supermatrix methods (concatenating genes into a single alignment) [105].

Table: Historical Transitions in Bacterial Taxonomic Methods

Era	Primary Methods	Key Innovations	Limitations
Phenotypic (1870s-1960s)	Morphology, biochemical traits, culturing conditions [104]	Bergey's Manual, numerical taxonomy [105]	Limited evolutionary insight, culture-dependent
Molecular (1970s-2000s)	16S rRNA sequencing, DNA-DNA hybridization, GC content [104] [105]	Phylogenetic framework, discovery of Archaea [105]	Single gene may not represent genome evolution
Genomic (2010s-present)	Whole-genome sequencing, average nucleotide identity, phylogenetic trees from conserved genes [105]	Genome Taxonomy Database (GTDB), metagenome-assembled genomes (MAGs) [105] [106]	Computational complexity, integration of uncultured diversity

The impact of these transitions cannot be overstated. The move to genomics has revealed that bacterial diversity is vastly underestimated, with estimates suggesting approximately 10^12 bacterial species on Earth, while only about 17,845 had valid species names as of November 2021 [104]. The expansion to 99 bacterial phyla is a direct result of these methodological advances, particularly through the recovery of metagenome-assembled genomes (MAGs) from environmental samples [105].

Methodological Foundations of Modern Bacterial Taxonomy

Genomic Standards and Criteria for Taxonomic Delineation

Modern bacterial taxonomy employs multiple quantitative genomic indices to establish robust taxonomic boundaries. The basic unit of taxonomy remains the species, which the Genomic Era has redefined using precise molecular measures [104]. For species delineation, Average Nucleotide Identity (ANI) has emerged as a gold standard, with values above 95% typically indicating members of the same species [105] [106]. The Average Amino Acid Identity (AAI), which uses protein sequences instead of genomic DNA, provides complementary evidence for taxonomic assignment, with proposed values between 65% and 95% for genomes from the same genus [106].

For genus-level classification, the Percentage of Conserved Proteins (POCP) has become a widely adopted metric. Originally proposed with a threshold of 50%, this method calculates the proportion of conserved proteins between two genomes, where proteins are considered conserved if they show sequence identity >40% and aligned region >50% of the query protein length [106]. Recent research has refined this approach to POCP with unique matches (POCPu), which accounts for duplicate genes (paralogs) and demonstrates improved differentiation between within-genus and between-genera comparisons [106].

Table: Genomic Indices for Taxonomic Delineation

Index	Calculation Method	Taxonomic Level	Typical Thresholds	Key Applications
Average Nucleotide Identity (ANI) [105] [106]	Nucleotide sequence comparison of orthologous genes	Species	≥95% for same species [106]	Primary species demarcation
Average Amino Acid Identity (AAI) [106]	Amino acid sequence comparison of orthologous proteins	Genus to Species	65-95% for same genus [106]	Supplementary evidence for various ranks
Percentage of Conserved Proteins (POCP) [106]	Reciprocal protein BLAST with identity >40% and alignment >50% of query	Genus	~50% for same genus [106]	Genus-level classification
Digital DNA-DNA Hybridization (dDDH) [105]	In silico simulation of laboratory DDH	Species	≥70% for same species [105]	Species demarcation

Phylogenetic Tree Construction from Genomic Data

Phylogenetic trees provide the evolutionary framework essential for modern taxonomy, representing genetic similarities and evolutionary relationships between organisms through branch lengths and topological structure [107]. The construction of robust phylogenetic trees from microbiome data follows a structured workflow, with key differences between 16S rRNA and whole-genome shotgun (WGS) sequencing approaches.

For 16S rRNA data, the process is well-established and relies primarily on sequence similarities within conserved regions of the 16S gene [107]. Standard tools include MAFFT, Clustal Omega, and MUSCLE for multiple sequence alignment, which typically use global alignment methods to maximize similarity across the entire sequence length [107]. For whole-genome shotgun data, the process is more complex due to the vast diversity of genomic regions, requiring reference databases and often employing local alignment methods like Bowtie 2, HISAT 2, or Minimap 2 that focus on regions of high similarity [107].

The fundamental steps in phylogenetic tree construction include:

• Sample collection and sequencing (16S rRNA or WGS)
• Quality control and denoising of raw sequences
• Sequence alignment using appropriate tools for each data type
• Tree construction using methods such as maximum likelihood or Bayesian inference

The resulting phylogenetic trees serve as critical inputs for downstream analyses, including diversity measures (e.g., UniFrac distance), statistical modeling, and association studies with clinical or environmental variables [107].

Integration of Uncultured Diversity through Metagenomics

A pivotal advancement enabled by standardized genomic taxonomy is the systematic incorporation of uncultured prokaryotes into the taxonomic framework. Traditional classification required cultivated isolates, ignoring the estimated 99% of microorganisms that resist laboratory cultivation [104] [105]. Metagenomic sequencing of environmental DNA now allows recovery of near-complete or complete genome sequences of naturally occurring microbial populations as metagenome-assembled genomes (MAGs) [105].

The Candidatus status provides provisional naming for incompletely characterized prokaryotes that cannot be cultivated [104]. However, with the rapid increase in quality MAGs, there is growing pressure to formally incorporate these taxa into the valid nomenclature, potentially requiring modifications to the International Code of Nomenclature of Prokaryotes (ICNP) or creating a new code specifically for uncultured taxa [104] [105]. This integration is essential for comprehensively representing microbial diversity, particularly from extreme environments and host-associated microbiomes where cultivation success remains low.

Key Databases and Computational Tools

Table: Essential Research Reagent Solutions for Bacterial Taxonomy

Resource Type	Specific Tools/Databases	Primary Function	Application Context
Genomic Databases	Genome Taxonomy Database (GTDB) [106], RefSeq [106]	Standardized taxonomic framework, curated genomes	Reference-based classification, phylogenetic placement
Nomenclature Resources	List of Prokaryotic Names with Standing in Nomenclature (LPSN) [104], International Code of Nomenclature of Prokaryotes (ICNP) [104]	Valid names, taxonomic status, nomenclatural rules	Proper naming and publication of novel taxa
Sequence Analysis Tools	DIAMOND [106], BLASTP [106], OrthoANI [105]	Rapid protein sequence comparison, ANI calculation	POCP analysis, species delineation
Phylogenetic Tools	MAFFT [107], Clustal Omega [107], MUSCLE [107], IQ-TREE, RAxML	Multiple sequence alignment, tree inference	Phylogenetic framework construction
Metagenomic Processing	Bowtie 2 [107], HISAT 2 [107], Minimap 2 [107], MetaPhlAn	Read alignment, MAG reconstruction	Incorporation of uncultured diversity

Experimental Protocol: Genus Delineation Using POCP/POCPu Analysis

For researchers proposing novel bacterial genera, the following detailed protocol provides a standardized approach for genus delineation using percentage of conserved proteins analysis:

• Genome Selection and Quality Control

  • Select high-quality genomes of the target organism and related type strains from databases such as GTDB or RefSeq [106].
  • Ensure genomes meet quality thresholds (e.g., completeness >90%, contamination <5%) as assessed by tools like CheckM.

• Proteome Prediction and Standardization

  • Predict protein sequences from genomes using Prodigal v2.6.3 or similar gene-finding software [106].
  • Standardize protein sequence files to ensure consistent formatting and annotation.

• All-vs-All Protein Comparison

  • Perform reciprocal BLAST-like searches between all protein sequences of compared genomes using DIAMOND with sensitive or very-sensitive settings for accelerated computation [106].
  • Apply filtering thresholds: e-value <10^-5, sequence identity >40%, and aligned region >50% of query protein length [106].

• POCP Calculation

  • Calculate POCP values using the formula:
    where CQS represents conserved proteins from query Q against subject S, CSQ represents conserved proteins from subject S against query Q, and TQ + TS represents the total number of proteins in both genomes [106].

• POCPu Calculation (Recommended)

  • Calculate POCPu considering only unique matches to account for paralogous genes:
    where CuQS and CuSQ represent conserved proteins with unique matches only [106].

• Threshold Application and Interpretation

  • Compare calculated values against threshold criteria (approximately 50% for genus demarcation).
  • Consider family-specific deviations from the general threshold, as some bacterial groups may require adjusted values [106].
  • Integrate POCP/POCPu results with other genomic indices (ANI, AAI) and phylogenetic analyses for robust taxonomic assignment.

This protocol enables rapid, reproducible genus assignment, facilitating the classification of novel taxa within the expanding framework of bacterial diversity.

Implications for Research and Drug Development

The standardization of bacterial taxonomy and the recognition of 99 phyla has profound implications for microbial ecology, therapeutic development, and clinical practice. For drug development professionals, this refined classification enables more targeted exploration of microbial natural products and virulence factors. The expanded phylogenetic framework allows researchers to identify evolutionary patterns in biosynthetic gene clusters and prioritize strains from under-explored phylogenetic groups for drug discovery [105].

In clinical microbiology, precise taxonomic assignment enhances our understanding of pathogen evolution and antibiotic resistance mechanisms. Strain-level classification facilitates tracking of outbreak lineages and identification of genetic determinants of virulence [107]. For researchers investigating host-microbiome interactions, the standardized taxonomy enables correlation of specific bacterial clades with health outcomes, potentially revealing novel therapeutic targets [107].

The expansion to 99 phyla also underscores the immense unexplored metabolic diversity within the bacterial domain. Each newly recognized phylum represents unique evolutionary solutions to environmental challenges, encoding novel enzymes and biochemical pathways with potential biotechnological and therapeutic applications. This comprehensive taxonomic framework thus serves not only as a classification system but as a roadmap for exploring bacterial functionality across the full spectrum of microbial diversity.

Taxonomy, the science of classification, identification, and nomenclature, provides the essential framework for microbiology that enables clear communication among scientists and clinicians [88]. For clinical and biotechnological applications, validated microbial taxonomy is not merely an academic exercise but a fundamental prerequisite for accurate diagnostics, effective treatment, and systematic strain selection in drug discovery. The validation process ensures that species names mean the same thing to all microbiologists, which is particularly crucial when dealing with pathogenic organisms where misidentification can lead to inappropriate patient care [88]. Within the broader context of microbial taxonomy and phylogeny fundamentals research, validation serves as the critical bridge between theoretical classification and practical application, ensuring that taxonomic determinations are reproducible, consistent, and clinically actionable.

The exponential growth in microbial genome sequencing has dramatically transformed taxonomic validation, with over 1.9 million bacterial genomes now available in databases [22]. This wealth of genetic information, combined with advances in computational biology, has enabled a shift from phenotype-based classification to methods incorporating DNA relatedness and overall genetic similarity [88]. The integration of multi-omic data sets provides unprecedented insights into microbial physiology and creates new opportunities for understanding microorganisms in clinical and industrial contexts [22]. This technical guide examines the methodologies, applications, and implementation frameworks for validating microbial taxonomy across clinical and biotechnological domains, with particular emphasis on practical protocols and analytical workflows.

Core Principles and Definitions

Taxonomy in microbiology encompasses three distinct but interrelated components: classification (the orderly arrangement of bacteria into groups), identification (the practical use of classification criteria to distinguish certain organisms from others), and nomenclature (the naming of organisms) [88]. A robust taxonomic validation framework must address all three components to ensure results are biologically meaningful, technically reproducible, and clinically relevant.

Species Definition in the Genomic Era

The concept of a bacterial species has evolved significantly with technological advances. A bacterial species is now understood as "a distinct organism with certain characteristic features, or a group of organisms that resemble one another closely in the most important features of their organization" [88]. Modern species definitions incorporate both phenotypic characteristics and genetic relatedness, with DNA hybridization values and Average Nucleotide Identity (ANI) providing quantitative measures for species delineation [88] [108].

Hierarchical Relationships in Information Science

In the context of information architecture, taxonomies represent controlled vocabularies—planned, prescriptive ways of adding descriptive metadata to content so it can be retrieved effectively [109]. These structured classification systems differ from, but complement, other organizational models including navigation structures, information architecture (IA) structures, and content models [109]. Understanding these relationships is crucial for implementing taxonomic systems in bioinformatics platforms and database architectures.

Table 1: Types of Organizational Models in Information Architecture

Model Type	Definition	Role in Microbial Taxonomy
Taxonomy	A closed list of acceptable terms arranged hierarchically to describe and classify content	Provides controlled vocabulary for consistent organism classification and metadata tagging
Navigation	UI elements that show users their current location and navigation options	Front-end interfaces for browsing taxonomic databases
IA Structure	A comprehensive map of all key nodes and relationships	The underlying architecture organizing taxonomic relationships and data connections
Content Models	Definitions of content types, their components, and metadata relationships	Specifies data structures for storing taxonomic information and associated metadata

Methodological Approaches for Taxonomic Validation

Phenotypic and Biochemical Characterization

Traditional phenotypic characterization remains a foundation for microbial identification, particularly in clinical laboratories. The numerical or phenetic approach to classification groups strains based on a large number of phenotypic characteristics, typically employing 50-200 biochemical, morphological, and cultural characteristics [88]. This method follows principles established by Edwards and Ewing, emphasizing that classification should be based on an organism's overall morphologic and biochemical pattern rather than any single characteristic, regardless of its importance [88]. The limitations of phenotypic methods include their inability to detect major genetic differences and the potential for variable gene expression to influence results.

Genomic-Based Validation Methods

Whole-genome sequencing has revolutionized taxonomic validation by enabling comprehensive genetic comparison. Multi-Locus Sequence Analysis (MLSA) and Average Nucleotide Identity (ANI) have emerged as gold standards for species delineation [108]. The AutoMLST2 platform exemplifies modern approaches, offering automated phylogenetic reconstruction through two distinct analysis modes: De novo mode (constructing phylogenetic trees entirely from scratch) and Placement mode (integrating query genomes into a precomputed reference tree) [108].

Table 2: Genomic Methods for Taxonomic Validation

Method	Resolution	Applications	Technical Requirements
16S rRNA Gene Sequencing	Genus to species level	Initial identification, phylogenetic placement	Sanger or NGS sequencing, reference databases
Whole-Genome Sequencing	Species to strain level	Definitive identification, outbreak tracking	NGS platforms, bioinformatics infrastructure
Multi-Locus Sequence Typing	Species to strain level	Epidemiology, population genetics	PCR amplification, sequencing, analysis software
Average Nucleotide Identity	Species demarcation	Species definition, novel species identification	Whole-genome data, specialized algorithms
Core Genome Analysis	Strain to subtype level	High-resolution typing, microevolution studies	Advanced bioinformatics, computational resources

The bioBakery 3 platform represents the state-of-the-art in integrated taxonomic profiling, providing a suite of tools including MetaPhlAn 3 for taxonomic profiling, StrainPhlAn 3 and PanPhlAn 3 for strain-level profiling, and PhyloPhlAn 3 for phylogenetic placement [110]. This platform leverages an updated ChocoPhlAn 3 database of systematically organized and annotated microbial genomes, enabling researchers to deepen the resolution, scale, and accuracy of microbial community studies [110].

Proteomic and Mass Spectrometry Approaches

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has emerged as a rapid, cost-effective method for microbial identification in clinical laboratories [111]. This proteomic approach generates spectral fingerprints from microbial proteins, which are compared against reference databases for identification. Implementation requires careful translation of taxonomic nomenclature to ensure clinical utility, as evidenced by the Cleveland Clinic's Bacterial Taxonomy Translation Guide, which provides reporting instructions for MALDI-TOF MS identifications [111].

Taxonomic Validation in Clinical Diagnostics

Implementation Frameworks for Clinical Laboratories

The Clinical and Laboratory Standards Institute (CLSI) provides guidelines for implementing taxonomy nomenclature changes in clinical settings. CLSI M64 recommends a two- to three-year timeline for laboratories to enact taxonomic changes, with more expedient action for changes that profoundly affect patient care [112]. Successful implementation requires communication with clinical and public health stakeholders, including physicians, pharmacists, and infection preventionists [112]. The guideline emphasizes that taxonomic changes for medically important bacteria and fungi must be carefully evaluated for their potential impact on antimicrobial susceptibility testing (AST) standards and patient management decisions [112].

Strain-Level Typing for Infection Control and Outbreak Management

Strain-level profiling has become increasingly important for tracking disease outbreaks and understanding transmission dynamics. Methods such as serotyping, enzyme typing, identification of toxins or other virulence factors, and characterization of plasmids provide sub-species classification essential for public health interventions [88]. Strain-level analysis is particularly critical for pathogens like Escherichia coli and Vibrio cholerae, where differences in pathogenicity necessitate precise characterization [88]. Modern molecular techniques permit species and strain identification by genetic sequences, sometimes directly from clinical specimens, enabling rapid response to emerging threats [88].

Diagram 1: Clinical Taxonomy Validation Workflow. This workflow integrates traditional and molecular methods for comprehensive microbial identification in clinical settings.

Taxonomic Validation in Biotechnology and Drug Discovery

Phylogeny Analysis for Target Identification and Validation

Phylogeny analysis plays a crucial role in drug discovery by helping identify and validate potential drug targets [113]. Evolutionary conservation analysis across species often denotes fundamental biological functions that, when dysregulated, can lead to disease [113]. By constructing phylogenetic trees, researchers can pinpoint evolutionarily conserved regions of molecules and differentiate between homologous proteins, assisting in discerning structural and functional similarities that may be targeted by new drugs [113]. This approach is particularly valuable for studying protein families implicated in disease pathways, such as enzymes, receptors, and ion channels that display sequence and structural conservation across species [113].

Understanding Pathogen Evolution for Antimicrobial Development

Phylogenetic analysis provides critical insights into the evolutionary dynamics of pathogens, enabling more effective antimicrobial development [113]. The phylogenetic mapping of pathogenic strains can identify mutations and gene acquisitions that confer drug resistance, allowing researchers to infer trends in resistance evolution and track the geographical spread of resistant clones [113]. This approach is particularly valuable for vaccine design, where phylogenetic analysis helps determine the most prevalent or emerging viral subtypes and informs antigen selection for broad protection against diverse strains [113]. Understanding antigenic evolution in pathogens like influenza and HIV guides the development of vaccines that can cope with rapid viral evolution [113].

Diagram 2: Phylogenetic Workflow for Drug Discovery. This pipeline illustrates how genomic data is processed for phylogenetic analysis and applied to various drug discovery applications.

Strain Selection for Natural Product Discovery

Taxonomic validation enables more systematic strain selection in natural product discovery through pharmacophylogeny—the study of chemical variations in plants and microbes in relation to their evolutionary history [113]. This approach helps prioritize natural products from closely related species that are more likely to produce similar biologically active compounds [113]. In botanical drug discovery, phylogenetic relatedness suggests similar chemical profiles and therapeutic effects, allowing researchers to expand the pool of potential drug resources by identifying substitute species with similar metabolomic profiles [113].

Table 3: Research Reagent Solutions for Taxonomic Validation

Reagent/Platform	Function	Application Context
bioBakery 3	Integrated suite for taxonomic, strain-level, functional, and phylogenetic profiling	Metagenomic studies, microbial community analysis
AutoMLST2	Automated phylogenetic reconstruction and microbial taxonomy analysis	Bacterial and archaeal genome phylogeny
ChocoPhlAn 3	Database of systematically organized and annotated microbial genomes	Reference database for meta-omic profiling
GTDB (Genome Taxonomy Database)	Standardized microbial taxonomy based on genome sequences	Taxonomic classification and phylogenetic placement
CLSI M64 Guideline	Framework for implementing taxonomy nomenclature changes	Clinical laboratory standardization

Integrated Validation Framework and Best Practices

Multi-Method Approach for Comprehensive Validation

Effective taxonomic validation requires a multi-method approach that integrates complementary techniques to overcome the limitations of any single method. The numerical taxonomy approach emphasizes testing a large and diverse strain sample to accurately determine the biochemical characteristics used to distinguish a given species [88]. This principle extends to genomic methods, where analyzing multiple genetic loci provides more robust phylogenetic resolution than single-gene approaches [108]. Atypical strains should be thoroughly investigated as they may represent typical members of an unrecognized new species rather than outliers within existing taxa [88].

Quality Assurance and Standardization

Standardization of metadata—the description of samples, collection methodologies, and experimental conditions—is essential for reproducible taxonomic validation [22]. Compliance with standards outlined in the Minimum Information for Biological and Biomedical Investigations (MIBBI) project facilitates consistent collection and storage of experimental metadata [22]. Continuous advances in sequencing technologies necessitate ongoing quality assessment, with particular attention to genome annotation accuracy and the implementation of centralized, automated systems for annotation updates [22].

Implementation Timeline and Stakeholder Communication

Successful implementation of taxonomic changes requires careful planning and communication. The CLSI M64 guideline recommends a two- to three-year timeline for laboratories to enact taxonomic changes, with provisions for more expedient implementation of changes that significantly affect patient care [112]. Communication with clinical and public health stakeholders is imperative throughout the implementation process, ensuring that taxonomic updates enhance rather than hinder patient management decisions [112]. This is particularly important for antimicrobial susceptibility testing, where taxonomic changes may alter interpretive criteria and treatment recommendations [112].

Validated microbial taxonomy serves as the foundation for both clinical diagnostics and biotechnological innovation, enabling accurate communication, effective intervention, and systematic discovery. The integration of genomic, phenotypic, and proteomic approaches within structured implementation frameworks ensures that taxonomic determinations are biologically meaningful and clinically actionable. As sequencing technologies continue to advance and computational methods become more sophisticated, taxonomic validation will increasingly rely on multi-omic data integration and automated analysis platforms. By adhering to standardized methodologies and maintaining open communication across scientific and clinical domains, researchers and practitioners can leverage validated taxonomy to improve patient outcomes, advance drug discovery, and unravel the complexity of microbial systems. The future of taxonomic validation lies in the development of increasingly accessible computational tools, enhanced reference databases, and integrated workflows that bridge the gap between fundamental research and practical application.

Conclusion

The field of microbial taxonomy has been fundamentally transformed by genomics, moving from a phenotype-dependent framework to a robust, sequence-based phylogenetic system. The key takeaways are the establishment of precise genomic thresholds for species definition, the ability to classify the vast uncultured microbial diversity, and the resolution of long-standing polyphyletic ambiguities. For biomedical and clinical research, these advances provide a reliable foundation for tracking pathogens, understanding microbiome dynamics in health and disease, and identifying microbes with biotechnological potential. Future directions will involve the continued refinement of the tree of life through large-scale metagenomic studies, the integration of taxonomic databases into clinical and industrial pipelines, and the development of real-time genomic identification systems that will accelerate drug discovery and diagnostic development.

References

• 1. Frontiers | The ever-evolving world of microbes : the current state of... [https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2025.1620652/full]
• 2. | SpringerLink Microbial taxonomy [https://link.springer.com/collections/fiijbbadbe]
• 3. PPT - Introduction: The microbial world, Classification, Taxonomy ... [https://www.slideserve.com/radwan/introduction-the-microbial-world-classification-taxonomy-nomenclature]
• 4. Phylogeny 2025 | PDF | Phylogenetic Tree [https://www.scribd.com/document/869040987/Phylogeny-2025]
• 5. Constructing phylogenetic trees for microbiome data analysis [https://www.sciencedirect.com/science/article/pii/S2001037024003532]
• 6. BISMiS - Home [https://bismis.net/]
• 7. Improving microbial phylogeny with citizen science within a ... [https://www.nature.com/articles/s41587-024-02175-6]
• 8. The first New to Science of 2025 [https://microbiologysociety.org/blog/the-first-new-to-science-in-2025.html]
• 9. The 1st International Online Conference on Taxonomy - Boom Or... [https://sciforum.net/event/IOCTX2025]
• 10. Genome-Based Microbial Taxonomy Coming of Age - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC4888819/]
• 11. Microbial genomic taxonomy | BMC Genomics | Full Text [https://bmcgenomics.biomedcentral.com/articles/10.1186/1471-2164-14-913]
• 12. Augmenting microbial phylogenomic signal with tailored ... [https://www.nature.com/articles/s41467-025-64881-2]
• 13. Genomic Sequencing Reveals Complexity of Microbial World [https://nihrecord.nih.gov/2025/08/29/genomic-sequencing-reveals-complexity-microbial-world]
• 14. A unified catalog of 14062 microbial species ... - Microbiome [https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-025-02232-5]
• 15. - Wikipedia Taxonomic rank [https://en.wikipedia.org/wiki/Taxonomic_rank]
• 16. biological classification - Students | Britannica Kids | Homework Help [https://kids.britannica.com/students/article/biological-classification/611149]
• 17. Bacterial taxonomy [https://en.wikipedia.org/wiki/Bacterial_taxonomy]
• 18. Hierarchy of Classification Groups Explained Simply [https://www.vedantu.com/biology/hierarchy-of-classification-groups]
• 19. Phylogenetics and the Human Microbiome - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC4265140/]
• 20. The Prokaryotic Species Concept and Challenges - NCBI - NIH [https://www.ncbi.nlm.nih.gov/books/NBK558821/]
• 21. High throughput ANI analysis of 90K prokaryotic genomes ... [https://www.nature.com/articles/s41467-018-07641-9]
• 22. An exciting future for microbial molecular biology and physiology [https://pmc.ncbi.nlm.nih.gov/articles/PMC12345256/]
• 23. Three-domain system [https://en.wikipedia.org/wiki/Three-domain_system]
• 24. 1.3: Classification - The Three Domain System [https://bio.libretexts.org/Bookshelves/Microbiology/Microbiology_(Kaiser)/Unit_1%3A_Introduction_to_Microbiology_and_Prokaryotic_Cell_Anatomy/1%3A_Fundamentals_of_Microbiology/1.3%3A_Classification_-_The_Three_Domain_System]
• 25. , Microbial and Biodiversity Taxonomy Phylogeny [https://www.frontiersin.org/research-topics/5493/microbial-taxonomy-phylogeny-and-biodiversity/magazine]
• 26. A standardized bacterial taxonomy based on genome phylogeny ... [https://pubmed.ncbi.nlm.nih.gov/30148503/]
• 27. Benchmarking of Methods for Genomic Taxonomy - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC3993634/]
• 28. Gene content dissimilarity for subclassification of highly similar... [https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-016-2991-9]
• 29. characterisation and Genome of Schaalia... comparative analysis [https://bmcmicrobiol.biomedcentral.com/articles/10.1186/s12866-024-03346-w]
• 30. Genome composition and GC content influence loci ... [https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-024-10312-3]
• 31. Reducing GC & PCR Bias in Whole-Genome Sequencing [https://www.revvity.com/blog/understanding-impact-gc-and-pcr-biases-whole-genome-sequencing]
• 32. of the Genus Nesterenkonia... Comparative Genomic Analyses [https://pmc.ncbi.nlm.nih.gov/articles/PMC8875376/]
• 33. Kostas lab | Tools [http://enve-omics.ce.gatech.edu/]
• 34. Summarizing and correcting the GC content bias in high- ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC3378858/]
• 35. Multilocus sequence analysis (MLSA) in prokaryotic ... [https://www.sciencedirect.com/science/article/abs/pii/S0723202015000508]
• 36. Performance and Application of 16 Gene Cycle Sequencing for... S rRNA [https://pmc.ncbi.nlm.nih.gov/articles/PMC7484979/]
• 37. Sequencing | News-Medical 16 S rRNA [https://www.news-medical.net/life-sciences/16S-rRNA-Sequencing.aspx]
• 38. Next Generation Sequencing Improves Diagnostic 16S ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12418051/]
• 39. Multilocus Sequence Analysis, a Rapid and Accurate Tool ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC6532039/]
• 40. Genome-resolved long-read sequencing expands known ... [https://www.nature.com/articles/s41564-025-02062-z]
• 41. Assessment of MultiLocus Sequence Analysis As a ... [https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2017.01107/full]
• 42. The tree of genomes: An empirical ... - PubMed Central [https://pmc.ncbi.nlm.nih.gov/articles/PMC2592249/]
• 43. Phylogeny of Bacterial and Archaeal Genomes Using ... [https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0062510]
• 44. The Tree and Net Components of Prokaryote Evolution [https://pmc.ncbi.nlm.nih.gov/articles/PMC2997564/]
• 45. De novo genome assembly, inversion detection, and ... [https://www.nature.com/articles/s41598-025-24574-8]
• 46. Accurate, scalable, and fully automated inference of species ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12088440/]
• 47. the current state of microbial taxonomy, genome ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12230012/]
• 48. GGDC Genome-to-Genome Distance Calculator 3.0 [https://ggdc.dsmz.de/ggdc.php]
• 49. A genome sequence-based approach to taxonomy of the ... [https://pubmed.ncbi.nlm.nih.gov/22836677/]
• 50. Context-Aware Phylogenetic Trees for Phylogeny-Based ... [https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2022.891240/full]
• 51. dna2bit: high performance genomic distance estimation ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11701053/]
• 52. Mash: fast genome and metagenome distance estimation ... [https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0997-x]
• 53. Opportunities and challenges of using metagenomic data to bring uncultured microbes into cultivation | Microbiome | Full Text [https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-022-01272-5]
• 54. Comparison of Metagenomics and Metatranscriptomics Tools [https://pmc.ncbi.nlm.nih.gov/articles/PMC9777648/]
• 55. Sizing Up the Uncultured Microbial Majority - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC6156272/]
• 56. Editorial: Microbial Taxonomy, Phylogeny and Biodiversity [https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2019.01324/full]
• 57. Metagenomic and metatranscriptomic insights into sulfate-reducing bacteria in a revegetated acidic mine wasteland | npj Biofilms and Microbiomes [https://www.nature.com/articles/s41522-022-00333-9]
• 58. Recent Trends in Metagenomic Approaches in Environmental Cleanup | SpringerLink [https://link.springer.com/chapter/10.1007/978-981-19-4320-1_25]
• 59. Visualizing metagenomic and metatranscriptomic data [https://www.sciencedirect.com/science/article/pii/S2001037024001430]
• 60. Detecting and correcting misclassified sequences in the ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC7821992/]
• 61. Misidentification of genome assemblies in public databases [https://pmc.ncbi.nlm.nih.gov/articles/PMC6001429/]
• 62. Common Methods for Phylogenetic Tree Construction and ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11117635/]
• 63. Correcting Inconsistencies and Errors in Bacterial Genome ... [https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2015.00138/full]
• 64. CAGI, the Critical Assessment of Genome Interpretation ... [https://genomebiology.biomedcentral.com/articles/10.1186/s13059-023-03113-6]
• 65. Tools for Analysis of the Microbiome - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC7598837/]
• 66. Misclassified: identification of zoonotic transition biomarker ... [https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2023.1145166/full]
• 67. GET_PHYLOMARKERS, a Software Package to Select ... [https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2018.00771/full]
• 68. GET_PHYLOMARKERS, a software package to select optimal ... [https://araid.es/en/content/getphylomarkers-software-package-select-optimal-orthologous-clusters-phylogenomics-and]
• 69. Horizontal gene transfer in evolution: facts and challenges [https://pmc.ncbi.nlm.nih.gov/articles/PMC2842723/]
• 70. Inferring Horizontal Gene Transfer - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC4462595/]
• 71. Bacterial chromosomal mobility via lateral transduction ... [https://www.nature.com/articles/s41467-021-26004-5]
• 72. Experimental setup of phage transduction [https://bio-protocol.org/exchange/minidetail?id=8443005&type=30]
• 73. Chromosomal rearrangements as a source of new gene ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC6776367/]
• 74. Change of Plans: Overview of Bacterial Taxonomy, Recent ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10318492/]
• 75. The taxonomy of the genus Clostridium [https://wswxtb.ijournals.cn/html/wswxtbcn/2016/5/tb16051070.htm]
• 76. The effect of taxonomic classification by full-length 16S ... [https://www.nature.com/articles/s41598-020-80826-9]
• 77. Understanding Pharmaceutical Quality by Design - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC4070262/]
• 78. Pharmaceutical product development: A quality by design approach - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC4991121/]
• 79. Quantitative microbial taxonomy across particle size, depth ... [https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1552305/full]
• 80. Quantitative microbial population study reveals geographical ... [https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-022-01276-1]
• 81. Comparing Published Gut Microbiome Taxonomic Data ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC8740627/]
• 82. 无菌药品污染控制策略要点探析 [https://www.yydbzz.com/article/2023/1004-0781/1004-0781-42-9-1424.shtml]
• 83. 生物制药中的污染控制策略（CCS）简析 [http://www.fosterbio.com/news/show/80.html]
• 84. DNA-DNA Hybridization - an overview [https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/dna-dna-hybridization]
• 85. DNA-DNA hybridization values and their relationship to ... [https://pubmed.ncbi.nlm.nih.gov/17220447/]
• 86. Relationship between genome similarity and DNA ... [https://pubmed.ncbi.nlm.nih.gov/18050912/]
• 87. High-throughput DNA melt measurements enable ... [https://www.nature.com/articles/s41467-025-60455-4]
• 88. Classification - Medical Microbiology - NCBI Bookshelf - NIH [https://www.ncbi.nlm.nih.gov/books/NBK8406/]
• 89. Classification of Bacteria: Methods and Criteria Explained [https://microbenotes.com/classification-of-bacteria/]
• 90. Traditional Microbial Identification Methods vs. Genetic ... [https://microchemlab.com/information/traditional-microbial-identification-methods-vs-genetic-sequencing/]
• 91. About - GTDB - Australian Centre for Ecogenomics [https://gtdb.ecogenomic.org/about]
• 92. FAQ - GTDB [https://gtdb.ecogenomic.org/faq]
• 93. The research on the identification, taxonomy, and ... [https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1544934/full]
• 94. Identification of robust species-specific marker genes for the ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12623006/]
• 95. GTDB - Genome Taxonomy Database [https://gtdb.ecogenomic.org/]
• 96. Methods for Microbial Detection and Identification [https://www.cd-genomics.com/microbioseq/methods-for-microbial-detection-and-identification.html]
• 97. Bioinformatics - 2025 UBC iGEM Wiki [https://2025.igem.wiki/ubc-vancouver/biocementing-bacteria/bioinformatics]
• 98. Frontiers | Genomic Metrics Applied to Rhizobiales (Hyphomicrobiales): Species Reclassification, Identification of Unauthentic Genomes and False Type Strains [https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2021.614957/full]
• 99. Shifting the genomic gold standard for the prokaryotic ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC2776425/]
• 100. Genome-guided investigation of secondary metabolites ... [https://pubmed.ncbi.nlm.nih.gov/33666690/]
• 101. Evaluating the Genome-Based Average Nucleotide Identity ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11433037/]
• 102. Average Nucleotide Identity and Digital DNA ... [https://www.mdpi.com/2075-4418/14/16/1800]
• 103. VBCG: 20 validated bacterial core genes for phylogenomic ... [https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-023-01705-9]
• 104. New Insights into the Taxonomy of Bacteria in the Genomic ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9148247/]
• 105. Prokaryotic taxonomy and nomenclature in the age of big ... [https://www.nature.com/articles/s41396-021-00941-x]
• 106. Fast and robust estimate of bacterial genus novelty using ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12622232/]
• 107. Constructing phylogenetic trees for microbiome data analysis [https://pmc.ncbi.nlm.nih.gov/articles/PMC11564040/]
• 108. AutoMLST2: a web server for phylogeny and microbial ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12230714/]
• 109. Taxonomy 101: Definition, Best Practices, and How It ... [https://www.nngroup.com/articles/taxonomy-101/]
• 110. Integrating taxonomic, functional, and strain-level profiling ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC8096432/]
• 111. Bacterial Taxonomy Translation Guide [https://clevelandcliniclabs.com/our-laboratories/laboratory-medicine/microbiology/bacterial-taxonomy-translation-guide/]
• 112. M64 | Implementation of Taxonomy Nomenclature Changes [https://clsi.org/shop/standards/m64/]
• 113. What are the applications of phylogeny analysis in drug ... [https://synapse.patsnap.com/article/what-are-the-applications-of-phylogeny-analysis-in-drug-discovery]