Taxonomic classification and abundance estimation using 16S and WGS-A comparison using controlled reference samples

Exploring the diversity and functional profile of microbial communities of Brazilian soils with high salinity and oil contamination

Environmental pollution associated with the petroleum industry is a major problem worldwide. Microbial degradation is extremely important whether in the extractive process or in bioremediation of contaminants. Assessing the local microbiota and its potential for degradation is crucial for implementing effective bioremediation strategies. Herein, contaminated soil samples of onshore oil fields from a semiarid region in the Northeast of Brazil were investigated using metagenomics and metataxonomics. These soils exhibited hydrocarbon contamination and high salinity indices, while a control sample was collected from an uncontaminated area. The shotgun analysis revealed the predominance of Actinomycetota and Pseudomonadota, while 16S rRNA gene amplicon analysis of the samples showed Actinomycetota, Bacillota, and Pseudomonadota as the most abundant. The Archaea domain phylotypes were assigned to Thermoproteota and Methanobacteriota. Functional analysis and metabolic profile of the soil microbiomes exhibited a broader metabolic repertoire in the uncontaminated soil, while degradation pathways and surfactant biosynthesis presented higher values in the contaminated soils, where degradation pathways of xenobiotic and aromatic compounds were also present. Biosurfactant synthetic pathways were abundant, with predominance of lipopeptides. The present work uncovers several microbial drivers of oil degradation and mechanisms of adaptation to high salinity, which are pivotal traits for sustainable soil recovery strategies.

A metagenome-wide association study of gut microbiome in patients with AMD, ASD, RA, T2D & VKH diseases

Clinical studies have already illustrated the associations between gut microbes and diseases, yet fundamental questions remain unclear that how we can universalize this knowledge. Considering the important role of human gut microbial composition in maintaining overall health, it is important to understand the microbial diversity and altered disease conditions of the human gut. Metagenomics provides a way to analyze and understand the microbes and their role in a community manner. It provides qualitative as well as quantitative measurements, in terms of relative abundance. Various studies are already going on to find out the association between microbes and diseases; still, the mined knowledge is limited. Considering the current scenario, using the targeted metagenomics approach, we analyzed the gut microbiome of 99 samples from healthy and diseased individuals. Our metagenomic analysis mainly targeted five diseased microbiomes (i.e., Age-related macular degeneration, Autism spectrum disorder, Rheumatoid arthritis, Type 2 diabetes and Vogt-Koyanagi harada), with compare to healthy microbiome, and reported disease-associated microbiome shift in different conditions.

Using meta-analysis to understand the impacts of dietary protein and fat content on the composition of fecal microbiota of domestic dogs (Canis lupus familiaris): A pilot study

The interplay between diet and fecal microbiota composition is garnering increased interest across various host species, including domestic dogs. While the influence of dietary macronutrients and their associated microbial communities have been extensively reviewed, these reviews are descriptive and do not account for differences in microbial community analysis, nor do they standardize macronutrient content across studies. To address this, a meta-analysis was performed to assess the impact of dietary crude protein ("protein") and dietary crude fat ("fat") on the fecal microbiota composition in healthy dogs. Sixteen publications met the eligibility criteria for the meta-analysis, yielding a final data set of 314 dogs. Diets were classed as low, moderate, high, or supra in terms of protein or fat content. Sequence data from each publication were retrieved from public databases and reanalyzed using consistent bioinformatic pipelines. Analysis of community diversity indices and unsupervised clustering of the data with principal coordinate analysis revealed a small effect size and complete overlap between protein and fat levels at the overall community level. Supervised clustering through random forest analysis and partial least squares-discriminant analysis indicated alterations in the fecal microbiota composition at a more individual taxonomic level, corresponding to the levels of protein or fat. The Prevotellaceae Ga6A1 group and Enterococcus were associated with increasing levels of protein, while Allobaculum and Clostridium sensu stricto 13 were associated with increasing levels of fat. Interestingly, the random forest analyses revealed that Sharpea, despite its low relative abundance in the dog's fecal microbiome, was primarily responsible for the separation of the microbiome for both protein and fat. Future research should focus on validating and understanding the functional roles of these relatively low-abundant genera.

Deciphering the role of female reproductive tract microbiome in reproductive health: a review

Relevant studies increasingly indicate that female reproductive health is confronted with substantial challenges. Emerging research has revealed that the microbiome interacts with the anatomy, histology, and immunity of the female reproductive tract, which are the cornerstone of maintaining female reproductive health and preventing adverse pregnancy outcomes. Currently, the precise mechanisms underlying their interaction and impact on physiological functions of the reproductive tract remain elusive, constituting a prominent area of investigation within the field of female reproductive tract microecology. From this new perspective, we explore the mechanisms of interactions between the microbiome and the anatomy, histology, and immunity of the female reproductive tract, factors that affect the composition of the microbiome in the female reproductive tract, as well as personalized medicine approaches in managing female reproductive tract health based on the microbiome. This study highlights the pivotal role of the female reproductive tract microbiome in maintaining reproductive health and influencing the occurrence of reproductive tract diseases. These findings support the exploration of innovative approaches for the prevention, monitoring and treatment of female reproductive tract diseases based on the microbiome.

Insights into gut microbiomes in stem cell transplantation by comprehensive shotgun long-read sequencing

The gut microbiome is a diverse ecosystem, dominated by bacteria; however, fungi, phages/viruses, archaea, and protozoa are also important members of the gut microbiota. Exploration of taxonomic compositions beyond bacteria as well as an understanding of the interaction between the bacteriome with the other members is limited using 16S rDNA sequencing. Here, we developed a pipeline enabling the simultaneous interrogation of the gut microbiome (bacteriome, mycobiome, archaeome, eukaryome, DNA virome) and of antibiotic resistance genes based on optimized long-read shotgun metagenomics protocols and custom bioinformatics. Using our pipeline we investigated the longitudinal composition of the gut microbiome in an exploratory clinical study in patients undergoing allogeneic hematopoietic stem cell transplantation (alloHSCT; n = 31). Pre-transplantation microbiomes exhibited a 3-cluster structure, characterized by Bacteroides spp. /Phocaeicola spp., mixed composition and Enterococcus abundances. We revealed substantial inter-individual and temporal variabilities of microbial domain compositions, human DNA, and antibiotic resistance genes during the course of alloHSCT. Interestingly, viruses and fungi accounted for substantial proportions of microbiome content in individual samples. In the course of HSCT, bacterial strains were stable or newly acquired. Our results demonstrate the disruptive potential of alloHSCTon the gut microbiome and pave the way for future comprehensive microbiome studies based on long-read metagenomics.

Rumen multi-omics addressing diet-host-microbiome interplay in farm animals: a review

Continuous improvement in the living standards of developing countries, calls for an urgent need of high quality meat and dairy products. The farm animals have a micro-ecosystem in gastro-intestinal tract, comprising of a wide variety of flora and fauna which converts roughages and agricultural byproducts as well as nutrient rich concentrate sources into the useful products such as volatile fatty acids and microbial crude proteins. The microbial diversity changes according to composition of the feed, host species/breed and host's individual genetic makeup. From culture methods to next-generation sequencing technologies, the knowledge has emerged a lot to know-how of microbial world viz. their identification, enzymatic activities and metabolites which are the keys of ruminant's successful existence. The structural composition of ruminal community revealed through metagenomics can be elaborated by metatranscriptomics and metabolomics through deciphering their functional role in metabolism and their responses to the external and internal stimuli. These highly sophisticated analytical tools have made possible to correlate the differences in the feed efficiency, nutrients utilization and methane emissions to their rumen microbiome. The comprehensively understood rumen microbiome will enhance the knowledge in the fields of animal nutrition, biotechnology and climatology through deciphering the significance of each and every domain of residing microbial entity. The present review undertakes the recent investigations regarding rumen multi-omics viz. taxonomic and functional potential of microbial populations, host-diet-microbiome interactions and correlation with metabolic dynamics.

Machine learning approaches in microbiome research: challenges and best practices

Microbiome data predictive analysis within a machine learning (ML) workflow presents numerous domain-specific challenges involving preprocessing, feature selection, predictive modeling, performance estimation, model interpretation, and the extraction of biological information from the results. To assist decision-making, we offer a set of recommendations on algorithm selection, pipeline creation and evaluation, stemming from the COST Action ML4Microbiome. We compared the suggested approaches on a multi-cohort shotgun metagenomics dataset of colorectal cancer patients, focusing on their performance in disease diagnosis and biomarker discovery. It is demonstrated that the use of compositional transformations and filtering methods as part of data preprocessing does not always improve the predictive performance of a model. In contrast, the multivariate feature selection, such as the Statistically Equivalent Signatures algorithm, was effective in reducing the classification error. When validated on a separate test dataset, this algorithm in combination with random forest modeling, provided the most accurate performance estimates. Lastly, we showed how linear modeling by logistic regression coupled with visualization techniques such as Individual Conditional Expectation (ICE) plots can yield interpretable results and offer biological insights. These findings are significant for clinicians and non-experts alike in translational applications.

The virtual microbiome: A computational framework to evaluate microbiome analyses

Microbiomes have been the focus of a substantial research effort in the last decades. The composition of microbial populations is normally determined by comparing DNA sequences sampled from those populations with the sequences stored in genomic databases. Therefore, the amount of information available in databanks should be expected to constrain the accuracy of microbiome analyses. Albeit normally ignored in microbiome studies, this constraint could severely compromise the reliability of microbiome data. To test this hypothesis, we generated virtual bacterial populations that exhibit the ecological structure of real-world microbiomes. Confronting the analyses of virtual microbiomes with their original composition revealed critical issues in the current approach to characterizing microbiomes, issues that were empirically confirmed by analyzing the microbiome of Galleria mellonella larvae. To reduce the uncertainty of microbiome data, the effort in the field must be channeled towards significantly increasing the amount of available genomic information and optimizing the use of this information.

Both Disease Activity and HLA-B27 Status Are Associated With Gut Microbiome Dysbiosis in Spondyloarthritis Patients

OBJECTIVE

Gut microbiome dysbiosis has previously been reported in spondyloarthritis (SpA) patients and could be critically involved in the pathogenesis of this disorder. The objectives of this study were to further characterize the microbiota structure in SpA patients and to investigate the relationship between dysbiosis and disease activity in light of the putative influence of the genetic background.

METHODS

Shotgun sequencing was performed on fecal DNA isolated from stool samples from 2 groups of adult volunteers: SpA patients (n = 102) and healthy controls (n = 63). A subset of the healthy controls comprised the age-matched siblings of patients whose HLA-B27 status was known. Changes in gut microbiota composition were assessed based on species diversity, enterotypes, and taxonomic and functional differences.

RESULTS

Dysbiosis was confirmed in SpA patients as compared to healthy controls. The restriction of microbiota diversity was detected in patients with the most active disease, and the abundance of several bacterial species was correlated with Bath Ankylosing Spondylitis Disease Activity Index score. Among healthy controls, significant differences in microbiota composition were also detected between the HLA-B27-positive and the HLA-B27-negative siblings of SpA patients. We highlighted a decreased abundance of several species of bacteria in SpA patients, especially those bacteria belonging to the Clostridiales order. Among the few species of bacteria showing increased abundance, Ruminococcus gnavus was one of the top differentiating species.

CONCLUSION

These findings reveal that genetic background and level of disease activity are likely to influence the composition of the gut microbiota of patients with SpA. It may be appropriate for further research on chronic arthritis to focus on these key parameters.

Wochenende — modular and flexible alignment-based shotgun metagenome analysis

Background

Shotgun metagenome analysis provides a robust and verifiable method for comprehensive microbiome analysis of fungal, viral, archaeal and bacterial taxonomy, particularly with regard to visualization of read mapping location, normalization options, growth dynamics and functional gene repertoires. Current read classification tools use non-standard output formats, or do not fully show information on mapping location. As reference datasets are not perfect, portrayal of mapping information is critical for judging results effectively.

Results

Our alignment-based pipeline, Wochenende, incorporates flexible quality control, trimming, mapping, various filters and normalization. Results are completely transparent and filters can be adjusted by the user. We observe stringent filtering of mismatches and use of mapping quality sharply reduces the number of false positives. Further modules allow genomic visualization and the calculation of growth rates, as well as integration and subsequent plotting of pipeline results as heatmaps or heat trees. Our novel normalization approach additionally allows calculation of absolute abundance profiles by comparison with reads assigned to the human host genome.

Conclusion

Wochenende has the ability to find and filter alignments to all kingdoms of life using both short and long reads, and requires only good quality reference genomes. Wochenende automatically combines multiple available modules ranging from quality control and normalization to taxonomic visualization. Wochenende is available at https://github.com/MHH-RCUG/nf_wochenende.

Whole Genome Analyses Accurately Identify Neisseria spp. and Limit Taxonomic Ambiguity

Genome sequencing facilitates the study of bacterial taxonomy and allows the re-evaluation of the taxonomic relationships between species. Here, we aimed to analyze the draft genomes of four commensal Neisseria clinical isolates from the semen of infertile Lebanese men. To determine the phylogenetic relationships among these strains and other Neisseria spp. and to confirm their identity at the genomic level, we compared the genomes of these four isolates with the complete genome sequences of Neisseria gonorrhoeae and Neisseria meningitidis and the draft genomes of Neisseria flavescens, Neisseria perflava, Neisseria mucosa, and Neisseria macacae that are available in the NCBI Genbank database. Our findings revealed that the WGS analysis accurately identified and corroborated the matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) species identities of the Neisseria isolates. The combination of three well-established genome-based taxonomic tools (in silico DNA-DNA Hybridization, Ortho Average Nucleotide identity, and pangenomic studies) proved to be relatively the best identification approach. Notably, we also discovered that some Neisseria strains that are deposited in databases contain many taxonomical errors. The latter is very important and must be addressed to prevent misdiagnosis and missing emerging etiologies. We also highlight the need for robust cut-offs to delineate the species using genomic tools.

Greater functional diversity and redundancy of coral endolithic microbiomes align with lower coral bleaching susceptibility

The skeleton of reef-building coral harbors diverse microbial communities that could compensate for metabolic deficiencies caused by the loss of algal endosymbionts, i.e., coral bleaching. However, it is unknown to what extent endolith taxonomic diversity and functional potential might contribute to thermal resilience. Here we exposed Goniastrea edwardsi and Porites lutea, two common reef-building corals from the central Red Sea to a 17-day long heat stress. Using hyperspectral imaging, marker gene/metagenomic sequencing, and NanoSIMS, we characterized their endolithic microbiomes together with ¹⁵N and ¹³C assimilation of two skeletal compartments: the endolithic band directly below the coral tissue and the deep skeleton. The bleaching-resistant G. edwardsi was associated with endolithic microbiomes of greater functional diversity and redundancy that exhibited lower N and C assimilation than endoliths in the bleaching-sensitive P. lutea. We propose that the lower endolithic primary productivity in G. edwardsi can be attributed to the dominance of chemolithotrophs. Lower primary production within the skeleton may prevent unbalanced nutrient fluxes to coral tissues under heat stress, thereby preserving nutrient-limiting conditions characteristic of a stable coral-algal symbiosis. Our findings link coral endolithic microbiome structure and function to bleaching susceptibility, providing new avenues for understanding and eventually mitigating reef loss.

RiboTaxa: combined approaches for rRNA genes taxonomic resolution down to the species level from metagenomics data revealing novelties

Metagenomic classifiers are widely used for the taxonomic profiling of metagenomics data and estimation of taxa relative abundance. Small subunit rRNA genes are a gold standard for phylogenetic resolution of microbiota, although the power of this marker comes down to its use as full-length. We aimed at identifying the tools that can efficiently lead to taxonomic resolution down to the species level. To reach this goal, we benchmarked the performance and accuracy of rRNA-specialized versus general-purpose read mappers, reference-targeted assemblers and taxonomic classifiers. We then compiled the best tools (BBTools, FastQC, SortMeRNA, MetaRib, EMIRGE, VSEARCH, BBMap and QIIME 2’s Sklearn classifier) to build a pipeline called RiboTaxa. Using metagenomics datasets, RiboTaxa gave the best results compared to other tools (i.e. Kraken2, Centrifuge, METAXA2, phyloFlash, SPINGO, BLCA, MEGAN) with precise taxonomic identification and relative abundance description without false positive detection (F-measure of 100% and 83.7% at genus level and species level, respectively). Using real datasets from various environments (i.e. ocean, soil, human gut) and from different approaches (e.g. metagenomics and gene capture by hybridization), RiboTaxa revealed microbial novelties not discerned by current bioinformatics analysis opening new biological perspectives in human and environmental health.

Comparative Evaluation of the Salivary and Buccal Mucosal Microbiota by 16S rRNA Sequencing for Forensic Investigations

The human microbiome has emerged as a new potential biomarker for forensic investigations with the development of high-throughput sequencing and bioinformatic analysis during the last decade. The oral cavity has many different microbial habitats, with each habit colonized by specific and individualized microbiota. As saliva and buccal mucosa are common biological evidence in forensic science, understanding the differences of microbial communities between the two is important for forensic original identification. Moreover, the oral microbiota is individualized, whereas there are few studies on the application of forensic personal identification that need to be supplemented. In this study, Streptococcus was the most abundant genus, with an average relative abundance of 49.61% in the buccal mucosa, while in the saliva, Streptococcus, Veillonella, and Neisseria had similar proportions (20%, 15%, 16%) and were the dominant genera. The α and β diversity displayed a significant distinctness between the saliva and buccal mucosal groups. The community assembly mechanism stated that the deterministic process played a more significant effect in shaping the salivary bacterial community assembly than buccal mucosa, which explained the microbial differences. Of the test samples, 93.3% can be correctly classified with the random forest model based on the microbial differences. Targeting the low-abundance bacteria at the species level, 52% of experimental participants could be discriminated by using the observed unique bacterial species. In conclusion, the salivary bacterial community composition differed from that of the buccal mucosa and showed high richness and diversity. With the random forest model, the microbiota of saliva and buccal mucosa can be classified, which can be used in identifying the source of oral biological trace. Furthermore, each individual has a unique bacterial community pattern, and the presence or absence of unique bacteria and differences in the composition of the core oral microbiota are the key points for forensic personal discrimination that supplement the study of oral microbial application to forensic personal discrimination. Whether for original identification or personal discrimination, the oral microbiome has great potential for application.

Can moderate heavy metal soil contaminations due to cement production influence the surrounding soil bacterial communities?

Events of soil contamination by heavy metals are mostly related to human activities that release these metals into the environment as emissions or effluents. Among the industrial activities related to heavy metal pollution, cement production plants are considered one of the most common sources. In this work we applied the High-throughput sequencing approach called 16 S rDNA metabarcoding to perform the taxonomic characterization of the prokaryotic communities of the soil surrounding three cement plants as well as two areas outside the influence of the cement plants that represented agricultural production environments free of heavy metal contamination (control areas). We applied the environmental genomics approaches known as "structural community metrics" (α- and β-diversity metrics) and "functional community metrics" (PICRUSt2 approach) to verify whether or not the effects of heavy metal contamination in the study area generated impacts on soil bacterial communities. We found that the impact related to the elevation of heavy metal concentration due to the operation of cement plants in the surrounding soil can be considered smooth according to globally recognized indices such as Igeo. However, we identified that both the taxonomic and functional structures of the communities surrounding cement plants were different from those found in the control areas. We consider that our findings contribute significantly to the general understanding of the effects of heavy metals on the soil ecosystem by showing that light contamination can disturb the dynamics of ecosystem services provided by soil, specifically those associated with microbial metabolism.

Meta-Ribosomalomics: RNA Sequencing Is an Unbiased Method for Parasite Detection of Different Sample Types

In this perspective article, we review the past use of ribosomal sequences to address scientific and diagnostic questions. We highlight a variety of sequencing approaches including metagenomics and DNA barcoding and their different demands and requirements. Meta-ribosomalomics is introduced as an unbiased approach to exploit high-throughput sequencing datasets for eukaryotic and prokaryotic ribosomal sequences. Prerequisites, benefits, drawbacks, and future perspectives are elaborated and compared to other sequencing approaches.

Shotgun metagenomic analysis reveals new insights into bacterial community profiles in tempeh

OBJECTIVE

Amplicon sequencing targeting 16S ribosomal RNA (rRNA) has been widely used to profile the microbial community from fermented food samples. However, polymerase chain reaction (PCR) steps on amplicon sequencing analysis and intragenomic heterogeneity within 16S rRNA are believed to contribute to bias in estimating microbial community composition. As potential paraprobiotics sources, a comprehensive profiling study of tempeh microbial ecology could contribute to tempeh product development. This study employed a shotgun metagenomic approach, where metagenome fragments from tempeh samples were sequenced directly for taxonomic and functional profiling analysis.

RESULTS

Taxonomic profiling showed that Proteobacteria, Firmicutes, and Bacteroidetes were the dominant phyla from the shotgun metagenomic analysis in all tempeh samples. In terms of composition, this shotgun metagenomic study revealed that Proteobacteria was the most abundant phylum. Functional profiling showed that iron complex outer-membrane recepter protein (KEGG ID: K02014) was the most transcribed gene based on this metagenomic analysis. The metagenome-assembled genomes (MAGs) results from the binning pipeline could reveal almost complete whole genome sequence of Lactobacillus fermentum, Enterococcus cecorum, Escherichia coli, Klebsiella pneumoniae, and Acinetobacter baumannii.

Data-Driven Modeling for Species-Level Taxonomic Assignment From 16S rRNA: Application to Human Microbiomes

With the emergence of next-generation sequencing (NGS) technology, there have been a large number of metagenomic studies that estimated the bacterial composition via 16S ribosomal RNA (16S rRNA) amplicon sequencing. In particular, subsets of the hypervariable regions in 16S rRNA, such as V1-V2 and V3-V4, are targeted using high-throughput sequencing. The sequences from different taxa are assigned to a specific taxon based on the sequence homology. Since such sequences are highly homologous or identical between species in the same genus, it is challenging to determine the exact species using 16S rRNA sequences only. Therefore, in this study, homologous species groups were defined to obtain maximum resolution related with species using 16S rRNA. For the taxonomic assignment using 16S rRNA, three major 16S rRNA databases are independently used since the lineage of certain bacteria is not consistent among these databases. On the basis of the NCBI taxonomy classification, we re-annotated inconsistent lineage information in three major 16S rRNA databases. For each species, we constructed a consensus sequence model for each hypervariable region and determined homologous species groups that consist of indistinguishable species in terms of sequence homology. Using a k-nearest neighbor method and the species consensus sequence models, the species-level taxonomy was determined. If the species determined is a member of homologous species groups, the species group is assigned instead of a specific species. Notably, the results of the evaluation on our method using simulated and mock datasets showed a high correlation with the real bacterial composition. Furthermore, in the analysis of real microbiome samples, such as salivary and gut microbiome samples, our method successfully performed species-level profiling and identified differences in the bacterial composition between different phenotypic groups.

Interpretations of Environmental Microbial Community Studies Are Biased by the Selected 16S rRNA (Gene) Amplicon Sequencing Pipeline

One of the major methods to identify microbial community composition, to unravel microbial population dynamics, and to explore microbial diversity in environmental samples is high-throughput DNA- or RNA-based 16S rRNA (gene) amplicon sequencing in combination with bioinformatics analyses. However, focusing on environmental samples from contrasting habitats, it was not systematically evaluated (i) which analysis methods provide results that reflect reality most accurately, (ii) how the interpretations of microbial community studies are biased by different analysis methods and (iii) if the most optimal analysis workflow can be implemented in an easy-to-use pipeline. Here, we compared the performance of 16S rRNA (gene) amplicon sequencing analysis tools (i.e., Mothur, QIIME1, QIIME2, and MEGAN) using three mock datasets with known microbial community composition that differed in sequencing quality, species number and abundance distribution (i.e., even or uneven), and phylogenetic diversity (i.e., closely related or well-separated amplicon sequences). Our results showed that QIIME2 outcompeted all other investigated tools in sequence recovery (>10 times fewer false positives), taxonomic assignments (>22% better F-score) and diversity estimates (>5% better assessment), suggesting that this approach is able to reflect the in situ microbial community most accurately. Further analysis of 24 environmental datasets obtained from four contrasting terrestrial and freshwater sites revealed dramatic differences in the resulting microbial community composition for all pipelines at genus level. For instance, at the investigated river water sites Sphaerotilus was only reported when using QIIME1 (8% abundance) and Agitococcus with QIIME1 or QIIME2 (2 or 3% abundance, respectively), but both genera remained undetected when analyzed with Mothur or MEGAN. Since these abundant taxa probably have implications for important biogeochemical cycles (e.g., nitrate and sulfate reduction) at these sites, their detection and semi-quantitative enumeration is crucial for valid interpretations. A high-performance computing conformant workflow was constructed to allow FAIR (Findable, Accessible, Interoperable, and Re-usable) 16S rRNA (gene) amplicon sequence analysis starting from raw sequence files, using the most optimal methods identified in our study. Our presented workflow should be considered for future studies, thereby facilitating the analysis of high-throughput 16S rRNA (gene) sequencing data substantially, while maximizing reliability and confidence in microbial community data analysis.