The advent of 16S rRNA gene sequencing has become a cornerstone of microbiome research in animal and food agriculture, offering a cost-effective, high-throughput method for taxonomic classification and community profiling (Ricke et al., 2017). Although traditional culture-based techniques provided foundational knowledge, they were inherently limited by their reliance on selective enrichment and growth media, which excluded many fastidious or unculturable microorganisms (Rodriguez-R and Konstantinidis, 2014; Ricke, 2015). Advances in sequencing technologies and bioinformatics have evolved microbial ecology studies, enabling comprehensive, culture-independent characterization of GIT microbial communities. Studies now routinely utilize 16S rRNA gene sequencing to examine the composition and diversity of complex microbiomes in most food-animal production systems, processing, and food products (Sabater et al., 2021; Olson et al., 2022; Ricke et al., 2017; Ricke et al., 2022a; Ricke et al., 2022b; Ricke et al., 2022c; Weinroth et al., 2022). Most importantly, these technologies support the discovery of biologics by identifying beneficial microorganisms and their ecological roles, microbial shifts in response to dietary interventions, pathogen exposure, and other environmental or physiological variables.

However, repeatability in 16S rRNA microbiome studies remains a widely acknowledged limitation, particularly when analyzing low-biomass or spatially heterogeneous samples. Technical and biological replicates can yield noticeably different community profiles due to small-scale variations in DNA input, extraction efficiency, and sequencing noise (Olson et al., 2024; Schmidt et al., 2018). Even duplicate swabs or tissue biopsies collected from adjacent sites may differ in microbial composition, highlighting the impact of microenvironmental variability and sampling error. These issues can be compounded by amplification bias, stochastic read dropout, and taxonomic assignment inconsistency, especially when microbial load is low or diversity is high. To mitigate these concerns, standardization of DNA extraction protocols, rigorous sample homogenization, and the use of negative/positive controls and technical replicates are recommended (Nearing et al., 2021). Moreover, increasing sequencing depth and applying compositional data analysis tools may improve the detection of true biological signals amid technical variability.

Initially proposed by Carl Woese, the 16S rRNA gene is a highly conserved component of the bacterial genome with variable regions that permit phylogenetic differentiation (Olsen and Woese, 1993; Clarridge, 2004). Amplification and sequencing of these variable regions enable broad-range microbial detection and taxonomic resolution down to the genus or species level, depending on sequencing depth and primer selection (Caporaso et al., 2011). Despite its utility, 16S rRNA sequencing has known limitations. One of the most obvious is that it cannot distinguish between live and dead cells, and the data generated are compositional, reflecting relative abundances rather than absolute counts (Gloor et al., 2017; Weinroth et al., 2022). Additionally, as a housekeeping gene, 16S rRNA does not directly affect functional capacity or metabolic activity (Weinroth et al., 2022).

Microbial diversity analyses using 16S data typically involve alpha diversity (within-sample richness and evenness) and beta diversity (between-sample dissimilarity). Alpha diversity indices such as Shannon, Pielou’s Evenness, and Chao1 measure species richness and evenness, while beta diversity utilizes distance metrics (e.g., UniFrac, Bray-Curtis) to assess differences in community composition (Callahan et al., 2019; IMPACCT Investigators, 2022). Analytical platforms such as QIIME2, Mothur, and R facilitate these calculations, utilizing PERMANOVA or Kruskal-Wallis tests to determine significance and pairwise differences (Estaki et al., 2020; Bolyen et al., 2019). Taxonomic classification of sequencing reads is performed using machine learning algorithms, such as Naive Bayes classifiers or Random Forest models, against curated reference databases such as SILVA or Greengenes (Bokulich et al., 2018; Yang et al., 2019). Differential abundance analysis is commonly conducted using tools such as ANCOM, DESeq2, and LEfSe. ANCOM employs log-ratio transformations to account for compositionality and control false discovery rates (Mandal et al., 2015), while DESeq2 applies negative binomial models suited for count-based data (Love et al., 2014). LEfSe uses linear discriminant analysis to identify taxa that differ significantly across treatment groups (Segata et al., 2011).

Although often applied for taxonomic surveys, 16S rRNA sequencing also supports biologics discovery by enabling researchers to track candidate probiotic strains, SCFA producers, and other beneficial taxa. Integrating functional prediction tools and downstream validation forms the foundation for precision microbiome engineering. As summarized in Table 4, 16S rRNA gene sequencing continues to enhance understanding of GIT ecology and supports the pipeline for identifying and developing novel microbiome-derived biologics.

Short-read 16S rRNA gene sequencing remains a widely adopted strategy for microbiome profiling due to its affordability, high-throughput capacity, and compatibility with established sequencing platforms and bioinformatics tools. 16S approach typically targets one or more hypervariable regions of the 16S rRNA gene, most commonly the V3 to V4 (∼460 bp) or V4 (∼250 bp) regions, which offer adequate taxonomic resolution for most microbial communities while maintaining manageable sequencing costs and error rates (Klindworth et al., 2013). Illumina platforms such as MiSeq and NovaSeq are commonly used for short-read sequencing. MiSeq, for example, generates 2 × 250 bp paired-end reads that overlap to produce highly accurate consensus sequences (Pollock et al., 2018). While short-read sequencing reliably resolves taxonomy at the genus or family level, it typically lacks the discriminatory power to differentiate closely related species or strains. Moreover, primer selection can introduce amplification biases, as the variable regions differ in taxonomic resolution across microbial lineages (Johnson et al., 2019). Despite these limitations, short-read sequencing remains foundational in microbiome studies, particularly when paired with robust bioinformatics workflows such as QIIME2 and DADA2. These pipelines offer reproducible data processing, denoising, and diversity analysis accessible to a broad research community.

Accurate taxonomic classification of short-read 16S sequences depends heavily on the reference database. The Ribosomal Database Project, SILVA, and Greengenes are the most commonly employed databases, each with specific advantages (Quast et al., 2013; DeSantis et al., 2006; Bokulich et al., 2018). Using a Naive Bayesian classifier, the Ribosomal Database Project (RDP) is optimized for short-read classification. It is fast and efficient, lacks full-length sequences, and is less comprehensive than other options. SILVA is the most phylogenetically curated and up-to-date database. Although SILVA supports short-read and full-length 16S rRNA sequencing and environmental microbiome research, it requires greater computational resources. Although no longer actively updated (last release in 2013), Greengenes remains helpful in working with older datasets and is still integrated with tools such as QIIME and PICRUSt for functional prediction. The study objectives should guide the selection of the appropriate database. As sequencing methods evolve, so must database curation and compatibility with emerging tools to ensure reliable classification and biologics discovery. Numerous studies have leveraged short-read 16S rRNA sequencing to explore GIT microbiota across food-animal species, providing insights into host-microbial interactions, production traits, and potential probiotic candidates (Table 5).

The 16S rRNA gene comprises 9 hypervariable regions (V1 to V9) interspersed with conserved sequences. In short-read amplicon sequencing applications, researchers target subsets of these hypervariable regions depending on the sample matrix and specific research goals. For instance, the V3 to V4 sub-region is recommended when assessing microbial members of the poultry digestive system, given its propensity to classify prokaryotic gut inhabitants and enteric pathogens (Ricke et al, 2017; Feye et al., 2020). Moreover, the V1 to V2 and V3 to V5 sub-regions have demonstrated inferiority in classifying phyla Proteobacteria and Actinobacteria sequences, respectively. At the same time, V1 to V3 well-classified sequences of the genus Escherichia/Shigella, V3 to V5 sequences of Klebsiella, and V6-V9 sequences of Clostridium and Staphylococcus (Johnson et al. 2019). Short-read platforms (e.g., Illumina) are subsequently used to sequence libraries encompassing the selected sub-regions, rendering data that can be matched against reference databases for taxonomic identification and subsequent microbiome exploration (Pollock et al., 2018).

Despite the widespread use of short-read amplicon sequencing (Pollock et al. 2018), multiple limitations can influence study reproducibility and biological inference. Among the most documented shortcomings is achieving taxonomic resolution at the species level, as sequencing 1 or 2 hypervariable regions (approximately 100 to 500 bp; Callahan et al. 2019; Bailén et al. 2020) commonly constrains taxonomic assignment beyond genus due to intraspecific sequence homology (Gupta et al. 2019; Johnson et al. 2019; Usyk et al. 2023). This limitation can present a notable challenge for prokaryotic genera exhibiting symbiotic plasticity, which may encompass mutualistic, commensal, and pathogenic species and subspecies that are difficult to delineate. With the addition of primer bias, these biases can result in over- or underrepresentation of taxon groups, distorting the accurate composition of a microbial community (Wang and Qian 2009; Kumar et al. 2011; Guo et al. 2013). Further limitations of short-read amplicon sequencing include putative uncertainty in taxonomic assignment (Wang et al. 2007; Guo et al. 2013) and the inability to sequence the entire 16S rRNA gene in a single read, as different hypervariable regions offer varying levels of taxonomic resolution (Graspeuntner et al. 2018; Johnson et al. 2019).

Full- and near-full-length 16S rRNA gene sequencing approaches have gained traction in microbiome studies, given the potential to capture all 9 hypervariable regions and thus vastly improve the prokaryotic taxonomic assignment. However, their adoption has been impeded historically by relatively high error rates (∼10%), platform cost, and limited availability (Pollock et al. 2018; Callahan et al. 2019; Callahan et al. 2021). Improvements in sequencing chemistries and error correction algorithms mitigate these challenges, making long-read applications increasingly viable. A comparative summary of short- and long-read sequencing platforms—including technical parameters, resolution, cost, and suitability for microbiome biologics discovery—is provided in Table 6. The following section reviews the primary techniques available for full-length 16S rRNA gene sequencing, emphasizing those commercially available. It further highlights their applications in profiling food-animal-associated microbiomes and addresses the unique considerations necessary for robust long-read amplicon workflows.

Pacific Biosciences (PacBio) sequencing employs single-molecule real-time (SMRT) technology, which enables the generation of highly accurate long reads by sequencing circularized DNA molecules (Wagner et al. 2016). To generate these reads, hairpin adapters (SMRTbells) are ligated to the ends of double-stranded DNA, circularizing the linear DNA molecule. Circularization allows the sequencing polymerase to read the DNA multiple times, generating circular consensus sequences (CCS) (Au et al. 2012; Schloss et al. 2016). While raw PacBio reads can reach lengths exceeding 100 kb, the CCS approach enhances accuracy by producing shorter reads, typically ranging from 1 to 20 kb (∼1,500 bp for full-length 16S rRNA gene applications), with per-base error rates comparable to short-read sequencing, approximately 0.5% (Callahan et al. 2019; Wenger et al. 2019). The method’s accuracy is particularly notable when coupled with analysis in DADA2 software (Callahan et al. 2016), resolving ASVs (Callahan et al. 2017) with single-nucleotide precision (Callahan et al. 2019). Such resolution enables species- and even subspecies-level taxonomic classification, a significant improvement compared to short-read approaches. Thus, the PacBio SMRT technology is becoming increasingly popular for high-resolution profiling of food-animal microbiomes despite its elevated cost relative to short-read and other long-read sequencing platforms (Jeong et al. 2021; Yu et al. 2022).

For instance, PacBio sequencing of the full-length 16S rRNA gene uncovered more rare bacterial species in the dairy cow vaginal microbiome relative to Illumina sequencing of the V4 sub-region, coupled with a higher percentage of classified reads (Souza et al., 2023). Similar findings were realized between the targeted sub-regions/sequencing platforms while characterizing the intestinal microbiota of equine (Di Pietro et al. 2021). Earlier work with the PacBio RSII instrument showed improved taxonomic resolution of the steer ruminal microbiome with V1 to V8 CCS reads compared to V1 to V3 Illumina reads (Myer et al. 2016). Biologically relevant insights have been realized with PacBio full-length 16S rRNA gene sequencing for other animal-associated microbiomes, including those of poultry (Ivulic et al. 2022; Dai et al. 2022), meat sheep (Li et al. 2024), swine (Peng et al. 2023), dairy goats (Hu et al. 2024), meat goats (Luo et al. 2023), and fish (Klemetsen et al. 2019; Sumithra et al. 2024).

Oxford Nanopore Technologies (ONT) sequencing utilizes biological nanopores to sequence native DNA or RNA molecules directly. In this process, single nucleic acid strands pass through a nanopore embedded in a membrane under an electric field. As the strand translocates through the pore, each nucleotide causes characteristic disruptions in the ionic current, which are recorded as electrical signals (Cao et al., 2017). Specialized base-calling algorithms then decode these current shifts to infer the nucleotide sequence (Deamer et al. 2016). The ONT sequencing chemistry produces ultra-long reads (greater than 2 mb), thus making it suitable for sequencing complex sequences such as the full-length 16S rRNA gene or highly repetitive genomic regions (Dumschott et al., 2020).

Oxford Nanopore Technologies has become an attractive option for full-length 16S rRNA gene sequencing due to its on-site accessibility with portable devices (MinION Mk1b, Mk1c, and Flongle), low platform and reagent costs relative to other long-read sequencing technologies, and real-time data analysis capabilities (Winand et al., 2019 and references therein). However, ONT’s higher error rates (5 to 38.5%; Heikema et al., 2020) present a significant limitation, often falling below the 97% and 99% identity thresholds needed for reliable genus- and species-level taxonomic assignments, respectively (Zhang T. et al., 2023). Improvements in ONT sequencing chemistry have increased raw-read accuracy progressively—from approximately 64% with Nanopore R7 (Ashton et al. 2015), approximately 87% with R9 (Minei et al. 2018), and approximately 92% with R9.4.1 (Huang et al. 2021), to approximately 99% with R10.4.1, which has enabled high-quality species-level identification of prokaryotes in environmental samples (Zhang T. et al., 2023). Yet, consensus sequence quality often requires post-sequencing correction for robust taxonomic resolution. For example, Zhang T. et al. (2023) analyzed over 4 million reads generated with R10.4.1 for a commercial standard, finding an average of 96.5% raw-read accuracy, which remains below the ideal genus and species classification thresholds. Moreover, because ONT lacks clustering methods like DADA2, nanopore reads cannot generate ASVs, limiting alpha and beta diversity analyses typically performed in short-read amplicon workflows. Unique molecular identifier (UMI)-based and consensus sequence approaches have been developed to improve sequence quality across both older ONT chemistries (Li et al. 2016; Calus et al. 2018; Volden et al. 2018; Karst et al. 2021) and R10.4+ (Deng et al. 2024; Lin et al. 2024), providing higher-accuracy options for full-length 16S rRNA gene sequencing. However, these methods often require additional instrumentation and bioinformatics resources, making ONT best suited for rapid diagnostics and preliminary microbiome assessment.

Notwithstanding error rate limitations, ONT sequencing has proven valuable for microbiological analyses within food-animal production. In poultry, ONT has been used successfully for on-farm detection of Campylobacter jejuni (Marin et al. 2022), microbiome profiling of gut and fecal environments through both 16S rRNA gene sequencing and shotgun metagenomic approaches (Lundberg et al. 2021; Ndotono et al. 2022; Peng et al. 2023), and for characterizing pathogen antibiotic resistance genes (Peng et al. 2023). Similar applications have advanced the dairy cattle industry, where ONT has enabled 16S rRNA-based milk microbiome profiling (Catozzi et al. 2020; Shinozuka et al. 2021; Urrutia-Angulo et al. 2024), identification of bacteria linked to mastitis (Usui et al. 2023; Urrutia-Angulo et al. 2024), and analysis of antimicrobial resistance genes in Staphylococcus aureus (Chakrawarti et al. 2024). Beyond poultry and dairy, ONT applications have expanded into other food-animal sectors, including the characterization of microbiomes in cultured fish (Toxqui-Rodríguez et al. 2023), beef cattle (Miura et al. 2022), meat and dairy sheep (Reinoso-Peláez et al. 2023), and swine (Chen et al. 2022; Vereecke et al. 2023).

LoopSeq by Element Biosciences is a synthetic long-read (SLR) sequencing chemistry developed to overcome limitations inherent to amplicon-based microbiome profiling. The technology first assigns UMIs to each parent DNA molecule in a sample, which are distributed intramolecularly before enzymatic fragmentation. Following sequencing, short reads that share a UMI are assembled de novo to reconstruct the full-length sequence of the original molecule (up to approximately 6kb; Callahan et al. 2021). LoopSeq’s consensus-driven error correction mechanism renders long reads with a remarkably low error rate of 0.005%, as only dominant read structures within a UMI group contribute to the final consensus sequence (Callahan et al., 2021). Although proprietary aspects of library preparation and SLR reassembly can add complexity to the workflow (Deng et al. 2024), LoopSeq has demonstrated superior sequencing accuracy and taxon identification over other short- and long-read 16S rRNA gene methods across synthetic communities (Chung et al. 2020; Callahan et al. 2021; Deng et al. 2024; Lin et al. 2024), soil (Yu et al. 2022), and human gut microbiomes (Jeong et al. 2021) at a lower per-Mb cost than PacBio CCS (Yu et al. 2022). Coupled with the ability to sequence libraries on accessible short-read instruments, such as the Illumina MiSeq (the most widely used instrument for 16S rRNA gene sequencing; Pollock et al. 2018) and the avidity chemistry-based AVITI platform (Arslan et al. 2024), it is likely that LoopSeq-based 16S rRNA gene sequencing will gain prominence in food-animal microbiome research.

Although relatively new, LoopSeq has already demonstrated value for characterizing microbiomes within food-animal production. In poultry, LoopSeq has been applied to profile the broiler chicken litter microbiome (Gupta et al. 2021) as well as the GIT microbiome following infection by Clostridium perfringens (Fathima et al. 2024) and Campylobacter jejuni (Al Hakeem et al., 2024a; Al Hakeem et al., 2024b). In beef cattle, it has facilitated the identification of bacterial taxa associated with fertility in the uterine microbiome (Walker et al., 2023). The platform has also been employed in dairy, including microbiome assessments of fermented milk (Baldeh et al. 2022) and biosurveillance efforts targeting animal-derived pathogens in food products (Callahan et al. 2021; Grinevich et al. 2024).

Selecting long-read 16S rRNA gene sequencing requires careful evaluation of research goals, sample type, and resource availability. Although this approach offers improved taxonomic resolution - sometimes reaching species or even subspecies levels—it comes with increased demands for DNA input quality and quantity (Tedersoo et al., 2021). These demands can pose significant challenges in food-animal research, where microbial biomass is often low (e.g., reproductive tracts, rinsates, and lavage samples) (Weinroth et al., 2022). Additionally, long-read platforms such as PacBio and Oxford Nanopore remain more expensive than short-read technologies, which can limit their accessibility in large-scale studies. Researchers must weigh the value of higher-resolution taxonomic data against these practical limitations. Short-read sequencing targeting hypervariable regions with higher replication may provide more statistical power and more precise biological insights for studies that detect broad compositional shifts or treatment effects. Conversely, long-read sequencing may be advantageous for specific applications requiring species-level resolution, such as identifying novel probiotic strains, tracing antimicrobial resistance lineages, or profiling microbiome-derived biologics with high taxonomic specificity. Table 4 provides a comparative summary of short- and long-read sequencing platforms, including technical parameters, resolution, cost, and suitability for microbiome biologics discovery.

Long-read sequencing technologies have spurred the development of bioinformatic pipelines capable of processing full-length 16S rRNA gene sequences. However, most reference databases used for taxonomic classification, such as SILVA, RDP, and Greengenes, were initially optimized for short-read data and often lack the resolution or completeness required for full-length reads (Ciuffreda et al., 2021; Tedersoo et al., 2021). This limitation was evident in recent studies where PacBio long-read sequencing identified greater bacterial richness in equine and bovine samples but still failed to consistently classify taxa at the species level (Di Pietro et al., 2021; Souza et al., 2023). These challenges underscore the need for system-specific, full-length reference databases. For example, DAIRYdb significantly outperformed general-purpose databases in resolving dairy-associated microorganisms (Meola et al., 2019), and custom-built databases incorporating all 16S alleles have enabled subspecies-level identification of key pathogens such as Escherichia coli and Salmonella enterica (Grinevich et al., 2024). Recent efforts to address this gap have resulted in the development of curated, full-length databases for broader applications across livestock and environmental microbiomes (Dueholm et al., 2022; Escapa et al., 2020; Graf et al., 2021; Seol et al., 2022; Krabberød et al., 2024; Walsh et al., 2024). These resources are essential for maximizing the taxonomic and functional insights gained from long-read sequencing platforms and enabling precise detection of biologically relevant taxa.

While the technical challenges of long-read sequencing are real, they must be weighed against the growing demand for precision in microbiome applications. In the context of biologics discovery, long-read sequencing enables more accurate tracking of probiotic strains, identification of novel antimicrobial producers, and differentiation of functionally distinct microbial lineages. This level of resolution can inform feed additive formulation, pathogen diagnostics, and microbial therapeutics in food-animal systems. Choosing between short- and long-read sequencing depends on study objectives, available resources, and desired taxonomic resolution. By combining sequencing strategy with system-specific database development and clear application goals, researchers can unlock the full potential of microbiome data to advance sustainable animal production, health, and food safety.

While 16S rRNA gene sequencing provides critical taxonomic information, it lacks insight into microbial function. Bioinformatic tools such as PICRUSt2 and Tax4Fun offer statistical predictions of functional potential. However, they heavily rely on available reference genomes and may miss underrepresented or novel taxa (Aßhauer et al., 2015; Douglas et al., 2020). To move beyond inference, researchers are increasingly integrating complementary -omics approaches—metagenomics, metatranscriptomics, metaproteomics, and metabolomics—to characterize the functional and metabolic activity of the microbiome in more detail.

Holo-omics are multi-omics strategies that aim to uncover host-microbiome interactions and support precision interventions in livestock and poultry (Nyholm et al., 2020; Dehau et al., 2022). Metagenomics enables comprehensive analysis of microbial genetic potential, including biosynthetic pathways, resistance genes, and enzymes relevant to animal health and nutrition. However, it does not capture gene expression or actual metabolic activity, necessitating integration with 1) metatranscriptomics, which reveals active gene expression under specific conditions (Khodadadian et al., 2020). 2) Metaproteomics, which quantifies the proteins produced by microbial communities (Andersen et al., 2021; Karaduta et al., 2021). 3) Metabolomics, which profiles small molecules and metabolic intermediates to illuminate real-time host-microorganism interactions and biochemical states (Goldansaz et al., 2017; Chatman et al., 2024). Large-scale integration of microbiome data with host genomics, transcriptomics, and metabolic phenotypes can enable researchers to identify candidate microbial strains for probiotic development, link specific microbial metabolites to improved performance traits, and guide the design of microbiome-informed feed formulations and therapies.

A high level of resolution is essential for translating microbiome data into actionable tools for animal production. Correlating functional microbial signatures with desirable production outcomes, multi-omics approaches can pave the way for next-generation biologics tailored to specific species, production systems, and health challenges. Future studies should prioritize cross-disciplinary data integration, investment in functional validation platforms, and the development of curated multi-omics databases specific to food-animal systems.