# Advancements and challenges in bioinformatics tools for microbial genomics in the last decade: Toward the smart integration of bioinformatics tools, digital resources, and emerging technologies for the analysis of complex biological data

Cheikh Tidiane Houmenou, Cheikh Sokhna, Florence Fenollar, Oleg Mediannikov

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Cheikh Tidiane Houmenou, Cheikh Sokhna, Florence Fenollar, Oleg Mediannikov. Advancements and challenges in bioinformatics tools for microbial genomics in the last decade: Toward the smart integration of bioinformatics tools, digital resources, and emerging technologies for the analysis of complex biological data. Infection, Genetics and Evolution, 2025, 136, pp.105859. 10.1016/j.meegid.2025.105859. hal-05403938

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Submitted on 8 Dec 2025

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Review

# Advancements and challenges in bioinformatics tools for microbial genomics in the last decade: Toward the smart integration of bioinformatics tools, digital resources, and emerging technologies for the analysis of complex biological data

Cheikh Tidiane Houmenou a,b, Cheikh Sokhna c,d, Florence Fenollar a,c, Oleg Mediannikov a,b,d,*

ARTICLE INFO

Keywords:

Microbial genomics

Bioinformatics tools

Functional annotation

Metagenomics

Next-generation sequencing (NGS)

Antimicrobial resistance (AMR)

One health

A B S T R A C T

Over the past decade, microbial genomics has been transformed by advances in sequencing technologies and bioinformatics, enabling the transition from targeted gene markers to complete genome assemblies and ecological scale metagenomic surveys. This review presents a comprehensive overview of the bioinformatics pipelines that structure this field, from sample preparation, PCR amplification, and next-generation sequencing (NGS) to read preprocessing, genome assembly, polishing, structural and functional annotation, and submission to public databases. We highlight the major tools that have become standards at each stage, including FastQC, SPAdes, Prokka, Bakta, CARD, GTDB-Tk, QIIME 2, and Kraken2, while also emphasizing recent innovations such as hybrid assemblers, ontology-driven annotation frameworks, and automated workflows (nf-core, Bactopia). Applications extend across microbiology, from antimicrobial resistance surveillance and phylogenetic classification to ecological studies, exemplified here by three case studies: termite gut microbiota profiling by 16S metabarcoding, the description of new *Bartonella* species from bats, and the genomic characterization of rare *Salmonella enterica* serovars from primates. Despite these advances, persistent challenges remain, including incomplete and biased reference databases, computational bottlenecks, and economic disparities in sequencing and storage capacities. In response, international initiatives increasingly promote open, interoperable, and reusable bioinformatics infrastructures. Conforming to the Findable, Accessible, Interoperable, Reusable (FAIR) principles and global frameworks such as Global Alliance for Genomics and Health (GA4GH), these efforts are driving greater standardization, transparency, and data sharing across the microbial genomics community. Future perspectives point toward the integration of artificial intelligence, long-read and telomere-to-telomere (T2T) sequencing, cloud-native infrastructures, and even quantum computing, paving the way for a predictive, reproducible, and globally inclusive microbial genomics.

# 1. Introduction

From general biology to modern microbiology, the integration of bioinformatics has opened a new era in the exploration of living systems. Classical molecular biology through polymerase chain reaction (PCR) and marker gene sequencing such as 16S rRNA for bacteria and mitochondrial cytochrome oxydase I for Metazoa, enabled the first descriptions of microbial diversity and DNA barcoding, Next-generation

sequencing (NGS) and bioinformatics tools have profoundly transformed the field of microbial genomics. These innovations now provide access to entire microbial genomes, their organization and functions, and allow linking such information to major issues in ecology, medicine, and public health (Mardis, 2017; Gilbert et al., 2018; Wang et al., 2024). The rise of NGS represented a major turning point, allowing simultaneous analysis of complete genomes and complex microbial communities, whether clinical or environmental. Short-read platforms

<footnote>ª Institut Hospitalo-universitaire Méditerranée Infection, Marseille, France</footnote>

<footnote>ᵇ Aix Marseille University, AP-HM, MEPHI, Marseille, France</footnote>

<footnote>ᶜ Aix Marseille University, AP-HM, SSA, RITMES, Marseille, France</footnote>

<footnote>ᵈ IRD, Marseille, France</footnote>

<footnote>* Corresponding author at: Institut Hospitalo-universitaire Méditerranée Infection, Marseille, France.
E-mail address: olegusss1@gmail.com (O. Mediannikov).</footnote>
(Illumina) offered high sequencing accuracy, while long-read technologies (Oxford Nanopore, PacBio HiFi) enabled more continuous assemblies capable of resolving plasmids, mobile elements, and repetitive regions (Rhie et al., 2021; Lee et al., 2016; Lang et al., 2020). These experimental advances have structured today's bioinformatics pipeline: (i) quality control and read preprocessing (FastQC, Trimmomatic, Cutadapt), (ii) genome assembly and polishing (SPAdes, Unicycler, Flye, BUSCO, CheckM), (iii) structural annotation (Prodigal, GeneMarkS-2) and functional annotation (Prokka, Bakta, eggNOG-mapper, KEGG), and (iv) targeted detection of resistance (CARD, ResFinder, AMRFinderPlus), virulence (VirulenceFinder), and plasmids (PlasmidFinder). The final step is the deposition of annotated genomes and metadata into international public repositories (NCBI GenBank/RefSeq, ENA-EBI, DDBJ), ensuring data standardization, reproducibility, and global accessibility (Tatusova et al., 2016; Wilkinson et al., 2016a). In parallel, real-time genomic epidemiology has demonstrated its potential for clinical and environmental surveillance. Building on these technological and methodological advances, the field of microbial genomics has progressively shifted from isolated genome assemblies toward integrated, large-scale frameworks for surveillance, comparative analyses, and functional interpretation. Platforms such as Nextstrain and GISAID have proven their effectiveness during the COVID-19 pandemic, while Pathogenwatch supports large-scale bacterial surveillance (Hadfield et al., 2018; Shu and J., 2017; Argimón et al., 2021). The increasing complexity of data has also driven the development of automated, reproducible workflows such as Bactopia (Petit and Read, 2020a) and nf-core pipelines (Ewels et al., 2020), based on workflow managers like Nextflow and Snakemake and reinforced by containerization technologies (Docker, Singularity), improving traceability and reproducibility of analyses. At the same time, the rapid expansion of sequencing data has necessitated powerful infrastructures ranging from High-Performance Computing (HPC) clusters to cloud-based services (Amazon Web Services (AWS), Google Cloud, Azure) and exascale architectures that enable massive-scale genomic analyses (Langmead and Nellore, 2018a; Perkel, 2021a; Perkel, 2021b). Looking ahead, the integration of artificial intelligence (DeepVariant, AlphaMissense), cloud-native bioinformatics, and emerging quantum computing applications for genome alignment and structural modeling promise a microbial bioinformatics ecosystem that is predictive, reproducible, and globally accessible (Cheng et al., 2023a; Mokhtari et al., 2024; Pallavi and Prasanna Kumar, 2025). In this review, we aim to provide a comprehensive overview of the main bioinformatics tools developed over the last decade for microbial genomics, highlighting their applications in genome assembly, functional annotation, antimicrobial resistance detection, and epidemiological surveillance. We also discuss persistent challenges such as incomplete reference databases, reproducibility issues, and computational bottlenecks and outline future perspectives shaped by artificial intelligence, long-read sequencing, and next-generation computational infrastructures. While several recent reviews have focused on specific aspects of microbial bioinformatics such as workflow management systems (Langer et al., 2025) or long-read metagenomics (Kim et al., 2024), a comprehensive and integrative synthesis is still lacking. This review provides a longitudinal perspective (2015–2025) that bridges technological, methodological, and conceptual advances across the entire analytical pipeline from sample preparation and genome assembly to functional annotation and data sharing. We also integrate bibliometric trends, One Health case studies, and a discussion of emerging challenges related to computational skills, economic disparities, and reproducibility under the Findable, Accessible, Interoperable, Reusable (FAIR)/ Global Alliance for Genomics and Health (GA4GH) framework.

## 2. From biological sample to raw data

### 2.1. Preparation of microbial samples

Microbial genomic analysis begins with the careful preparation of

biological material, which may originate from pure cultures, host-associated systems, or environmental matrices such as soil, water, or animal microbiomes. For many microorganisms, particularly bacteria such as *Enterobacteriaceae*, axenic culture remains the gold standard to ensure clonality and to avoid strain mixtures that could compromise genomic interpretation (Blainey, 2013). Establishing pure isolates allows high-quality genomic DNA to be obtained, essential for robust assembly and functional annotation. For obligate intracellular bacteria, such as *Rickettsia* species, cell culture systems are required to propagate the microorganisms within host cells. In such cases, minimizing host DNA contamination is critical, often achieved through host cell depletion, density-gradient centrifugation, or selective lysis methods (Weiss et al., 1975; Tamura et al., 1982; Islam Sajib et al., 2024). Similarly, certain pathogens that cannot be cultured in vitro, such as *Treponema pallidum*, must be maintained in animal models, where subsequent purification and enrichment steps are necessary to separate bacterial cells from host material prior to sequencing (Miao and Fieldsteel, 1980; Noguchi, 1911; Stamm, 2010). When microbial DNA is extracted directly from biological samples (e.g., blood, feces, biopsies, or environmental matrices), the proportion of prokaryotic DNA can be extremely low compared to eukaryotic DNA. Therefore, enrichment strategies are often employed to enhance microbial genome recovery including differential centrifugation, enzymatic digestion of host DNA, methylation-dependent depletion of eukaryotic genomes, or selective amplification of microbial targets (Islam Sajib et al., 2024; Cheng et al., 2023b; Shi et al., 2022). These methods are essential to reduce host interference and increase the sensitivity and coverage of microbial sequencing, particularly in metagenomic and clinical studies. Overall, the success of microbial genomic analyses depends heavily on the integrity, purity, and representativeness of the nucleic acids extracted. Each sample type requires tailored preparation strategies to balance yield, purity, and genome completeness, forming the foundation upon which reliable sequencing and downstream bioinformatics analyses can be built (Abdi et al., 2024; Joshi et al., 2024).

DNA or RNA extraction then represents a critical step, as nucleic acid quality and quantity directly determines sequencing success and downstream bioinformatic analyses (Gilbert et al., 2018). Importantly, these extractions often require pre-treatment steps such as mechanical, enzymatic, or chemical lysis, removal of inhibitors, or matrix-specific treatments which must be tailored to the nature of the sample to ensure efficient nucleic acid recovery and reliable downstream applications (Prins, 2024; Salonen et al., 2010). Given its central role in determining the reliability of sequencing outputs, this stage demands rigorous methodological attention and careful optimization, as inadequate extraction can compromise the entire analytical pipeline from library preparation to bioinformatic interpretation (Miao and Fieldsteel, 1980; Costea et al., 2017; Fouhy et al., 2016).

### 2.2. Amplification and PCR

Before sequencing, targeted amplification steps may be performed. The PCR is widely used to target genetic markers, particularly the 16S rRNA gene for bacteria (Fadrosh et al., 2014), which remains a cornerstone for bacterial taxonomy, as well as genes associated with antimicrobial resistance. This approach, known as metabarcoding, provides a rapid and standardized view of microbial diversity. In parallel, shotgun sequencing is increasingly favored; by randomly sequencing genomic fragments without targeted amplification, it enables reconstruction of complete microbial genomes and characterization of complex communities (Mardis, 2017).

### 2.3. Sequencing technologies

Sequencing technologies have evolved over three major generations. First-generation sequencing, based on the Sanger method, laid the foundations of microbial genomics by validating targeted genes, but its
low throughput (hundreds of kilobases per day) limited its applicability to large-scale projects. The emergence of second-generation technologies (Illumina, Roche 454, Ion Torrent, Beijing Genomics Institute (BGI)/MGI Tech Company (MGI)) introduced massively parallel sequencing, drastically reducing costs from nearly 10,000 USD per genome in 2005 to less than 1000 USD by the early 2010s, and today to below 200 USD for complete microbial genomes (Sollis et al., 2023; Shean et al., 2025a) (Fig. 1.A). These platforms democratized access to microbial genomics, although their short reads (100–300 bp) reduce the resolution of repeats and plasmids, often leading to fragmented assemblies in complex genomes (Goodwin et al., 2016; Sanders et al., 2022) (Table 1). Short-read sequencing, exemplified by Illumina, provides billions of bases per run with >99.9 % accuracy, making it the gold standard for base-level precision. Roche 454 and Ion Torrent contributed to early innovations in massively parallel sequencing, while BGI/MGI expanded global accessibility. Despite their accuracy and throughput, short-read platforms remain limited in resolving structural variants and repetitive regions, highlighting the need for complementary technologies, especially in bacterial genomics where they remain central for resistance detection (Heather and Chain, 2016). Third-generation platforms (Pacific Biosciences HiFi, Oxford Nanopore Technologies (ONT)) marked a paradigm shift by producing long (>25 kb) and even ultra-long (>100 kb) reads, capable of resolving plasmids, mobile genetic elements, and repetitive regions, while maintaining high accuracy (>99.9 %) (Rhie et al., 2021; Lee et al., 2016; Logsdon et al., 2020; Wenger et al., 2019; Arslan et al., 2024). ONT's MinION, released in 2014–2015, enabled real-time and field-deployable sequencing with

applications in epidemiology and environmental genomics, while PacBio's Sequel II system, launched in 2019, introduced HiFi reads with >99 % accuracy, dramatically improving assembly quality and reliability for complex genomes (Lee et al., 2016; Wenger et al., 2019). Together, ONT and PacBio have made it possible to resolve plasmids, repeats, and structural variants, producing assemblies that approach telomere-to-telomere continuity (Logsdon et al., 2020). More recently, hybrid innovations have emerged: 10× Genomics and Loop Genomics generate synthetic long reads from short fragments, while Hi-C sequencing captures chromosomal interactions for chromosome-level scaffolding, particularly useful in metagenomics (Chen et al., 2014; Zheng et al., 2016; Smith et al., 2017). In the last few years (2021–2025), new sequencing platforms have further reduced costs and improved scalability: Ultima Genomics (Sanders et al., 2022; Polonis et al., 2025; Shean et al., 2025b), Element Biosciences AVITI (Arslan et al., 2024), and Complete Genomics DNBSEQ-T20x2 (Rao et al., 2025) now enable sequencing for less than 100 USD (NEWARK, California, May 31st, 2022, https://www.ultimagenomics.com/blog/ultima-genomics-delivers-the-usd100-genome/, last access October 31st, 2025) per microbial genome, paving the way for global democratization of genomics (Fig. 1B, Table 1).

### 3. Quality control and preprocessing of reads

Once sequencing data are generated, rigorous quality control and preprocessing are indispensable to ensure reliable downstream analyses, since raw reads often contain sequencing errors, low-quality bases, and

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Sequencing technologies and cost evolution. (A) Timeline of sequencing platforms across four generations, from Sanger to emerging technologies. (B) Reduction of sequencing costs per genome (2001–2025), from ~100 million USD (Sanger) to <100 USD with platforms such as Ultima Genomics, Element, and DNBSEQ, illustrating the democratization of microbial genomics.</figcaption></figure>
<caption>**Table 1**</caption>

Comparative table of sequencing technologies across four generations. This table summarizes the evolution of DNA sequencing technologies from first-generation Sanger sequencing to fourth-generation emerging platforms. For each technology, we describe the sequencing chemistry (how each platform detects and records nucleotide incorporation), typical read lengths achieved (read characteristics including read type, length, and accuracy), main fields of application in microbiology (typical research or clinical uses such as microbial genome assembly, metagenomics, or transcriptomics), and concrete examples of their use in genomic research and clinical practice. Read lengths are expressed in base pairs (bp). NGS: Next-Generation Sequencing; GWAS: Genome-Wide Association Studies; bp: base pairs (for Sanger, second-generation (Illumina, Roche 454, Ion Torrent, BGI/MGI), third generation (PacBio HiFi, Oxford Nanopore), and emerging platforms (10× Genomics, Hi-C, Loop Genomics, Ultima, Element, DNBSEQ), illustrating their impact on microbial genomics and bioinformatics).

<table><thead><tr><th>Sequencing generation</th><th>Platform and Sequencing Method</th><th>Sequencing Method (principe)</th><th>Read Type &amp; Length</th><th>Primary Applications</th><th>Representative Use Cases (example)</th></tr></thead><tbody><tr><td>First generation</td><td>Sanger (capillary sequencing)</td><td>Chain-termination sequencing (capillaries)</td><td>Long reads (800–1000 bp)</td><td>Validation, cloning, targeted genes</td><td>Reference sequencing for single genes; validation of NGS results</td></tr><tr><td>Second generation (NGS, short-reads)</td><td>Illumina (HiSeq, MiSeq, NextSeq, NovaSeq)</td><td>Sequencing by synthesis with reversible fluorescent terminators</td><td>Short reads (50–300 bp)</td><td>Metagenomics, microbial genomes, RNA-seq, GWAS</td><td>Large-scale microbial genome projects; outbreak surveillance</td></tr><tr><td rowspan="2">Third generation (long-reads)</td><td>Roche 454 (pyrosequencing)</td><td>Pyrosequencing (light detection of nucleotide incorporation)</td><td>Medium reads (~700 bp)</td><td>Pioneering NGS, early metagenomics (discontinued)</td><td>Historical use in early microbiome studies</td></tr><tr><td>Ion Torrent (PGM, Proton)</td><td>Detection of protons released during nucleotide incorporation</td><td>Short reads (100–400 bp)</td><td>Fast diagnostics, small genomes, targeted panels</td><td>Hospital diagnostics; rapid pathogen detection</td></tr><tr><td rowspan="3">Fourth generation / Emerging plateforms</td><td>BGI/MGI (BGISEQ, MGISEQ, DNBSEQ)</td><td>DNA nanoball (DNB) technology with PCR-free amplification</td><td>Short reads (100–200 bp)</td><td>Metagenomics, microbial genomes, transcriptomics</td><td>Population-scale microbiome surveys; clinical applications</td></tr><tr><td>PacBio SMRT (Sequel, Sequel II/Ile)</td><td>Single Molecule Real-Time (SMRT) sequencing of DNA molecules</td><td>Long reads (10–25 kb, up to >100 kb)</td><td>De novo genome assembly, isoform resolution, complex genomes</td><td>Resolving plasmids, repetitive regions; antibiotic resistance genes</td></tr><tr><td>Oxford Nanopore (MinION, GridION, PromethION)</td><td>DNA strand passes through a nanopore with current measurement</td><td>Ultra-long reads (up to >2 Mb)</td><td>Complete assemblies, highly complex genomes, real-time metagenomics</td><td>Portable sequencing for field epidemiology; pandemic monitoring</td></tr><tr><td rowspan="3">Fourth generation / Emerging plateforms</td><td>10× Genomics (linked-reads)</td><td>Short reads linked with barcodes to reconstruct pseudo-long reads</td><td>Pseudo-long reads (~10–100 kb)</td><td>Structural variant resolution, haplotyping</td><td>Cancer genomics; phasing of microbial genomes</td></tr><tr><td>Hi-C sequencing</td><td>3D chromatin conformation capture combined with sequencing</td><td>Associated with short reads</td><td>Improving assemblies, chromosome organization</td><td>3D genome mapping; host-pathogen interaction studies</td></tr><tr><td>Loop Genomics (synthetic long reads)</td><td>Synthetic reconstruction of long reads using informatics</td><td>Pseudo-long reads</td><td>Single-cell studies, haplotyping, long RNA sequencing</td><td>Microbial strain-level resolution; rare variant detection</td></tr><tr><td></td><td>Ultima Genomics</td><td>Novel chemistry (similar to Illumina) aiming at ultra-low cost sequencing</td><td>Short reads (Illumina-like)</td><td>High-throughput sequencing at very low cost (in development)</td><td>Potential democratization of sequencing due to cost reduction</td></tr></tbody></table>

adapters. Tools such as FastQC and Trimmomatic are routinely used to assess and trim reads, while Cutadapt efficiently removes adapter contamination (Martin, 2011; Bolger et al., 2014; Simon Andrews et al., 2010). To further improve data quality, normalization steps help balance uneven sequencing depth and prepare datasets for genome assembly. Another critical step is contamination detection, particularly in metagenomics, where exogenous sequences may arise from reagents (the so-called *kitome*), PCR carryover, or cross-sample contamination during sequencing. Dedicated classifiers such as Kraken2 and Centrifuge enable the rapid taxonomic identification of contaminants across millions of reads (Wood et al., 2019; Kim et al., 2016), providing insights into the taxonomic origin of unwanted sequences. However, while these approaches reveal whether contaminants are present, they cannot determine the precise stage of introduction (DNA extraction, PCR, or sequencing), a limitation that underlines the importance of experimental negative controls for accurate interpretation (Salter et al., 2014; Schloss et al., 2011; Costello et al., 2018). In this way, preprocessing steps including trimming, normalization, and contamination screening serve as a crucial bridge between sequencing and bioinformatic analysis, ensuring that only high-quality, biologically relevant data are retained for genome assembly and annotation.

## 4. Genome assembly of microbial genomes

### 4.1. Assembly strategies and technological evolution: applications and microbiological impact

Following preprocessing, genome assembly represents a pivotal step in microbial bioinformatics, transforming fragmented reads into continuous genomic sequences that can be biologically interpreted.

Early short-read assemblers such as Velvet, SOAPdenovo, and especially SPAdes pioneered the use of de Bruijn graphs, and SPAdes has since become the gold standard with over 21,500 citations as of October 2025 (Table 2). However, assemblies from Illumina data often remain fragmented due to repeats and plasmids (Schloss et al., 2011; Costello et al., 2018; Zimin et al., 2013). To overcome these limitations, long-read assemblers including Canu, Flye, Miniasm, wtdbg2/Redbean, and Shasta were developed to exploit PacBio and ONT reads, enabling assemblies that approach complete bacterial chromosomes, albeit with initially higher error rates (Bankevich et al., 2012; Wick et al., 2017; Koren et al., 2017; Kolmogorov et al., 2019). Hybrid assemblers such as Unicycler and MaSuRCA (Zimin et al., 2013) integrate Illumina and long-read data to combine base-level accuracy with structural continuity, making them particularly valuable in clinical microbiology and outbreak investigations (Li, 2016; Ruan and Li, 2020) (Fig. 2). These assemblies are further refined by polishing tools such as Pilon, Racon, and Medaka, which improve consensus accuracy, while standardized evaluation frameworks like BUSCO and CheckM provide measures of completeness and contamination that enable cross-study benchmarking (Shafin et al., 2020; tRNAscan-SE: Searching for tRNA Genes in Genomic Sequences, 2025; Lomsadze et al., 2018; BMC Bioinformat. [Internet], 2025). The microbiological impact of these advances is substantial, as high-quality assemblies allow not only the closure of plasmids carrying antimicrobial resistance genes but also the reconstruction of metagenome-assembled genomes (MAGs), thereby uncovering novel microbial lineages and ecological functions. Over the past decade, the development of specialized genome assemblers has paralleled the evolution of sequencing technologies (Table 2). Table 2 summarizes the main assemblers applied in microbiology, highlighting their year of release, sequencing read compatibility, and primary applications. Together,
<caption>**Table 2**</caption>

Comparative overview of genome assemblers used in microbiology. The table presents major assemblers categorized by read type (short, long, hybrid, or reference-assisted), outlining their main applications from bacterial genomes to complex metagenomes, reflecting the evolution of bioinformatics strategies in microbial genomics.

<table><thead><tr><th>Assembler</th><th>Type of reads</th><th>Main applications</th></tr></thead><tbody><tr><td>SPAdes</td><td>Short reads (Illumina, BGI, Ion Torrent)</td><td>Bacterial genomes, single-cell, metagenomics</td></tr><tr><td>Velvet</td><td>Short reads</td><td>Early short-read genome assembly</td></tr><tr><td>SOAPdenovo</td><td>Short reads</td><td>Large eukaryotic genomes (Illumina)</td></tr><tr><td>ABySS</td><td>Short reads</td><td>Distributed computing, large genomes</td></tr><tr><td>MEGAHIT</td><td>Short reads (metagenomics)</td><td>Metagenomes with high complexity</td></tr><tr><td>IDBA-UD</td><td>Short reads (metagenomics)</td><td>Uneven coverage metagenomes</td></tr><tr><td>Canu</td><td>Long reads (PacBio, Nanopore)</td><td>De novo assembly of complex genomes</td></tr><tr><td>Flye</td><td>Long reads (PacBio, Nanopore)</td><td>Fast and accurate genome/ metagenome assembly</td></tr><tr><td>Miniasm</td><td>Long reads (PacBio, Nanopore)</td><td>Ultra-fast, requires polishing</td></tr><tr><td>wtdbg2 (Redbean)</td><td>Long reads (Nanopore, PacBio)</td><td>Fast assembly of noisy long reads</td></tr><tr><td>Shasta</td><td>Long reads (Nanopore)</td><td>Optimized for nanopore reads Combining Illumina + long reads</td></tr><tr><td>MaSuRCA</td><td>Hybrid (short + long)</td><td>Bacterial genome assembly with hybrid data</td></tr><tr><td>Unicycler</td><td>Hybrid (short + long, bacteria)</td><td>Extension of SPAdes for hybrid data</td></tr><tr><td>HybridSPAdes</td><td>Hybrid (short + long)</td><td>Metagenomic assemblies</td></tr><tr><td>MetaSPAdes</td><td>Short reads (metagenomics)</td><td>Reference-assisted (short/ long)</td></tr><tr><td>AMOS</td><td>Reference-guided assembly</td><td>Reference-guided assembly</td></tr></tbody></table>

these tools illustrate how bioinformatics innovation has kept pace with technological advances, from short-read Illumina assemblies to hybrid and long-read approaches enabling complete microbial genomes.

## 4.2. Bibliometric analysis of genome assemblers

Bibliometric data on the usage and citation frequency of genome assemblers were retrieved via Google Scholar and Semantic Scholar in October 2025. For each assembler, citation counts were obtained using the official tool name as the primary query (e.g., “SPAdes genome assembler,” “Unicycler hybrid assembly pipeline”), including only peer-reviewed research articles. Review papers, conference abstracts, and duplicate entries were excluded to ensure data accuracy. Citation numbers were validated across multiple academic databases (Google Scholar, Semantic Scholar, Web of Science, and Scopus) to minimize bias and reflect relative usage trends in microbial genomics. Bibliometric analyses underscore the sustained dominance of SPAdes (>21,500 citations; (Bankevich et al., 2012)) but also reveal the rapid adoption of hybrid and long-read assemblers such as Unicycler (>7400 citations; (Wick et al., 2017)), Canu (~7100 citations; (Koren et al., 2017)), Flye (~4700 citations; (Kolmogorov et al., 2019)), Miniasm (~1200 citations; (Li, 2016)), wtdbg2/Redbean (~2800 citations; (Ruan and Li, 2020)), and Shasta (~385 citations; (Shafin et al., 2020)) (Fig. 2, Table 3). These assemblers are now widely used in surveillance and clinical settings, while Shasta and wtdbg2 are gaining traction in Nanopore-based epidemiology (Zimin et al., 2013; Wick et al., 2017; Koren et al., 2017; Kolmogorov et al., 2019; Li, 2016) (Fig. 2, Table 3). These trends are illustrated in Fig. 2, which compares citation metrics and highlights how usage patterns reflect both technical strengths and evolving community needs.

## Comparative Citation Metrics and Usage Indicators of Major Genome Assemblers Applied in Microbiology

<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Adoption and bibliometric trends of genome assemblers. Comparative citation metrics of SPAdes, Unicycler, Canu, Flye, wtdbg2, and Shasta across multiple databases (PubMed, Google Scholar, Scopus), showing SPAdes dominance and the rapid rise of hybrid/long-read assemblers.</figcaption></figure>
<caption>**Table 3**</caption>

Challenges and limitations in microbial genomics. The table outlines major obstacles such as sequencing errors, incomplete assemblies, database biases, computational demands, and lack of interoperability, emphasizing the need for standardized, scalable, and reproducible bioinformatics pipelines.

<table><thead><tr><th>Type</th><th>Tool</th><th>Scope/Typical Use</th><th>Main Databases</th><th>Input/Output</th><th>Strengths</th><th>Limitations</th><th>Key Ref.</th></tr></thead><tbody><tr><td>Genome annotation (pipeline)</td><td><strong>Prokka</strong></td><td>Fast annotation of bacterial isolates</td><td>UniProt/Swiss-Prot, RefSeq, Pfam, TIGRFAMs</td><td>FASTA/GenBank → GFF3, GenBank, TSV</td><td>Very fast, reproducible, widely adopted</td><td>Less “deep” than curated pipelines; depends on local DBs</td><td>Seemann 2014</td></tr><tr><td>Genome annotation (pipeline)</td><td><strong>Bakta</strong></td><td>Standardized prokaryotic annotation; submission-ready outputs</td><td>UniProt, RefSeq, Pfam, TIGRFAMs, Rfam</td><td>FASTA → GFF3/GBK, reports</td><td>Consistent naming/ met data; clean outputs</td><td>Newer adoption vs Prokka/PGAP; DB-version dependent</td><td>Schwengers et al. 2021</td></tr><tr><td>Genome annotation (pipeline)</td><td><strong>DFAST</strong></td><td>Lightweight pipeline and web server for prokaryotes</td><td>RefSeq, UniProt (links to KEGG)</td><td>FASTA → annotation files, maps</td><td>Easy (web/CLI), built-in QC</td><td>Coverage/curation &lt; PGAP; server dependence</td><td>Tanizawa et al. 2018</td></tr><tr><td>Genome annotation (reference)</td><td><strong>NCBI PGAP</strong></td><td>Reference-grade annotation for GenBank submissions</td><td>CDD, RefSeq proteins/HM M, tRNAscan-SE, Rfam</td><td>FASTA/assembly → ASN.1, GFF3, tables</td><td>Rigorous curation; NCBI standard</td><td>Stricter formats; higher compute; less flexible</td><td>Tatusova et al. 2016</td></tr><tr><td>Gene prediction (structural)</td><td><strong>Prodigal</strong></td><td>ORF calling for prokaryotes (isolates & metagenomes)</td><td>—</td><td>FASTA → gene/protein coordinates</td><td>Very accurate and fast; used by many pipelines</td><td>No functional assignment</td><td>Hyatt et al. 2010</td></tr><tr><td>Gene prediction (structural)</td><td><strong>GeneMarkS-2</strong></td><td>ORF calling with self-trained HMMs</td><td>—</td><td>FASTA → genes/proteins</td><td>High sensitivity; detects atypical genes</td><td>Academic license; slower</td><td>Lomsadze et al. 2018</td></tr><tr><td>Functional annotation (orthology)</td><td><strong>eggNOG-mapper v2</strong></td><td>Ortholog & function assignment (COG/GO/KEGG)</td><td>eggNOG</td><td>Protein/nucl. → function/GO/KEGG tables</td><td>Broad coverage, scalable & fast</td><td>Orthology-driven; sensitive to thresholds</td><td>Cantalapiedra et al. 2021</td></tr><tr><td>Functional annotation (domains)</td><td><strong>InterProScan v5</strong></td><td>Multi-signature domain & family detection</td><td>InterPro (Pfam, TIGRFAMs, SMART, PROSITE, ...)</td><td>Protein → domains, GO</td><td>Widest family coverage</td><td>Heavy compute; complex install</td><td>Paysan-Lafosse et al. 2023</td></tr><tr><td>KEGG assignment</td><td><strong>KOfamKOALA</strong></td><td>KO via HMMs with adaptive cutoffs</td><td>KEGG KOfam</td><td>Protein → KO, pathways</td><td>Family-specific scores; precise</td><td>KEGG license needed for some uses</td><td>Aramaki et al. 2020</td></tr><tr><td>KEGG assignment (web)</td><td><strong>BlastKOALA/GhostKOALA</strong></td><td>KO assignment on KEGG servers</td><td>KEGG</td><td>Protein/nucl. → KO, pathways</td><td>Simple web service; pathway maps</td><td>Quotas/queues; server dependence</td><td>Kanehisa et al. 2016–2017</td></tr><tr><td>Functional profiling (multi-DB)</td><td><strong>MicrobeAnnotator</strong></td><td>Functional profiles across KEGG/COG/Pfam/InterPro</td><td>KEGG, COG, Pfam, InterPro</td><td>FASTA/GBK → tables, graphics</td><td>Interpretation ready summaries</td><td>Less standardized than PGAP/eggNO G-mapper</td><td>BMC Bioinformatics 2021</td></tr><tr><td>Functional profiling (ontologies)</td><td><strong>ProkFunFind</strong></td><td>Ontology-centric mapping of microbial functions</td><td>KEGG, eggNOG, GO</td><td>FASTA/GBK → ontology tables</td><td>Emphasis on biological interpretation</td><td>New tool; adoption in progress</td><td>Dufault-Thompson & Jiang 2024</td></tr></tbody></table>

# 5. Structural and functional annotation of microbial genomes

## 5.1. Structural annotation

Once assemblies are obtained, structural annotation provides the foundation for downstream interpretation by identifying coding sequences (CDSs) and non-coding RNAs. Widely used tools such as Prodigal and GeneMarkS-2 can predict open reading frames with high accuracy, while tRNAscan-SE identifies transfer RNAs and other essential RNA features (tRNAscan-SE: Searching for tRNA Genes in Genomic Sequences, 2025; Lomsadze et al., 2018; BMC Bioinformat. [Internet], 2025). These tools have become integral to microbial genomics pipelines, with Prodigal alone being cited in more than 10,000 PubMed studies, underscoring its status as a core component of bacterial genome annotation (Table 3). In microbiology and genetics, structural annotation is critical not only for cataloging genes but also for establishing the genomic framework upon which functional predictions and comparative analyses are built (Richardson and Watson, 2013). By defining gene boundaries, start and stop codons, and RNA elements, structural annotation enables accurate mapping of functional categories (Tatusova et al., 2016), identification of operon structures (BMC Bioinformat. [Internet], 2025; Mao et al., 2014), and detection of mobile genetic elements (Siguier et al., 2014). This information is essential for

downstream applications such as antimicrobial resistance gene discovery, phylogenomic reconstruction, metabolic pathway inference, and the study of evolutionary adaptations (Tatusova et al., 2016; Richardson and Watson, 2013; Seemann, 2014) (Fig. 3).

## 5.2. Functional annotation

Beyond structural annotation, functional characterization assigns biological roles to predicted genes. Pipelines such as Prokka, PGAP, Bakta, and DFAST have become standard frameworks for rapid annotation, each balancing speed and comprehensiveness (Tatusova et al., 2016; Seemann, 2014; Schwengers et al., 2021; Tanizawa et al., 2018). Functional depth is further achieved by integrating orthology-based and domain-based methods: eggNOG-mapper v2 provides large-scale orthology assignments linked to COGs, KEGG, and GO terms (Cantalapiedra et al., 2021), while InterProScan 5 aggregates over 15 protein signature databases to identify protein domains, motifs, and functional families (Jones et al., 2014) (Fig. S1). In parallel, KEGG-based tools such as KOfamKOALA and BlastKOALA/GhostKOALA enable metabolic reconstruction, linking genes to enzymatic activities and pathway networks (Aramaki et al., 2020; Kanehisa et al., 2016). More recently, integrative pipelines like MicrobeAnnotator and ProkFunFind provide ontology-driven, interpretation-ready summaries, highlighting
<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Hierarchical integration of bioinformatics tools for microbial genomics. This framework illustrates a hierarchical integration process, starting with orchestration and portability using workflow engines (Nextflow, Snakemake, CWL), and standards for interoperability through containerization (Docker, Singularity), FAIR data principles (RO-Crate), and reproducible distribution (Bioconda). On this foundation, domain-specific modules such as genome annotation (PGAP, Prokka, Bakta), functional profiling (eggNOG-mapper, InterProScan, KOfamKOALA), and registries (Dockstore, nf-core) are integrated into workflows. These modules enable deployment targets like antimicrobial resistance typing (AMRFinderPlus, CARD/RGI), phylogenomics (GTDB-Tk), and HPC schedulers. The resulting system ensures governance and outcomes including reproducibility, scalability in cloud environments, interoperability, versioning, and auditability, ultimately leading to translation and impact through clinical and epidemiological applications, biotechnological innovation, decision support for precision medicine, and knowledge discovery.</figcaption></figure>

a shift toward semantic integration of functional data (Ruiz-Perez et al., 2021; Dufault-Thompson and Jiang, 2024). These approaches enrich microbial genomes with ecological, metabolic, and clinical insights, though their accuracy remains constrained by reference database completeness (Fig. 3).

### 5.3. Applications in microbiology

Structural and functional annotation provides the foundation for a wide range of applications in microbiology, spanning clinical surveillance, ecological investigations, and metabolic discovery. In clinical contexts, annotation pipelines are central to pathogen genomics as they enable the rapid identification of antimicrobial resistance (AMR) genes, virulence determinants, and plasmid content, which are indispensable for hospital surveillance and outbreak investigations. Tools such as ResFinder (Zankari, 2014), CARD/RGI (Jia et al., 2017), and AMRFinderPlus (Feldgarden et al., 2019) are routinely applied for AMR detection, while VirulenceFinder (J. Clin. Microbiol. [Internet], 2025) supports the recognition of virulence factors, and PlasmidFinder (Carattoli et al., 2014) together with MobileElementFinder (Johansson et al., 2021) provide insights into plasmid and mobile genetic element content (Fig. S1). Beyond clinical applications, annotation is equally essential in ecology and environmental microbiology, where genome-resolved metagenomics has revealed novel pathways such as cellulose degradation, nitrogen fixation, and lignocellulose breakdown, particularly in termite gut consortia dominated by Bacteroidota, Firmicutes/Bacillota, Spirochaetota, and Fibrobacterota (Brune, 2014; Mikaelyan et al., 2015a; Mikaelyan et al., 2017). Functional annotation tools such as Prokka (Seemann, 2014), eggNOG-mapper (Cantalapiedra et al., 2021), and the KEGG Automatic Annotation Server (KAAS) (Moriya et al., 2007) are widely employed in these contexts to assign gene functions and reconstruct metabolic capacities, thereby linking genomic variation with ecological function. Structural and functional annotation also plays a critical role in systems microbiology and metabolic engineering, where platforms such as ModelSEED (>Henry et al., 2010) enable genome-scale metabolic reconstructions, supporting predictions of metabolic fluxes, growth phenotypes, and the design of industrially relevant microbial strains. In addition to primary metabolism, annotation has

become central to natural product discovery: antiSMASH (Blin et al., 2021) is the reference tool for detecting biosynthetic gene clusters and predicting the production of antibiotics, pigments, and signaling molecules, a role reflected in its more than 6000 PubMed citations. More recently, visualization platforms such as iTOL (Letunic and Bork, 2021) and Proksee (Grant et al., 2023) have expanded the accessibility of microbial genomics by enabling the interactive exploration of annotated bacterial genomes, phylogenetic trees, and mobile genetic elements, complementing traditional detection pipelines and facilitating the biological interpretation of genomic data. Taken together, these applications highlight the central role of annotation as an integrative layer of microbial genomics, bridging raw sequencing data with biological meaning and connecting genomes not only to taxonomy and resistance but also to metabolic functions, ecological interactions, and clinical outcomes. (Fig. 3, Fig. S1).

### 5.4. Ontology-driven synthesis and integrative pipelines

In bioinformatics, an ontology is a hierarchical and standardized system of concepts that describes biological functions, cellular processes, or metabolic pathways, providing a common language to unify gene annotation and facilitate comparisons across databases and studies (Ashburner et al., 2000; Gene Ontology Consortium, 2004; The Gene Ontology Consortium, 2019). Ontology-driven synthesis therefore enhances the interpretation of genomic data by restructuring gene-level annotations into higher-order biological functions. Rather than dealing with fragmented lists of predicted genes, frameworks such as Microbe-Annotator (Ruiz-Perez et al., 2021) and ProkFunFind integrate the outputs of multiple databases and reorganize them into pathway- and ontology-based categories (Ruiz-Perez et al., 2021; Dufault-Thompson and Jiang, 2024). In practice, comprehensive annotation workflows combine several layers: first, a reproducible pipeline such as PGAP for standardized genome submissions or rapid tools like Prokka, Bakta, or DFAST; second, extended functional profiling with resources including eggNOG-mapper, InterProScan, and KEGG; and third, ontology-based synthesis tools that aggregate these results into biologically meaningful summaries. This layered approach exemplifies how bioinformatics transforms dispersed gene predictions into coherent insights on
metabolism, virulence, and ecological function. Nevertheless, challenges remain in harmonizing heterogeneous database outputs and ensuring reproducibility across studies (Fig. 3).

## 5.5. Best practices for reproducible annotation

Best practices in annotation emphasize reproducibility, transparency, and interoperability. These include systematic versioning of reference databases, explicit documentation of thresholds (identity, coverage, e-values, HMM scores), and cross-validation across orthology- and domain-based methods to minimize false positives (Wilkinson et al., 2016a). Workflow managers such as Nextflow and Snakemake orchestrate modular pipelines, while containerization tools (Docker, Singularity) and frameworks such as nf-core and Bactopia ensure portability and reproducibility across infrastructures (Petit and Read, 2020a; Ewels et al., 2020) (Fig. 4). At the same time, the adoption of standards such as FAIR principles, GA4GH schemas, and ontologies like EDAM, coupled with provenance tracking (RO-Crate), is central to improving interoperability and transparency. Looking forward, the integration of AI and machine learning into validated workflows (e.g., DeepVariant, AlphaMissense) is expected to enhance predictive annotation, providing more comprehensive and interpretable results for both basic and applied microbiology (Cheng et al., 2023a; Arslan et al., 2024). These developments underline the dual challenge of annotation: the need to balance biological accuracy with computational scalability while ensuring long-term reproducibility in rapidly evolving bioinformatics ecosystems (Fig. 4).

# 6. Phylogeny, taxonomy, comparative genomics, and community profiling

Accurate taxonomic classification, phylogenetic reconstruction, comparative genomics, and microbial community profiling are now indispensable pillars of microbiology, providing insights into microbial diversity, adaptation, and evolutionary dynamics. Genome-based taxonomy, exemplified by the Genome Taxonomy Database (GTDB) and its companion toolkit GTDB-Tk, offers standardized classifications for bacteria and archaea and has become widely adopted for both novel isolates and metagenome-assembled genomes, with adoption exceeding 8000 citations by 2024, while complementary tools such as FastANI provide quantitative thresholds for species delineation (Parks et al., 2015; Chaumeil et al., 2020; Jain et al., 2018) (Fig. 5). Phylogenetic

<figure><img src="image_5.png" /><figcaption>**Fig. 5.** Bioinformatics tools for taxonomy, phylogeny, comparative genomics, and community profiling. Tools include GTDB-Tk and FastANI (taxonomy), PhyloPhlAn, IQ-TREE, RAxML-NG (phylogeny), Roary (comparative genomics), and QIIME 2, mothur, Kraken2, MetaPhlAn 3, Centrifuge (community profiling).</figcaption></figure>

frameworks such as PhyloPhlAn, IQ-TREE 2, and RAxML-NG enable robust evolutionary reconstruction and have been decisive both for taxonomic reclassifications, such as the redefinition of the genus *Clostridium*, and for documenting clonal transmission of multidrug-resistant *Klebsiella pneumoniae* (Segata et al., 2013; Nguyen et al., 2015; Kozlov et al., 2019; Wyres et al., 2020). Comparative genomics has been strengthened by pan-genome analysis tools such as Roary, which can process hundreds of genomes to distinguish core from accessory genes, shedding light on conserved versus adaptive functions (Page et al., 2015). At the ecological scale, microbial community profiling has been revolutionized by pipelines such as QIIME 2 and mothur for 16S-based studies and classifiers such as Kraken2, MetaPhlAn 3, and Centrifuge for shotgun metagenomics, enabling taxonomic resolution down to the strain level and transforming investigations of host-associated microbes and environmental ecosystems (Wood et al., 2019; Kim et al., 2016; Schloss et al., 2011; Bolyen et al., 2019). The integration of taxonomy, phylogeny, comparative genomics, and community profiling situates genomes within evolutionary and ecological frameworks, supporting applications ranging from outbreak surveillance and antimicrobial resistance monitoring to studies of symbiosis and biogeochemical cycling (Fig. 5). Despite persistent challenges such as the incompleteness and biases of reference databases (109,110), the underrepresentation of certain taxonomic lineages (Pasolli et al., 2019), and the computational demands associated with scaling analyses to

<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Standardization and submission workflow for microbial genomes. Pipeline from input data (FASTA, GFF, metadata), formatting, submission tools (PGAP, Genome Submit, Webin-ENA), to deposition in INSDC databases (GenBank/RefSeq, ENA, DDBJ), aligned with FAIR principles and GA4GH standards.</figcaption></figure>
handle rapidly expanding datasets now exceeding 1.5 million bacterial genomes referenced in NCBI/RefSeq as of 2025 (O'Leary et al., 2016; Sayers et al., 2022) these approaches remain indispensable to modern microbiology. In the future, the expansion of carefully curated reference databases (114), the development of hybrid strategies integrating both targeted (amplicon-based) and shotgun sequencing data (Quince et al., 2017), as well as the application of machine learning to enhance taxonomic prediction (Bengtsson-Palme, 2018), are expected to further strengthen their reproducibility, resolution, and impact across both clinical and ecological domains (Fig. 5).

## 7. Microbial community profiling

Microbial community profiling enables the study of human, animal, and environmental microbiomes and the identification of their ecological or clinical roles. Amplicon-based approaches, particularly 16S rRNA gene sequencing, remain widely used through pipelines such as QIIME 2 (Bolyen et al., 2019) and mothur (Schloss et al., 2011), which have been cited more than 30,000 and 25,000 times in PubMed, respectively, and continue to provide robust estimates of microbial diversity (Fig. 5). In parallel, shotgun metagenomics has gained increasing popularity thanks to rapid and accurate classifiers such as Kraken2 (Wood et al., 2019), MetaPhlAn 3 (Beghini et al., 2011) and Centrifuge (Kim et al., 2016), which are able to classify millions of reads within minutes, achieving taxonomic resolution down to the strain level. These pipelines are now central to large-scale initiatives such as the Human Microbiome Project and the Earth Microbiome Project, as well as to ecological investigations. A particularly illustrative case is our own study of the gut microbiota of seven termite species from Senegal (ongoing study), where 16S rRNA metabarcoding was applied to characterize bacterial diversity and ecological functions. The workflow combined QIIME 2 for sequence preprocessing and taxonomic assignment using the SILVA database (Bolyen et al., 2019; Quast et al., 2013), FastTree and IQ-TREE 2 for phylogenetic inference (Nguyen et al., 2015; Price et al., 2010), and iTOL for phylogenetic tree visualization (Letunic and Bork, 2021). The results revealed a predominance of Bacteroidota, Firmicutes/Bacillota, Spirochaetota, and Fibrobacterota, taxa well known for their roles in lignocellulose degradation and nitrogen cycling, in agreement with previous studies (Brune, 2014; Mikaelyan et al., 2015b; Hu et al., 2021). Beyond ecological applications, these bioinformatics pipelines are also widely applied in clinical and pathogenic contexts, including in our investigations of newly described *Bartonella* and *Salmonella* strains. Together, these examples highlight the versatility and complementarity of bioinformatics tools in microbial community profiling, demonstrating their capacity to connect community composition with ecological function and clinical relevance, while also underlining persistent challenges such as biased reference databases and limited reproducibility across pipelines. Besides ecological applications such as termite gut microbiota analysis, we also applied these bioinformatics pipelines in clinical and pathogenic contexts, notably in the description of new *Bartonella* and *Salmonella* strains. Together, these case studies highlight the versatility and complementarity of bioinformatics tools, which can address questions in both environmental microbiology and medical and public health microbiology.

### 7.1. Case study: New *Bartonella* species from bats

In our study of *Bartonella* strains isolated from bats (Labarrere et al., IJSEM-D-25-00297R1), genomic characterization enabled the description of new bacterial species through a bioinformatics pipeline integrating multiple tools. After extraction and sequencing, genome assembly was performed with SPAdes, followed by quality and completeness assessment with CheckM. Structural and functional annotation was carried out with Prokka, while comparative genomics employed Orthologous Average Nucleotide Identity (OrthoANI), digital DNA-DNA hybridization (dDDH), and the Proksee platform, confirming

the taxonomic novelty of the isolates. Phylogenetic inference relied on multilocus analysis of marker genes (*gltA*, *robo*, *ftsZ*, *ITS*) and tree construction using IQ-TREE 2 and FastTree, validated by visualization and interactive annotation with iTOL. These approaches led to the description of six new *Bartonella* species, demonstrating the importance of bioinformatics pipelines in bacterial systematics and zoonotic surveillance, particularly for emerging pathogens transmitted by reservoir hosts (Sayers et al., 2022).

### 7.2. Case study: Genomic sequencing and typing of *Salmonella* *enterica* in primates

In our study of rare *Salmonella enterica* strains isolated from primates (ongoing study, 2025), genomic analysis employed a hybrid pipeline integrating multiple bioinformatics steps. Read quality control was performed with Trimmomatic and Filtlong, followed by hybrid assembly using Unicycler, optimized to combine the accuracy of Illumina reads with the continuity of Nanopore data. Genome annotation was carried out with Prokka, while phylogenetic analyses were conducted with IQ-TREE 2 and Scapper, situating the isolates within a robust evolutionary framework. For in silico serotyping, SeqSero2 and SISTR were applied, confirming the identification of unusual serovars such as Abadina, Ipeko, and Hvittingfoss. These analyses illustrate how the integration of quality control, assembly, annotation, and phylogenetic typing pipelines not only enables fine-scale pathogen characterization but also supports One Health epidemiological surveillance by linking wildlife reservoirs, human health, and the emergence of rare serovars (Mukherjee et al., 2023).

## 8. Standardization and submission to public databases

The submission of microbial genomes to public databases represents a final and indispensable step in the bioinformatics pipeline, ensuring transparency and reproducibility of scientific research. Data must be formatted according to strict standards, including nucleotide sequences in FASTA, gene annotations in GFF, and associated metadata (sample origin, culture conditions, experimental parameters). The main international repositories NCBI GenBank/RefSeq, ENA-EBI, and DDBJ form the core of the International Nucleotide Sequence Database Collaboration (INSDC), which currently hosts more than 2.5 billion sequences and nearly 1.5 million complete bacterial genomes deposited in NCBI by 2025 (RefSeq/NCBI Statistics, 2025). To facilitate submissions, several automated tools are available: PGAP for structured annotation and NCBI submissions (Tatusova et al., 2016), Genome Submit for direct deposits, and Webin-ENA for the European platform (Fig. 4). The importance of this step extends far beyond archiving: it promotes global data sharing, enables cross-study comparisons, and feeds the reference databases that underpin all bioinformatics pipelines. In microbiology, missing or incomplete genome submissions significantly reduce the long-term value of scientific discoveries, as other researchers cannot fully reuse or validate the data. To address this, international initiatives have introduced shared principles and frameworks to improve data management. The FAIR data principles provide guidelines ensuring that genomic datasets are easy to locate, openly available under clear conditions, compatible across platforms, and suitable for reuse in future studies (Wilkinson et al., 2016a). Complementing this, the GA4GH is a worldwide consortium that develops technical standards and policy frameworks to promote responsible, secure, and standardized sharing of genomic and clinical data across institutions and countries (Rehm et al., 2021). Together, FAIR and GA4GH aim to maximize the impact of microbiological data by improving transparency, interoperability, and global accessibility (Fig. 4).

The microbial genomics analytical pipeline integrates nine sequential stages: (i) sample collection and DNA extraction, (ii) quality control and library preparation, (iii) sequencing platform selection (short-read, long-read, or hybrid), (iv) read preprocessing and contamination
screening, (v) genome assembly, (vi) assembly polishing and quality
evaluation, (vii) structural and functional annotation, (viii) phyloge-
netic reconstruction and comparative genomics, and (ix) standardized
submission to public repositories with FAIR-compliant metadata cura-
tion (Fig. 6).

9. Persistent challenges and future perspectives for a predictive, reproducible, and inclusive microbial genomics

9.1. *Automation and standardization in bacterial systematics*

In bacterial systematics, new bioinformatics tools have emerged to
streamline the description of novel taxa and the preparation of proto-
logues. Protologger is an all-in-one genome description tool that auto-
matically gathers the essential data for species description, providing
taxonomic, functional, and ecological insights in a standardized format
suitable for publication (Williams et al., 2019). Complementary to this,
Proksee offers integrated project management and visualization of
bacterial genomes, combining assembly validation, annotation,
comparative genomics, and interactive genome maps that can be
exported for taxonomic reports (The Gene Ontology Consortium, 2019).
Finally, the Type Strain Genome Server (TYGS) delivers high-
throughput genome-based taxonomy, including ANI, dDDH, and phy-
logenomic trees, which are now considered essential metrics for delin-
eating species boundaries (Tastan Bishop et al., 2015). Together, these
platforms exemplify how automation and standardization are trans-
forming microbial taxonomy, making genome-based protologue prepa-
ration more efficient, reproducible, and globally accessible.

9.2. *Persistent technical challenges in genome assembly and annotation*

Despite major advances, microbial genomics continues to face sig-
nificant challenges that limit its scalability, reproducibility, and acces-
sibility. Raw sequencing reads often contain errors, uneven coverage,
and contamination, particularly in metagenomic data, complicating
genome reconstruction (Logsdon et al., 2020; Atwood et al., 2015). Even
with hybrid approaches and long-read sequencing, many genomes
remain fragmented, with plasmids and repetitive regions misassembled,
despite the use of quality metrics such as CheckM and BUSCO (Rhie
et al., 2021; Lomsadze et al., 2018; BMC Bioinformat. [Internet], 2025).
Functional annotation and taxonomic inference are still heavily
dependent on incomplete and biased databases, where underrepre-
sented lineages and database drift reduce reliability (Gilbert et al.,
2018). Computational demands represent another bottleneck, as tools
such as InterProScan or pan-genome analysis require access to HPC in-
frastructures (Ewels et al., 2020). Workflow managers like Nextflow and
Snakemake help standardize and automate analyses, while containeri-
zation technologies such as Docker and Singularity ensure portability
across computing environments, but many workflows remain hetero-
geneous and inconsistent (Wilkinson et al., 2016a).

9.3. *Economic barriers and the shift from sequencing to computational costs*

Economic barriers further exacerbate global inequalities: although
sequencing costs continue to decline to below 100 USD per microbial
genome with new platforms such as Ultima Genomics or DNBSEQ by
2025, the costs of data analysis and storage often exceed sequencing
itself (Fig. S2). For example, sequencing a genome may cost less than

<figure><img src="image_7.png" /><figcaption>Fig. 6. Integrated analytical pipeline for microbial genomics research: from sample to data dissemination. This schematic diagram illustrates the comprehensive workflow for microbial genomics analysis, organized into nine sequential stages spanning experimental procedures, computational analysis, and data stewardship. The pipeline is color-coded by functional category: experimental stages (blue, stages 1–3) encompass sample collection, quality control, and sequencing platform selection; bioinformatics stages (green, stages 4–6) include read preprocessing, genome assembly, and assembly refinement; analytical stages (orange, stages 7–8) comprise functional annotation and comparative genomics; and data dissemination (purple, stage 9) ensures FAIR-compliant data deposition. Each stage displays the key computational tools or experimental procedures employed, along with the primary output generated. The workflow emphasizes the interdependence of laboratory techniques and computational methods, highlighting critical quality control checkpoints that ensure data integrity and biological validity. Arrows indicate the directional flow of information and materials through the pipeline, from initial microbial isolation to public repository submission. This integrated approach ensures reproducibility, supports meta-analyses across studies, and facilitates global data sharing within the microbial genomics community. Key tools mentioned include: FastQC and Trimmomatic for quality filtering; SPAdes, Flye, and Unicycler for assembly; BUSCO and CheckM for assembly evaluation; Prokka and Bakta for annotation; and CARD, eggNOG, and KEGG for functional characterization. The pipeline concludes with submission to repositories such as NCBI GenBank, ENA, and MG-RAST, ensuring long-term accessibility and compliance with open science principles. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
100 USD (NEWARK, California, May 31st, 2022), while analyzing and archiving the associated raw data can cost two to three times more on commercial cloud platforms, shifting the main bottleneck from sequencing to computation and storage (Langmead and Nellore, 2018a; Via et al., 2011; Tenopir et al., 2016; Poplin et al., 2018) (Fig. S2). Recent developments have also emphasized the growing role of user-friendly bioinformatics platforms equipped with graphical user interfaces (GUIs), such as Galaxy and the Center for Genome Epidemiology (CGE) (Luo et al., 2020). These systems complement command-line environments by enabling researchers to perform complex genomic analyses through intuitive workflows, thereby broadening accessibility for users without advanced computational expertise. Such hybrid GUI/CUI infrastructures democratize NGS data interpretation and promote reproducible, transparent bioinformatics practices across the microbial genomics community (Cheng et al., 2023c).

## 9.4. Challenges related to bioinformatics skills and training gaps

The effective analysis of NGS data requires not only biological expertise but also proficiency in command-line environments, scripting languages (Python, R, Bash), and workflow management systems such as Nextflow and Snakemake. However, such computational skills remain unevenly distributed across research institutions, particularly in low- and middle-income countries, where infrastructure constraints and insufficient training programs limit the development of sustainable (Mulder et al., 2016) genomics research capacity (Mulder et al., 2018). The skill gap between experimental microbiologists and computational scientists presents a significant barrier to genomic technology adoption. Many instructors cite lack of time, expertise, and information as major barriers to teaching data management (Williams et al., 2019), while researchers in resource-limited settings often struggle to transition from data generation to interpretation due to limited computational training. This disparity is particularly pronounced in Africa and other LMICs, where the lack of experienced faculty capable of training bioinformatics students has significantly limited high-throughput biology research outputs (Tastan Bishop et al., 2015).

To address these challenges, international training initiatives such as GOBLET (Global Organization for Bioinformatics Learning, Education and Training), ELIXIR (Atwood et al., 2015), and EMBL-EBI provide standardized curricula, online courses, and hands-on workshops to strengthen data analysis competencies and promote FAIR and reproducible research practices (Via et al., 2011). Regional networks like H3ABioNet have demonstrated that targeted capacity-building efforts can successfully develop bioinformatics expertise through training workshops, internships, and collaborative projects (Mulder et al., 2018). Multidisciplinary collaboration between microbiologists, bioinformaticians, data scientists, and software engineers is essential to ensure robust study design, proper data interpretation, and sustainable pipeline maintenance. Building a globally skilled workforce requires sustained investment in education, infrastructure, and collaborative networks, making bioinformatics training an indispensable component of modern microbiological research (Tenopir et al., 2016).

## 9.5. Emerging technologies: AI, long-read sequencing, and quantum computing

In response to these limitations, emerging technologies point toward a more predictive and inclusive microbiology: artificial intelligence and machine learning tools such as DeepVariant (Poplin et al., 2018), Clair (Luo et al., 2020), and AlphaMissense (Cheng et al., 2023c) are already improving variant calling, functional annotation, and predictive modeling, paving the way for precision microbiology and AI-CRISPR integration (Qu et al., 2025); long-read sequencing platforms such as PacBio HiFi and ONT now deliver highly accurate reads that resolve plasmids, repeats, and even epigenetic modifications (Rhie et al., 2021), (Wenger et al., 2019), (Gao et al., 2023); cloud-native bioinformatics

and exascale computing enable real-time analysis of thousands of genomes (Langmead and Nellore, 2018b), (Perkel, 2021c); community-driven workflows such as nf-core and Bactopia (Petit and Read, 2020b) reinforce reproducibility in line with FAIR (Wilkinson et al., 2016b) and GA4GH (Rehm et al., 2021) standards; and finally, quantum computing an emerging paradigm using the principles of quantum mechanics to solve problems beyond the reach of classical computers is beginning to be explored for genome alignment, protein folding, and antimicrobial drug design, although technical barriers remain (Langmead and Nellore, 2018b) (Mokhtari et al., 2024) (Table 3).

# 10. Conclusion: Toward a predictive, reproducible, and inclusive microbial genomics

In summary, microbial genomics stands at a crossroads: sequencing errors, incomplete assemblies, biased databases, massive computational requirements, and economic barriers still hinder its global deployment. Yet, the convergence of AI/ML, long-read/T2T sequencing, cloud/HPC infrastructures, and quantum computing promises a microbial bioinformatics ecosystem that is predictive, reproducible, and globally inclusive, with transformative implications for medicine, ecology, and biotechnology.

Supplementary data to this article can be found online at https://doi.org/10.1016/j.meegid.2025.105859.

## Institutional review board statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the National Health Research Ethics Committee of the Ministry of Health and Social Action of Senegal (No.: 0000045/MSAR/CNERS/SP). The samples were collected during patient care with their informed consent, specifically for the purposes of scientific and non-profit research.

## CRediT authorship contribution statement

**Cheikh Tidiane Houmenou:** Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.
**Cheikh Sokhna:** Validation, Supervision, Investigation.
**Florence Fenollar:** Writing – original draft, Visualization, Supervision.
**Oleg Mediannikov:** Writing – review & editing, Validation, Supervision, Project administration, Investigation, Formal analysis, Conceptualization.

## Declaration of competing interest

We declare that we have no competing interests: This work was supported by a grant from the French Government managed by the National Research Agency under the “Investissements d’avenir (Investments for the Future)” programme with the reference ANR-10IAHU-03 (Méditerranée Infection), by the Contrat Plan Etat-Région and the European funding FEDER IHUPERF.

## Data availability

No data was used for the research described in the article.

## Acknowledgements

We are grateful to the Direction des Parcs Nationaux and the Direction des Eaux et Forests Chasses et de la Conservation des Sols for permission to work in Senegal. “We would like to express our sincere gratitude to Dr. Maryam Tidjani Alou (Microbiologist, MSc - PhD in human pathology) for their invaluable assistance and insightful guidance throughout this work.
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