The human gastrointestinal tract harbors a highly diverse and dynamic microbial ecosystem, predominantly composed of over 1,000 bacterial species, with the phyla Firmicutes and Bacteroidetes being the most dominant (Bull and Plummer, 2014). This microbiota functions as a critical interface between the host and its environment, playing indispensable roles in nutrient metabolism, immune system modulation, and pathogen resistance. Dietary fibers fermented by these microbes generate short-chain fatty acids (SCFAs) notably acetate, propionate, and butyrate—which reinforce intestinal barrier integrity, modulate systemic immune responses, and suppress inflammation by inducing regulatory T-cell differentiation (Yang and Cong, 2021; Rooks and Garrett, 2016).

Beneficial symbionts such as Faecalibacterium prausnitzii and Akkermansia muciniphila enhance mucosal immunity by producing anti-inflammatory metabolites like indole derivatives and conjugated linoleic acid (Yang and Cong, 2021). Additionally, commensal microbes maintain ecological balance by competitively excluding pathogens and secreting antimicrobial compounds such as bacteriocins, thereby ensuring gastrointestinal homeostasis (Zeng et al., 2016). The complex interplay between specific microbial taxa, their metabolic byproducts, and their roles in health and disease is summarized in Tables 1, 2.

Disruptions in microbial equilibrium, termed dysbiosis, can be triggered by factors like high-sugar diets, antibiotic overuse, and reduced fiber intake. Dysbiosis favors pro-inflammatory taxa such as Enterobacteriaceae and Fusobacterium nucleatum, while depleting protective microbes (Forster et al., 2019; Zeng et al., 2016). These alterations impair the intestinal barrier, allowing microbial products like lipopolysaccharide (LPS) and flagellin to translocate into systemic circulation. Such pathogen-associated molecular patterns (PAMPs) activate Toll-like receptors (TLRs) and NOD-like receptors (NLRs), triggering NF-κB signaling and systemic low-grade inflammation that underlies metabolic diseases such as obesity and T2D (Potrykus et al., 2021).

In IBD for instance, blooms of Enterobacteriaceae are associated with elevated IL-17 production and mucosal damage (Zeng et al., 2016). Similarly, in colorectal cancer (CRC), overabundance of Bacteroides fragilis promotes oncogenic Wnt/β-catenin signaling through polysaccharide A, emphasizing how pathobionts exploit dysbiosis to drive disease progression (Bull and Plummer, 2014; de Vos et al., 2022).

The impact of gut dysbiosis extends to extraintestinal sites via multiple host-microbe interaction axes.

Beyond metabolic and neurological impacts, gut microbial metabolites significantly influence cardiovascular health and multisystemic processes. One such metabolite, phenylacetylglutamine (PAGln), produced by gut microbes from dietary phenylalanine, has been shown to enhance platelet reactivity, thereby increasing the risk of atherosclerosis and thrombotic events. Additionally, metabolites like TMAO contribute to endothelial dysfunction, inflammation, and lipid metabolism disturbances. These microbial products not only affect cardiovascular physiology but also have systemic effects, linking gut dysbiosis to broader health issues, including chronic kidney disease and metabolic syndrome, underscoring the gut microbiota’s central role in maintaining systemic homeostasis and disease susceptibility (Rooks and Garrett, 2016).

The 16S rRNA gene is a highly conserved and universally present genetic marker in bacterial and archaeal genomes, making it one of the most widely used tools for microbial taxonomic profiling (Bukin et al., 2019). Cryan et al. (2019) highlighted that 16S rRNA sequencing is a cost-effective, widely accessible method for studying microbial communities, particularly in the gut microbiome of humans, mice, and insects. This method allows for the assessment of microbial diversity and relative abundance using next-generation sequencing technologies.

In this approach, PCR is used to amplify conserved regions of the 16S rRNA gene, and the variable regions are sequenced to distinguish different taxa. The resulting sequences, or amplicons, are grouped into operational taxonomic units (OTUs), typically at 97% sequence similarity (Bukin et al., 2019). Alternatively, amplicon sequence variants (ASVs), generated through denoising algorithms such as DADA2 or Deblur, offer higher resolution and reduced error rates (Prodan et al., 2020).

However, while 16S rRNA is the most commonly used genetic marker for bacterial identification, it is not without limitations. Critically, the 16S rRNA gene is not always present as a single copy within bacterial genomes-some bacteria carry multiple copies with varying sequences. This gene copy number variability can distort estimates of microbial abundance and skew taxonomic profiles, especially when comparing species with different 16S copy numbers. Moreover, the resolution of 16S rRNA sequencing is generally limited to the genus level, often lacking the specificity to accurately identify species (Knight et al., 2018; Callahan et al., 2017).

Different strategies for OTU clustering (e.g., de novo, closed-reference) further influence the accuracy and comparability of results (Callahan et al., 2017). Despite these challenges, 16S rRNA sequencing remains a widely adopted approach for large-scale microbiome studies, especially when focusing on bacterial and archaeal populations across various health and disease conditions (Prodan et al., 2020).

Shotgun metagenomic sequencing provides a comprehensive and accurate depiction of microbial communities by sequencing all DNA present in a sample, surpassing 16S rRNA in species-level resolution and functional potential (Dovrolis et al., 2017; Ranjan et al., 2016). However, this approach is expensive, data-intensive, and technically complex. Crucially, the accuracy and reproducibility of results heavily depend on sample handling, conservation, and preparation procedures-critical elements often overlooked. Temperature control, cell-size filtration, and DNA extraction methods directly influence microbial representation and community structure, introducing variability across studies.

Inconsistent sample processing, such as improper freezing or delayed processing, may degrade DNA, affect microbial viability, or bias detection of certain taxa. Moreover, variations in DNA extraction protocols can result in differential lysis efficiency, leading to underrepresentation of key microbial groups, particularly Gram-positive species. Filtration by cell size, if improperly performed, may skew microbial diversity by excluding small-sized microbes or including host DNA (Ranjan et al., 2016).

The NGS library construction for RNA or DNA uses a procedural methodology that results in variations between studies. Regardless of whether a complete sample is being sequenced, this methodology entails the generation of infinitesimal reads ranging from 25 to 500 base pairs. This allows the identification of microorganisms that are either unknown or exist in minute quantities. Chiu and Miller (2019) reported that extensive bioinformatics preprocessing tools are required, including pruning, merging, assembly, scaffolding, and mapping tools. Following the sequencing procedure, distinct sequences of the microbial components of the samples will be produced in fasta or fastq files, along with a mapping file that contains all the necessary metadata associated with the sample. It is said that these files serve as inputs to the subsequent identification of the species to which the sequences belong and the assignment of taxonomy to the sequences (Caporaso et al., 2010). Using the term OTU as shown in Figure 2, it is possible to identify groups of similar sequences that have the potential to represent a distinct taxonomic classification based on these similarities (Caporaso et al., 2010).

Although this method is not without faults, it is remarkably effective for clustering sequences with 97% similarity. Using phylogenetic alignment, one sequence is selected per OTU to represent its corresponding taxa. Numerous bioinformatics techniques and algorithms have been devised in the field of shotgun and 16S rRNA metagenomics, either as independent homology- and prediction-based methods or as components of more comprehensive workflows (Singh et al., 2022; Edgar, 2018).

Multiple investigations, utilising 16S rRNA analysis, have demonstrated a connection between the gut microbiota and general well-being (Kinross et al., 2011; Schippa and Conte, 2014; Ganesan et al., 2018). An extensive examination of the gut metagenome, including WGS, can enhance our understanding of the development of illnesses and allow for the discovery of new therapeutic targets. This occurs because there is a chance of discovering small genetic differences among species that cause changes in physical traits, ultimately resulting in the development of diseases. For instance, WGS investigations carried out with Citrobacter spp. have shown that genetic differences within the species lead to changes in their observable characteristics and ability to adapt to different environments (Karlsson et al., 2013).

At present, the use of Illumina shotgun sequencing of stool samples is widely prevalent in the field of whole genome WGS studies of the gut microbiome. This is primarily because the gut harbours a multitude of diverse microbial species, thereby necessitating a thorough sequencing process with a coverage of at least 20 times. The purpose of such extensive sequencing is to investigate and analyze individual communities within the gut microbiome that possess low abundance (Karlsson et al., 2013). However, it is important to analyse the substantial amount of WGS data, particularly in the form of short reads, which poses significant challenges. This is needful because the gut microbiome is home to a wide range of bacterial species, ranging from hundreds to thousands, all of which exhibit varying levels of abundance. Complicating matters further, there is a lack of taxonomic identification available for the majority of these species, further exacerbating the analytical complexities (Karlsson et al., 2013; Caporaso et al., 2010).

Bioinformatics pipelines are essential for analyzing gut microbiota, allowing researchers to process and interpret complex metagenomic datasets efficiently. They support taxonomic, functional, and strain-level analyses, providing insights into microbial diversity and health associations. Table 3 highlights key, updated bioinformatics tools commonly used in gut microbiota studies for comprehensive and accurate data analysis.

These pipelines streamline data processing from raw sequences to biological insights, enabling researchers to study gut microbiota’s role in health and disease. By integrating these tools, researchers can uncover novel therapeutic targets and biomarkers, advancing our understanding of microbiome-disease interactions.

Long-read sequencing technologies, such as Oxford Nanopore and PacBio SMRT, have transformed metagenomic analyses by enabling the resolution of complex genomic regions and structural variations that evade detection by short-read methods. These platforms generate reads spanning thousands of base pairs, allowing for the assembly of complete microbial genomes, including repetitive elements, plasmids, and mobile genetic elements critical for antibiotic resistance and virulence (Panahi et al., 2024; Satam et al., 2023). Unlike short-read sequencing, which fragments these regions, long-read data preserves genomic context, enhancing functional annotation accuracy and strain-level resolution. For instance, long-read sequencing has resolved strain-specific adaptations in Citrobacter spp., linking genetic variations to phenotypic changes in environmental adaptability and pathogenicity (Logsdon et al., 2020). This capability is vital for studying horizontal gene transfer dynamics in dysbiotic gut communities, where plasmid-borne resistance genes proliferate. Additionally, long-read sequencing mitigates biases in taxonomic profiling by capturing low-abundance taxa and uncultured species, which are often underrepresented in traditional workflows (Ruscheweyh et al., 2022). Long-read sequencing technologies overcome limitations of short-read methods by resolving repetitive genomic regions, structural variations, and microbial mobile elements. Below is a comparative analysis of key platforms and their applications in gut microbiota research (Table 4).

The application of long-read sequencing in gut microbiota research has unveiled niche-specific microbial activities and structural variations driving disease. For example, it has identified mucosal-associated Escherichia coli strains in IBD patients with intact virulence operons, correlating with NF-κB-mediated inflammation (Schirmer et al., 2019). Similarly, long-read assemblies of Akkermansia muciniphila genomes have revealed strain-specific mucin degradation pathways critical for metabolic health (Ouwerkerk et al., 2022). Despite its advantages, challenges persist, including higher costs, computational demands for data processing, and the need for high-quality DNA input. Platforms like PacBio HiFi address error rate limitations, offering >99% accuracy for clinical-grade analyses (Oehler et al., 2023). As these technologies mature, they will bridge gaps in functional and structural metagenomics, enabling precision interventions targeting microbial contributions to diseases like T2D and colorectal cancer (Oehler et al., 2023).

Single-cell metagenomics has emerged as a transformative strategy to resolve uncultured microbial taxa and strain-level heterogeneity, overcoming limitations of bulk sequencing methods that obscure rare or low-abundance species (Xu and Zhao, 2018). By isolating individual microbial cells via microfluidics or fluorescence-activated cell sorting, this approach bypasses PCR amplification biases and culturability challenges, enabling direct sequencing of genomes from “microbial dark matter.” For example, single-cell genomics has expanded the Human Gastrointestinal Bacteria Culture Collection (HBC), providing genomic blueprints for novel species like Saccharimonadia and uncultured Clostridiales, which evade traditional cultivation (Yu et al., 2022). This technique resolves strain-specific genetic variations, such as antibiotic resistance gene clusters in Escherichia coli subpopulations or mucin-degrading adaptations in Akkermansia muciniphila strains, critical for understanding niche-specific functionalities (Ouwerkerk et al., 2022). Coupled with AI-driven annotation pipelines, single-cell data enhances reference databases, improving taxonomic classification accuracy by 30% compared to conventional methods (Erfanian et al., 2023). Furthermore, it elucidates horizontal gene transfer dynamics, revealing plasmid-mediated virulence factor exchange in dysbiotic gut communities. By decoding strain-specific metabolic capabilities and host-interaction genes, single-cell metagenomics bridges gaps in functional annotation, offering insights into microbial contributions to diseases like IBD and T2D, while guiding precision probiotics and phage therapies (Ott and Mellata, 2022).

Integrative multi-omics frameworks synergize genomic, transcriptomic, and metabolomic datasets to unravel the functional and spatial dynamics of gut microbiota. By coupling shotgun metagenomics with metatranscriptomics, researchers can map microbial gene expression to metabolic pathways, revealing how taxa like Akkermansia muciniphila modulate mucin degradation or Faecalibacterium prausnitzii regulate butyrate synthesis in health and disease (Chetty and Blekhman, 2024). For instance, metatranscriptomic profiling in T2D patients identified upregulated carbohydrate metabolism genes in Muribaculaceae, linking microbial activity to glycemic dysregulation (Zhu and Goodarzi, 2020). Tools like HUMAnN3 quantify pathway contributions across taxa, while KEGG and EggNOG databases annotate gene functions, enabling systems-level insights into host-microbe crosstalk (Hernández-Plaza et al., 2022). These frameworks resolve strain-specific adaptations, such as Citrobacter spp. genetic variations influencing environmental adaptability, and track horizontal gene transfer of antibiotic resistance genes via plasmids. There are few important integrative multi-omics methods for gut microbiota analysis are listed in Table 5.

Metabolomic integration further contextualizes microbial activity by identifying metabolites like SCFAs or TMAO that mediate host physiology. For example, NMR-based metabolomics paired with metagenomics revealed reduced butyrate and elevated LPS in T2D, correlating with Bifidobacterium depletion and Enterobacteriaceae blooms (Zou et al., 2022). Multi-omics platforms like MetaboAnalyst and XCMS align metabolite profiles with microbial gene expression, clarifying mechanisms like bile acid transformations by Clostridium scindens in non-alcoholic fatty liver disease (Eicher et al., 2020). However, challenges persist in data harmonization, as batch effects and platform-specific biases require advanced normalization algorithms (Adamer et al., 2022). Emerging AI-driven pipelines, such as MetaBGC, predict biosynthetic gene clusters from metagenomic reads, accelerating therapeutic discovery (Sahayasheela et al., 2022). By bridging omics layers, these frameworks decode microbial contributions to disease, paving the way for precision probiotics and microbiome-editing therapies (Sahayasheela et al., 2022).

The integration of artificial intelligence (AI) and machine learning (ML) into metagenomics has transformed functional annotation and pathway prediction, addressing limitations of traditional homology-based methods such as database bias and incomplete reference genomes. AI-driven tools like MetaBGC leverage probabilistic models to identify biosynthetic gene clusters (BGCs) directly from metagenomic sreads, enabling the discovery of geographically stratified metabolites with therapeutic potential, such as type II polyketides with antimicrobial properties (Vaccaro et al., 2024; Tsouka and Masoodi, 2023). For instance, MetaBGC identified BGCs in gut microbiota linked to dietary adaptations, offering insights into evolutionary strategies of uncultured taxa like Clostridiales (Vaccaro et al., 2024). Similarly, DeepARG, a deep learning framework, predicts ARGs from raw sequencing data with 20% higher accuracy than BLAST-based methods, critical for tracking plasmid-borne resistance genes in dysbiotic communities (Tsouka and Masoodi, 2023; Quainoo et al., 2017). These models bypass reliance on reference databases, enabling annotation of “microbial dark matter” that lacks representation in public repositories (Kanehisa et al., 2015).

ML frameworks also enhance pathway prediction by integrating multi-omics data. HUMAnN3 employs hierarchical alignment to KEGG and MetaCyc databases, quantifying taxonomic contributions to metabolic pathways. For example, it mapped Akkermansia muciniphila mucin degradation pathways to improved insulin sensitivity in metabolic syndrome (Vaccaro et al., 2024; Kanehisa et al., 2015). AI models trained on metatranscriptomic data have linked upregulated carbohydrate metabolism genes in Muribaculaceae to glycemic dysregulation in T2D (Shen et al., 2015). Tools like eggNOG-mapper use DIAMOND aligners to assign orthologous groups, improving functional annotation of genes from fragmented metagenomic assemblies (Tsouka and Masoodi, 2023). Meanwhile, MMvec, a neural network, predicts microbe-metabolite interactions, such as Faecalibacterium prausnitzii-derived butyrate synthesis correlating with IBD remission (Vaccaro et al., 2024; Shen et al., 2015). The key AI/ML tools for functional annotation and pathways prediction are listed in the Table 6.

AI/ML frameworks bridge gaps in strain-specific gene function and host-microbe crosstalk. For example, Citrobacter spp. strain variations influencing environmental adaptability were resolved using PacBio HiFi sequencing and ML-driven annotation (Quainoo et al., 2017). These tools are pivotal for identifying therapeutic targets, such as Clostridium scindens-mediated bile acid metabolism in NAFLD (Kanehisa et al., 2015). As the field advances, AI-driven metagenomics will underpin precision probiotics and microbiome-editing therapies, translating microbial ecology into clinical innovation (Vaccaro et al., 2024; Huerta-Cepas et al., 2017).