Analyzing human gut microbiome data from global populations: challenges and resources.

Analyzing human gut microbiome data from global populations: challenges and resources - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Trends Microbiol . Author manuscript; available in PMC: 2026 Jun 6. Published in final edited form as: Trends Microbiol. 2025 Jun 6;33(11):1212–1223. doi: 10.1016/j.tim.2025.05.008 Analyzing human gut microbiome data from global populations: challenges and resources Sabrina J Arif 1, * , Samantha P Graham 2, # , Richard J Abdill 1, # , Ran Blekhman 1, * Author information Article notes Copyright and License information 1 Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA, 2 Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, Minnesota, USA # Authors contributed equally * Correspondence: sjarif@uchicago.edu (Sabrina J Arif), blekhman@uchicago.edu (Ran Blekhman). Issue date 2025 Nov. PMC Copyright notice PMCID: PMC12353970  NIHMSID: NIHMS2083356  PMID: 40483188 The publisher's version of this article is available at Trends Microbiol Abstract Research on the human gut microbiome is expanding rapidly, yet most published studies focus on populations from high-income countries such as North America and Europe. Underrepresentation of populations from low- and middle-income countries in the microbiome literature limits the generalizability of microbiome–health associations. These challenges are compounded by computational barriers, including biases in reference databases, non-representative metadata, and infrastructure limitations in low- and middle-income countries. However, recent efforts in large-scale global sampling have begun to address these problems. This review provides recommendations for future research efforts applying computational analysis to global microbiome data, including guidelines to initiate and maintain equitable partnerships, identify representative datasets, overcome technical limitations, and contextualize results at the global scale. Keywords: Microbiome, global health, global microbiome diversity, reference genomes, next-generation sequencing, research equity Representation biases in gut microbiome literature The gut microbiome is the collection of trillions of bacteria, viruses, fungi, small eukaryotes and archaea that inhabit the gastrointestinal tract and play important roles in host health and disease. In health, gut organisms break down food, produce essential vitamins and metabolites, and protect against pathogens [ 1 , 2 ]. Disruptions in the gut microbiome have been linked to various health conditions, including inflammatory bowel disease, obesity, diabetes, and even neurodegenerative disorders [ 3 , 4 ]. Accordingly, characterizing and explaining variation in microbiome composition is crucial for achieving a more comprehensive understanding of human health. Gut microbiome composition is shaped by environmental, dietary, and host genetic factors [ 5 – 7 ]. Geography is a key driver of variation, influencing microbiome composition through factors such as industrialization, socioeconomic status, diet, and ethnicity [ 8 – 11 ]. A comprehensive metagenomic analysis of more than 9,000 human microbiome samples revealed that low- and middle-income (LMIC) (non-North American, non-European) populations harbor a large fraction of previously unknown species [ 12 ]. Similarly, Almeida and colleagues generated a catalog of reference genomes from human gut microbiomes and found that genomes from African populations contribute over three times more variation than European or North American genomes, with especially high strain variability between continents [ 13 ]. These findings have broad implications for human health. For example, Segatella copri ( Prevotella copri ), a species that is found abundantly in LMI nations but not high-income (HI) nations, displays impressive functional diversity across distinct clades [ 14 ] and has conflicting roles in insulin tolerance, a discrepancy that may be driven by diet [ 15 , 16 ]. Taxa that produce butyrate and other metabolites that regulate gut homeostasis are likewise found in greater abundance in LMI nations [ 11 , 17 ]. Although the value of studying microbiomes from diverse populations is gaining wider recognition, HI nations remain overrepresented in microbiome literature relative to their population sizes. One of the largest publicly available databases of gut microbiome data, the Sequence Read Archive, is dominated by sequences from Europe, the United States, and Canada. In contrast, Eastern Asia, Southern Asia, and Sub-Saharan Africa have strikingly low contributions to this database relative to their population sizes [ 18 ]. This trend is slow to change: while the number of samples in the Sequence Read Archive has grown exponentially in recent years (i.e., 487,626 in 2022, 607,732 in 2023, and 768,125 in 2024), the proportion of samples sourced from Europe and North America has remained relatively constant over the past decade, comprising approximately 50% of the database in 2024 ( Figure 1A – B ). Accordingly, studies of disease–microbiome associations from HI nations focus on conditions that predominantly affect North America and Europe, such as inflammatory bowel disease, diabetes, and obesity. These conditions are also rising in LMI nations amidst rapid urbanization, however, differences in diet and lifestyle amongst HI and LMI cohorts may limit applicability of associations between populations [ 19 , 20 ]. Further, LMI nations experience distinct health challenges that may be shaped by gut microbiome characteristics [ 21 , 22 ] that cannot be effectively modeled with existing bioinformatic data at the scale of HI nation health challenges.. FIGURE 1. Open in a new tab Global representation in INSDC databases. (A) Line plot illustrating the cumulative samples available at the end of each year in the dataset. The x-axis represents the year, and the y-axis illustrates total samples. Each line represents the cumulative sample count from a single world region, as indicated by line color. The inset figure illustrates the same data using a log scale on the y-axis. (B) An illustration of the proportional share of all samples at the end of each year in the dataset. Data based on a search of the NCBI BioSample repository ( https://www.ncbi.nlm.nih.gov/biosample ) on 25 Feb 2025 for all publicly accessible samples deposited in the “human gut metagenome” category associated with a sample in the databases maintained by the International Nucleotide Sequence Database Collaboration (INSDC), made up of governmental research organizations in the United States, Japan, and Europe. Region of origin was assigned using manual annotation of the “geo_loc_name” metadata field, as described previously [ 18 ]. These geographic and demographic biases call into question how applicable the results of microbiome studies are to world populations. Gut microbiome research thus joins a growing number of fields in which the challenge of generalizing findings — such as polygenic risk scores derived from predominantly European cohorts — has become evident [ 23 , 24 ]. Importantly, several gut organisms identified as important for human health in North America and Europe do not have a strong presence in LMI nations [ 25 ]. Similarly, researchers have reported that geography and ethnicity confound associations of microbiome composition and metabolic disease [ 9 , 26 , 27 ]. For example, in a study utilizing microbiomes of Chinese participants from the Guangdong Gut Microbiome Project, researchers found only weak associations between gut microbiome composition and metabolic disease when geography was not considered [ 28 ]. These findings highlight that microbiome-based biomarkers may fail to translate effectively across regions, and that narrowing down the universal signifiers of health and disease requires broader representation in research. Thus, expanding the diversity of gut microbiome literature is essential for understanding associations of microbiomes and host health. Several recent studies describing gut microbiomes from underrepresented nations have moved this field forward [ 29 – 31 ]. Even in the absence of comprehensive metadata, global gut microbiome sequences can still be leveraged to quantify broad differences in microbiome composition across populations, contributing to our understanding of microbial biogeography and diversity. In aggregate, large-scale datasets can also serve as compendia for machine learning approaches, enabling discovery of patterns that would be impossible to detect in smaller or more homogeneous datasets [ 25 ]. However, expanding global data collection is only part of the challenge — translating this data into insights relevant to human health also requires computational analysis, which presents its own challenges in the context of the global microbiome. Potential hurdles include incomplete reference base sources, ill-fitting taxonomic classifiers, and non-representative metadata. Some of these issues arise from biases in reference databases, whereas others arise from intrinsic properties of microbiomes from LMI countries. For example, Nayfach et al. [ 32 ] described how assembly of metagenome-assembled genomes (MAGs) is particularly challenging in diverse gut communities. While this and similar studies have highlighted individual computational challenges associated with analyzing globally diverse microbiomes, to our knowledge, no comprehensive review has yet synthesized these issues or proposed cohesive solutions. As microbiome research expands into underrepresented regions, such a guide is urgently needed to address these barriers and ensure equitable, accurate insights into global human health. In this review, we discuss computational issues in analyzing global microbiome data in detail. We also describe solutions to some of these challenges and provide guidelines to improve the accuracy and inclusivity of gut microbiome research. Difficulties in computational analysis of diverse human microbiome data Reference database limitations The most fundamental challenge in analysis of microbiome data from underrepresented populations lies in the bias of taxonomic reference databases toward HI populations. The substantial underrepresentation of LMI microbial diversity creates cascading challenges throughout standard computational pipelines, affecting every step of microbiome analysis. Commonly used methods show systematic limitations: for example, MetaPhlAn relies on mapping reads to curated marker genes [ 33 , 34 ], Kraken2 uses k-mer-based assignment against reference genomes [ 35 ], and DIAMOND aligns translated DNA sequences against protein databases [ 36 ]. When processing samples from LMI populations, all these methods can miss or misclassify novel species since their underlying databases lack appropriate references [ 37 , 38 ]. Similarly, strain-tracking tools like StrainPhlAn and PanPhlAn may struggle with accurate strain identification in LMI populations due to limited strain-level reference data [ 33 ]. In metagenome assembly, short sequencing reads must be computationally stitched together into longer contiguous sequences based on overlapping regions and coverage patterns, a process that becomes more challenging when dealing with species that diverge significantly from references. After assembly, these contigs must be grouped (or “binned”) into individual genomes, but without appropriate reference genomes, this binning process can incorrectly cluster contigs from different species into the same MAG or fail to recover complete MAGs [ 37 ]. Even genome annotation pipelines show bias, as protein-coding genes and other genomic features are typically predicted based on models trained primarily on HI reference genomes [ 39 ]. Integrating cultural and genetic host data Traditional dietary patterns play a crucial role in shaping gut microbiome composition, but analyzing these relationships in LMI populations presents unique methodological challenges. A fundamental issue is that food questionnaires and databases developed for HI populations lack appropriate categories to capture traditional foods and cooking methods [ 40 ], as demonstrated by studies of Hmong and Karen populations adapting to life in the United States [ 41 ]. This limitation is further compounded by the complexity of traditional diets; research on African populations has demonstrated that both seasonal food availability and traditional processing methods create distinct temporal patterns in microbiome composition that standard analytical approaches cannot adequately capture [ 42 ]. Similar methodological barriers extend to other lifestyle factors that influence microbiome composition. Physical activity questionnaires often fail to account for LMI forms of exercise and daily movement patterns [ 43 ]. Cultural practices around hygiene, including traditional cleaning methods and shared living spaces, may be misclassified by standard surveys [ 44 ]. Additionally, the documentation of traditional and complementary medicine poses particular challenges, as these interventions often involve complex combinations of herbs, foods, and behavioral modifications that resist categorization in conventional medical terminology [ 45 ]. Environmental exposure assessments similarly struggle to capture the nuanced ways in which traditional communities interact with their local ecosystems, from foraging practices to seasonal migration patterns [ 46 ]. Host age and sex are also important variables influencing gut microbiome composition [ 47 , 48 ], but achieving balanced representation of sexes and a full range of age groups can be challenging in some study settings [ 49 , 50 ]. These methodological limitations complicate the analysis and interpretation of microbiome and lifestyle associations. Finally, host genetics play a critical role in shaping microbiome composition, adding another layer of complexity to population-specific microbiome research [ 51 ]. The gut microbiome varies widely across the globe [ 25 ], and there are significant associations between host genetic variation and microbiome composition across diverse human populations [ 52 ]. Not all of these associations are universal; for example, Boulund et al identified several specific associations between host genotype and gut microbes that were specific to ethnicity [ 53 ]. Host genetics can also affect susceptibility to diseases that are prevalent in LMI countries, such as malaria and helminth infection, which are also known to modulate host-microbiome interactions [ 54 – 56 ]. These findings underscore the importance of considering both host genetic background and environmental factors when analyzing microbiome data from diverse populations. Infrastructure barriers Resource-limited settings introduce significant barriers that extend beyond simple technical considerations. Limitations in access to high-performance computing systems, sample storage capabilities, and data transfer systems necessary for microbiome data analyses create obstacles for researchers in developing nations [ 57 , 58 ] ( Outstanding Questions ). Varying environmental conditions in remote settings can impact sample quality and processing requirements, necessitating the development of robust analysis pipelines that can function effectively with limited resources. While not specific to global microbiome data, differences in sample collection and processing can introduce technical artifacts upon integration of multiple studies. Biological factors relevant to microbiome composition such as microbial load, stool quality, and health status may also be recorded differently study-to-study and cause analytical biases [ 25 , 37 ] Lastly, funding limitations in regional institutions may restrict large-scale studies of the gut microbiome, even when physical and computational infrastructure exists [ 11 , 59 ]. OUTSTANDING QUESTIONS. Can existing marker-based tools for taxonomic classification be adapted to improve accuracy when analyzing microbiomes from underrepresented populations? How can dietary and lifestyle questionnaires be modified to more accurately capture cultural practices across world regions? How can institutions from high income countries support collaborators in low and middle income countries seeking to strengthen local research programs, expand training opportunities, and improve access to high-performance computing? Resources and guidelines for analysis of microbiomes from underrepresented populations Global microbiome datasets Efforts are actively being made to catalog a broader range of human microbiome samples. Many of these datasets are publicly available or accessible upon request and offer sample sizes large enough to support well-powered analyses of microbiome variation across diverse geographies. We documented several large datasets that include regions that have historically been underrepresented in the microbiome literature, and we provide an overview of the contents of these datasets in Table 1 . Several of these resources combine multiple sources of publicly available data with the aim of harmonizing data across studies and capturing diversity between samples. The largest of these efforts, the Human Microbiome Compendium [ 25 ] comprises 168,464 16S rRNA gene amplicon sequencing (16S amplicon) samples from 68 countries, including many nations underrepresented in microbiome literature such as Colombia, Malawi, and Thailand. These samples originate from over 400 studies that are publicly available and are uniformly processed to integrate as many microbiome samples as possible. Another large dataset, GMrepo [ 60 , 61 ], contains 71,642 consistently annotated samples from more than 60 countries spanning every continent but Antarctica, including both whole-genome metagenomics and 16S amplicon sequencing. WestLake Gut Project focuses on integrating diverse samples from both rural and urban populations within China, mainly featuring 16S amplicon sequencing data. As there are more amplicon sequencing samples currently available than metagenomic samples, 16S amplicon samples typically dominate the largest datasets. Other datasets intentionally focus on integrating metagenomic data: curatedMetagenomicData [ 63 ] features 5,716 samples from 25 countries. TABLE 1. Large datasets of globally diverse human gut microbiome sequences Name Description Sequence Type Continents # Samples Human Microbiome Compendium [ 25 ] Integration of publicly available human gut microbiome data 16S amplicon Africa, Asia, Australia, Europe, North America, South America >160,000 samples GMRepo [ 60 , 61 ] Database of curated and consistently annotated human gut metagenomes 16S amplicon; Metagenomics Africa, Asia, Australia, Europe, North America, South America >70,000 samples WestLake Gut Project [ 62 ] Consortium of gut microbiome -based human cohort studies in China 16S amplicon; Metagenomics Asia >32,000 samples curatedMetagenomicData [ 63 ] Standardized human microbiome data with manually curated metadata Metagenomics Africa, Asia, Europe, North America, South America >22,000 samples (v 3.6.0) AWI-Gen 2 Microbiome Project (H3Africa) [ 30 ] Largest population-representative survey of gut metagenomes of African individuals to date Metagenomics; MAGS Africa >1,800 samples; >1,000 genomes Global Microbiome Conservancy ( https://microbiomeconservancy.org/ ) Collection of non-industrialized and industrialized microbiome samples focused on preserving microbial diversity; mainly operates as a biobank/database Metagenomics Africa, Asia, Europe, North America >1,400 samples Human Reference Gut Microbiome [ 64 ] Collected samples from 3 under-represented Asian Metagenomics; MAGs Asia >800 samples; countries to expand Unified Human Gastrointestinal Genome >29,000 genomes Unified Human Gastrointestinal Genome 2.0 [ 13 ] Comprehensive, high-quality reference genomes of the human gut microbiota MAGs Africa, Asia, Australia, Europe, North America, South America >200,000 genomes Open in a new tab Other studies aimed at improving representation in microbiome studies have generated new samples from underrepresented regions. One example is the AWI-Gen 2 Microbiome Project [ 30 ], which generated 1,803 new metagenomic samples from women in Burkina Faso, Ghana, Kenya, and South Africa. Many additional samples have come from smaller studies, generally conducted by independent teams who are working to improve microbiome representation on a scale that may be more practical for individual research groups. The first microbiome study on a Paraguayan cohort, containing 60 samples, was published in 2024 [ 65 ]. Recent efforts have also been made to study the microbiomes of Indigenous populations in places such as Brazil [ 66 ], Nepal [ 31 ], and Mexico [ 67 ]. Studies such as these that sample underrepresented populations provide important insight into the breadth and diversity of gut microbiomes, improving our understanding of how microbiomes differ between populations across the world. Ethical considerations While microbiome sampling of global populations is crucial for addressing the knowledge disparities outlined above, it also involves ethical concerns. One common pitfall is a disproportionate focus on human health questions specific to North American and European populations when conducting microbiome studies in global populations. This risk may be particularly pronounced when these initiatives are led solely by teams from the HI countries. When starting a new collaboration, it is critical to take steps to ensure mutually beneficial relationships with both regional institutions and research participants. Partnering with regional scientists to incorporate research questions that are applicable to the local community broadens the scope and impact of the project. For example, a collaboration between scientists in the United States and a group at the International Centre for Diarrhoeal Disease Research in Bangladesh has led to the development of interventions for childhood malnutrition, a prominent issue in LMI countries [ 68 – 70 ]. This collaboration can serve as a valuable example of how international teams can incorporate health priorities of LMI nations into microbiome research. In tandem, researchers can enable institutions in LMI countries to strengthen their own research centers through guidance in developing computational infrastructure and bioinformatics capacity or training of local investigators [ 71 , 72 ]. A landmark effort in empowering regional scientific leadership is the Human Heredity and Health in Africa (H3Africa) consortium—a partnership between the NIH, the Wellcome Trust, and the African Academy of Sciences—which has significantly advanced genomic research led by African scientists. The initiative has supported regional capacity building through training programs, the development of biorepositories, and the creation of bioinformatics infrastructure across the continent [ 73 , 74 ]. Members of the H3Africa Microbiome Working Group outlined a detailed framework with clear guidance for international researchers on partnering with local leadership and building long-term collaborations in Africa [ 75 ]. Secondly, global cohorts from underrepresented nations may include Indigenous communities, who are at increased risk of exploitation from ethical misconduct in research settings [ 76 , 77 ]. It is essential for research teams working with these communities to engage in tiered consent: ongoing and proactive transparency with participants about the use of their biological data coupled with informed consent. Researchers should also inquire if potential collaborators work with a community advisory group that could simplify this process by acting as a cultural and linguistic bridge between researchers and local communities [ 76 , 78 ]. Researchers can draw guidance from the practices of the Yanomami Foundation, an organization that studies the microbiome of an Indigenous Amazonian people. Their approach includes intercultural training, participatory research, and benefit-sharing, ensuring that the Yanomami people are actively involved as co-creators of scientific knowledge ( yanomamifoundation.org , [ 79 ]). Another important consideration in data sovereignty: “open science” guidelines, such as the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) [ 80 ], should be complemented with practices that promote autonomy in sequencing information [ 81 ]. An example is the CARE Principles (Collective Benefit, Authority to Control, Responsibility, and Ethics), which advocate for equitable participation of Indigenous communities in the re-use of data [ 82 , 83 ]. Lastly, we recommend researchers aim to accurately describe participants by avoiding connotative labels such as “traditional” and “ancestral” [ 76 ], or by providing study-specific definitions when binary labels are inevitable. Instead, descriptors such as subsistence strategy (e.g., hunter-gatherer, agriculturist), region of sampling, or other self-reported attributes relevant to the study question may offer more meaningful context and minimize harm to the communities [ 77 , 84 ]. While these recommendations aim to promote ethical and equitable research practices, their implementation may be challenging. Some Indigenous communities may lack representative organizations, and not all regional institutions possess the cultural competence necessary for responsible collaboration. In such cases, researchers must take care to assess the integrity of their partnerships. Analysis recommendations The two most common microbiome assays are 16S amplicon sequencing and shotgun metagenomic sequencing. Each offers distinct advantages for analysis of diverse microbiome data: metagenomic sequencing, which sequences microbial DNA extracted from a sample, provides more genomic information about functional capacity [ 33 ], phylogeny and evolution [ 14 , 52 ]; with sufficient library size, this data can also be used to quantify strain-level abundance [ 85 ] and transmission [ 86 ] of even under characterized taxa. 16S amplicon sequencing, which uses a section of a single marker gene to infer taxonomic identity of microbiota in a sample, can provide ecological information with far less sequencing data than is required by metagenomics [ 87 , 88 ], though the relationship between reference databases and community composition makes it difficult to discern which techniques may uncover the most relevant information [ 89 , 90 , 91 ]. For microbial communities that are poorly represented in reference databases, particularly at the genus or species level, 16S amplicon sequencing may reveal more useful information at higher taxonomic ranks (phylum, class, order, and family) than other metagenomic assays [ 92 , 93 ]. Another consideration is that most human microbiome datasets in publicly available databases are 16S amplicon datasets. In some world regions, 16S amplicon sequencing is the only form of human gut microbiome data with enough samples to support robust analysis [ 25 ]. 16S amplicon data may also be an advantageous choice when large sample sizes are important, such as training machine-learning models. Resources such as the Human Microbiome Compendium may hold tens of thousands of relevant 16S amplicon sequencing samples. Annotating projects with technical factors such as amplicon choice and extraction protocol could help correct for batch effects in large-scale analyses, though with large enough sample sizes from a diverse range of projects may diminish the need to perform corrections [ 94 ]. In addition to re-analysis of existing data, large compendia can also be used to help contextualize your own samples. For example, one could perform principal coordinate analysis on samples from a single study combined with all compendium samples from the same continent, resulting in an ordination informed by variation observed from a broader selection of samples that may reveal new between-group differences in the relevant dataset [ 95 ]. Many sequencing-based microbiome quantification methods rely on comparing reads to databases of known microbial genomes. Studies employing metagenomic assembly have enabled a rapid expansion in knowledge of microbial genomics, and additional analysis of sequencing data has yielded huge gains in improving the mappability of metagenomic reads [ 12 , 96 – 98 ], but many human-associated species derived using this method lack publicly available reference genomes [ 12 ]. Since uncharacterized taxa are likely to be more prevalent in undersampled populations, analytic approaches that can accurately identify microbes lacking public genomes will be important for improving global microbiome representation. Building libraries with better characterized amplicons such as the V4 region of the 16S rRNA gene can help mitigate these effects [ 91 ]. Using analytical approaches that do not rely exclusively on references may also help. Evaluating amplicon sequence variants (ASVs) using their sequences, rather than their inferred taxonomic classification, allows results to include unknown taxa without consolidating distantly related organisms into a large “unknown” bucket; using ASVs directly, rather than groups of sequences clustered into operational taxonomic units, also simplifies the process of combining results from multiple studies [ 99 ]. Still, reference databases continue to grow: technological and methodological improvements, such as deeper metagenomic sequencing and single amplified genomes, continue to expand available genomes for gut microbiota [ 100 ]. Using larger reference databases may increase computational complexity [ 101 ], but resources such as the Unified Human Gastrointestinal Genome collection provide significant improvements to coverage over smaller, more commonly applied references such as RefSeq, particularly for samples from LMI countries [ 13 ]. Taxonomic assignments are also more reliable when performed with longer sequences at higher ranks, that is, performing taxonomic assignments on assembled contigs rather than raw reads and using assignments at the family or order level [ 102 ]. Indigenous and underrepresented cohorts are often limited by small sample sizes [ 103 – 105 ], requiring strategies to identify robust associations. To generate well-powered insights into global microbiome diversity, researchers often integrate data from multiple studies, though protocol differences may contribute to differences in relative abundances [ 106 ]: Studies performed by labs in differing conditions, possibly on opposite sides of the world, likely take different approaches to sample collection, storage and preservation [ 107 , 108 ], followed by use of different extraction kits [ 109 , 110 ] and amplification protocols [ 111 ], all of which can alter observed sample composition [ 25 , 37 ]. Researchers should account for differences in library preparation and sequencing that may influence read quality and how filtering and trimming steps should be taken [ 112 , 113 ]. When contextualizing results from these analyses, considering these differences is critical: for example, when considering data from studies using fecal samples and rectal swabs, it is important to consider that swabs are more susceptible to contamination [ 114 , 115 ]. Most importantly, the study source must be included as a batch effect in downstream statistical tests to further avoid spurious findings caused by these processing differences. While amplicon sequencing has unique concerns introduced by the amplification process [ 116 ], many experiment-specific effects are linked to sampling choices made prior to sequencing [ 117 ]. Though some intra-experimental variation could be difficult to account for [ 118 ], keeping these factors in mind as potential covariates is important, as is deciding how to model project-level effects. However, even with these measures in place, researchers should acknowledge the inherent limitations when interpreting results from integrated microbiome analysis. Comparing results to existing studies may also be difficult because of differences in baseline composition, a factor that can at least be described by carefully comparing controls and considering previous findings of population-level patterns in composition [ 25 ]. For instance, a depletion of Lactobacillus in one study may have different implications than a similar dynamic observed in a population with a much higher abundance of the same microbe. Finally, integration of global microbiome data and non-representative diet questionnaires remains a significant computational hurdle. Because food frequency data is often prone to zero-inflation, researchers have turned to clustering and distance metrics to group similar dietary patterns [ 41 , 119 , 120 ], rather than testing individual food items which may be culturally or regionally specific. Johnson et al. further addressed these limitations by developing a hierarchical food tree to establish metrics of dietary alpha and beta diversity [ 121 ]. Although based on U.S. data, this framework could potentially be adapted for non-industrialized diets using global resources like the INFOODS database ( fao.org/infoods ). In fact, the sparse nature of diet questionnaire data lends itself well to established multivariate techniques for the analysis of microbiome data. Choi et al. recently highlighted several methods for integration of diet and microbiome data including canonical correspondence analysis and co-occurrence networks [ 122 ]. Vangay and colleagues used Procrustes analysis, which evaluates how well the overall structure of one dataset corresponds to another, to compare diet and microbiome profiles in cohorts spanning a gradient of immigration from Thailand to the United States. However, they noted that interpretation was limited by incomplete nutritional data and complex diet–microbiome interactions [ 41 ]. In such cases, tools such as FoodOn may be useful for inference. This database provides ontologies of global food items, helping researchers describe and organize foods in a standardized way, with details on their origins, ingredients, processing methods, and nutritional content ( https://foodon.org/ ). To further aid interpretation, researchers can highlight the variables driving the largest sources of lifestyle variation and provide region-specific context. For instance, Jha and colleagues utilized correspondence analysis of survey data to find that drinking water source, fuel type, consumption of yogurt, and several other factors collectively explained nearly 30% of gut microbiome variation across several Himalayan and Hadza hunter-gatherer populations. They contextualized these findings using studies of water chemistry in similar geographies [ 31 ]. While culturally-aware questionnaires still have room for improvement, a combination of these methods make working with existing data more informed and straightforward. Concluding Remarks and Future Perspectives Expanding the diversity of gut microbiome research is essential for a more nuanced understanding of its role in health and disease. In addition to broader sampling, this effort will require improved computational methods to overcome the challenges involved in analyzing datasets from diverse global regions. The most prominent issues for computational analysis are biases in taxonomic reference databases, complications in integrating participant data, and a lack of computational infrastructure in developing nations. Here, we have outlined solutions to some of those problems and provided recommendations for maintaining equitable partnerships with regional institutions and participants. Although this review describes recommendations for working with existing data, we note that new sample recruitment and collection is necessary for improved global representation in microbiome literature. The literature includes extensive guidelines for effective sampling, including strategies to promote equity in sampling efforts [ 75 , 82 ], creation of region-specific diet and lifestyle surveys [ 31 , 120 ], and methods for preserving stool samples collected in remote areas [ 107 , 108 , 123 ]. By prioritizing the ethical analysis of diverse datasets, the field can move toward a more comprehensive understanding of the human gut microbiome. This progress will enhance the accuracy of microbiome-based health interventions and ensure that discoveries benefit populations worldwide. HIGHLIGHTS. Publicly available microbiome datasets are strongly biased in favor of nations in Europe and North America, and these biases limit generalizability of findings to non-Western nations. Ongoing large-scale efforts to characterize the microbiota of other regions are expanding our knowledge of microbiome diversity. While a lack of global representation in microbiome reference databases and lifestyle surveys complicates computational pipelines, these challenges can be addressed at each step of analysis. Ensuring ethical microbiome research requires addressing data sovereignty, engaging local leadership, and promoting community-driven research to avoid exploitation and ensure mutual benefits, particularly for Indigenous and underrepresented populations. Acknowledgements This work is supported by National Institutes of Health grant R35-GM128716. We would like to thank Maanasa Raghavan, PhD and Shreya Ramachandran for their constructive criticism and guidance on drafts of this article. We would also like to thank Sarah Sumner and Frederick Naumann for helpful comments. Footnotes Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Declaration of interests The authors declare no competing interests. References [1]. Wardman JF, Bains RK, Rahfeld P, et al. Carbohydrate-active enzymes (CAZymes) in the gut microbiome. Nat Rev Microbiol 2022; 20: 542–556. [ DOI ] [ PubMed ] [ Google Scholar ] [2]. Shi N, Li N, Duan X, et al. Interaction between the gut microbiome and mucosal immune system. Mil Med Res 2017; 4: 14. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [3]. de Vos WM, Tilg H, Van Hul M, et al. Gut microbiome and health: mechanistic insights. Gut 2022; 71: 1020–1032. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [4]. Ghaisas S, Maher J, Kanthasamy A. Gut microbiome in health and disease: Linking the microbiome-gut-brain axis and environmental factors in the pathogenesis of systemic and neurodegenerative diseases. Pharmacol Ther 2016; 158: 52–62. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [5]. Kurilshikov A, Medina-Gomez C, Bacigalupe R, et al. Large-scale association analyses identify host factors influencing human gut microbiome composition. Nat Genet 2021; 53: 156–165. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [6]. Parizadeh M, Arrieta M-C. The global human gut microbiome: genes, lifestyles, and diet. Trends Mol Med 2023; 29: 789–801. [ DOI ] [ PubMed ] [ Google Scholar ] [7]. De Filippo C, Cavalieri D, Di Paola M, et al. Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa. Proc Natl Acad Sci U S A 2010; 107: 14691–14696. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [8]. Kwak S, Usyk M, Beggs D, et al. Sociobiome - Individual and neighborhood socioeconomic status influence the gut microbiome in a multi-ethnic population in the US. NPJ Biofilms Microbiomes 2024; 10: 19. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [9]. Deschasaux M, Bouter KE, Prodan A, et al. Depicting the composition of gut microbiota in a population with varied ethnic origins but shared geography. Nat Med 2018; 24: 1526–1531. [ DOI ] [ PubMed ] [ Google Scholar ] [10]. He Y, Wu W, Wu S, et al. Linking gut microbiota, metabolic syndrome and economic status based on a population-level analysis. Microbiome 2018; 6: 172. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [11]. Brewster R, Tamburini FB, Asiimwe E, et al. Surveying gut microbiome research in Africans: Toward improved diversity and representation. Trends Microbiol 2019; 27: 824–835. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [12]. Pasolli E, Asnicar F, Manara S, et al. Extensive unexplored human microbiome diversity revealed by over 150,000 genomes from metagenomes spanning age, geography, and lifestyle. Cell 2019; 176: 649–662.e20. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [13]. Almeida A, Nayfach S, Boland M, et al. A unified catalog of 204,938 reference genomes from the human gut microbiome. Nat Biotechnol 2021; 39: 105–114. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [14]. Tett A, Huang KD, Asnicar F, et al. The Prevotella copri complex comprises four distinct clades underrepresented in Westernized populations. Cell Host Microbe 2019; 26: 666–679.e7. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [15]. Pedersen HK, Gudmundsdottir V, Nielsen HB, et al. Human gut microbes impact host serum metabolome and insulin sensitivity. Nature 2016; 535: 376–381. [ DOI ] [ PubMed ] [ Google Scholar ] [16]. Kovatcheva-Datchary P, Nilsson A, Akrami R, et al. Dietary fiber-induced improvement in glucose metabolism is associated with increased abundance of Prevotella. Cell Metab 2015; 22: 971–982. [ DOI ] [ PubMed ] [ Google Scholar ] [17]. O’Keefe SJD, Li JV, Lahti L, et al. Fat, fibre and cancer risk in African Americans and rural Africans. Nat Commun 2015; 6: 6342. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [18]. Abdill RJ, Adamowicz EM, Blekhman R. Public human microbiome data are dominated by highly developed countries. PLoS Biol 2022; 20: e3001536. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [19]. Ramaboli MC, Ocvirk S, Khan Mirzaei M, et al. Diet changes due to urbanization in South Africa are linked to microbiome and metabolome signatures of Westernization and colorectal cancer. Nat Commun 2024; 15: 3379. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [20]. de la Cuesta-Zuluaga J, Corrales-Agudelo V, Velásquez-Mejía EP, et al. Gut microbiota is associated with obesity and cardiometabolic disease in a population in the midst of Westernization. Sci Rep 2018; 8: 11356. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [21]. Ghosh TS, Gupta SS, Bhattacharya T, et al. Gut microbiomes of Indian children of varying nutritional status. PLoS One 2014; 9: e95547. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [22]. Hu Y, Feng Y, Wu J, et al. The gut microbiome signatures discriminate healthy from pulmonary tuberculosis patients. Front Cell Infect Microbiol 2019; 9: 90. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [23]. Wang Y, Tsuo K, Kanai M, et al. Challenges and opportunities for developing more generalizable polygenic risk scores. Annu Rev Biomed Data Sci 2022; 5: 293–320. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [24]. Ding Y, Hou K, Xu Z, et al. Polygenic scoring accuracy varies across the genetic ancestry continuum. Nature 2023; 618: 774–781. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [25]. Abdill RJ, Graham SP, Rubinetti V, et al. Integration of 168,000 samples reveals global patterns of the human gut microbiome. Cell 2025; 188: 1100–1118.e17. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [26]. Vujkovic-Cvijin I, Sklar J, Jiang L, et al. Host variables confound gut microbiota studies of human disease. Nature 2020; 587: 448–454. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [27]. Brooks AW, Priya S, Blekhman R, et al. Gut microbiota diversity across ethnicities in the United States. PLoS Biol 2018; 16: e2006842. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [28]. He Y, Wu W, Zheng H-M, et al. Regional variation limits applications of healthy gut microbiome reference ranges and disease models. Nat Med 2018; 24: 1532–1535. [ DOI ] [ PubMed ] [ Google Scholar ] [29]. Carter MM, Olm MR, Merrill BD, et al. Ultra-deep sequencing of Hadza hunter-gatherers recovers vanishing gut microbes. Cell 2023; 186: 3111–3124.e13. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [30]. Maghini DG, Oduaran OH, Olubayo LAI, et al. Expanding the human gut microbiome atlas of Africa. Nature 2025; 638: 718–728. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [31]. Jha AR, Davenport ER, Gautam Y, et al. Gut microbiome transition across a lifestyle gradient in Himalaya. PLoS Biol 2018; 16: e2005396. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [32]. Nayfach S, Shi ZJ, Seshadri R, et al. New insights from uncultivated genomes of the global human gut microbiome. Nature 2019; 568: 505–510. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [33]. Beghini F, McIver LJ, Blanco-Míguez A, et al. Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3. Elife; 10. Epub ahead of print 4 May 2021. DOI: 10.7554/eLife.65088. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [34]. Segata N, Waldron L, Ballarini A, et al. Metagenomic microbial community profiling using unique clade-specific marker genes. Nat Methods 2012; 9: 811–814. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [35]. Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol 2019; 20: 257. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [36]. Buchfink B, Reuter K, Drost H-G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat Methods 2021; 18: 366–368. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [37]. Nearing JT, Comeau AM, Langille MGI. Identifying biases and their potential solutions in human microbiome studies. Microbiome 2021; 9: 113. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [38]. Ye SH, Siddle KJ, Park DJ, et al. Benchmarking metagenomics tools for taxonomic classification. Cell 2019; 178: 779–794. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [39]. Haynes WA, Tomczak A, Khatri P. Gene annotation bias impedes biomedical research. Sci Rep 2018; 8: 1362. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [40]. Shim J-S, Oh K, Kim HC. Dietary assessment methods in epidemiologic studies. Epidemiol Health 2014; 36: e2014009. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [41]. Vangay P, Johnson AJ, Ward TL, et al. US immigration westernizes the human gut microbiome. Cell 2018; 175: 962–972.e10. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [42]. Smits SA, Leach J, Sonnenburg ED, et al. Seasonal cycling in the gut microbiome of the Hadza hunter-gatherers of Tanzania. Science 2017; 357: 802–806. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [43]. Bauman A, Bull F, Chey T, et al. The International Prevalence Study on Physical Activity: results from 20 countries. Int J Behav Nutr Phys Act 2009; 6: 21. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [44]. Kenters N, Gottlieb T, Hopman J, et al. An international survey of cleaning and disinfection practices in the healthcare environment. J Hosp Infect 2018; 100: 236–241. [ DOI ] [ PubMed ] [ Google Scholar ] [45]. Lee EL, Richards N, Harrison J, et al. Prevalence of use of traditional, complementary and alternative medicine by the general population: A systematic review of national studies published from 2010 to 2019. Drug Saf 2022; 45: 713–735. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [46]. Ezzati M, Utzinger J, Cairncross S, et al. Environmental risks in the developing world: exposure indicators for evaluating interventions, programmes, and policies. J Epidemiol Community Health 2005; 59: 15–22. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [47]. Yatsunenko T, Rey FE, Manary MJ, et al. Human gut microbiome viewed across age and geography. Nature 2012; 486: 222–227. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [48]. Markle JGM, Frank DN, Mortin-Toth S, et al. Sex differences in the gut microbiome drive hormone-dependent regulation of autoimmunity. Science 2013; 339: 1084–1088. [ DOI ] [ PubMed ] [ Google Scholar ] [49]. Tijdens K, Steinmetz S. Is the web a promising tool for data collection in developing countries? An analysis of the sample bias of 10 web and face-to-face surveys from Africa, Asia, and South America. Int J Soc Res Methodol 2016; 19: 461–479. [ Google Scholar ] [50]. Weber AM, Gupta R, Abdalla S, et al. Gender-related data missingness, imbalance and bias in global health surveys. BMJ Glob Health 2021; 6: e007405. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [51]. Goodrich JK, Waters JL, Poole AC, et al. Human genetics shape the gut microbiome. Cell 2014; 159: 789–799. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [52]. Suzuki TA, Fitzstevens JL, Schmidt VT, et al. Codiversification of gut microbiota with humans. Science 2022; 377: 1328–1332. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [53]. Boulund U, Bastos DM, Ferwerda B, et al. Gut microbiome associations with host genotype vary across ethnicities and potentially influence cardiometabolic traits. Cell Host Microbe 2022; 30: 1464–1480.e6. [ DOI ] [ PubMed ] [ Google Scholar ] [54]. Kwok AJ, Mentzer A, Knight JC. Host genetics and infectious disease: new tools, insights and translational opportunities. Nat Rev Genet 2021; 22: 137–153. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [55]. Mandal RK, Schmidt NW. Mechanistic insights into the interaction between the host gut microbiome and malaria. PLoS Pathog 2023; 19: e1011665. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [56]. Ling F, Steinel N, Weber J, et al. The gut microbiota response to helminth infection depends on host sex and genotype. ISME J 2020; 14: 1141–1153. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [57]. Trejo-Sanchez JA, Hernandez-Lopez FJ, Uh Zapata MA, et al. Teaching high-performance computing in developing countries: A case study in Mexican universities. In: 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). IEEE, 2022, pp. 338–345. [ Google Scholar ] [58]. Akpan, Imaobong O, Esang MO, et al. View of Accelerating Scientific Discovery Through High Performance Computing: Challenges and Opportunities for Africa Researchers. International Journal of Diplomacy and Political Relations; 1, https://pubjournals.com/ijdpr/article/view/24/21 (2024, accessed 18 February 2025). [ Google Scholar ] [59]. Allali I, Abotsi RE, Tow LA, et al. Human microbiota research in Africa: a systematic review reveals gaps and priorities for future research. Microbiome 2021; 9: 241. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [60]. Wu S, Sun C, Li Y, et al. GMrepo: a database of curated and consistently annotated human gut metagenomes. Nucleic Acids Res 2020; 48: D545–D553. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [61]. Dai D, Zhu J, Sun C, et al. GMrepo v2: a curated human gut microbiome database with special focus on disease markers and cross-dataset comparison. Nucleic Acids Res 2022; 50: D777–D784. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [62]. Gou W, Chen Y-M, Pan A, et al. Westlake Gut Project: A consortium of microbiome epidemiology for the gut microbiome and health research in China. Med Microecol 2022; 14: 100064. [ Google Scholar ] [63]. Pasolli E, Schiffer L, Manghi P, et al. Accessible, curated metagenomic data through ExperimentHub. Nat Methods 2017; 14: 1023–1024. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [64]. Kim CY, Lee M, Yang S, et al. Human reference gut microbiome catalog including newly assembled genomes from under-represented Asian metagenomes. Genome Med 2021; 13: 134. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [65]. Molinas-Vera M, Ferreira-Sanabria G, Peña P, et al. The Paraguayan gut microbiome contains high abundance of the phylum Actinobacteriota and reveals the influence of health and lifestyle factors. Gut Microbes Reports 2024; 1: 1–16. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [66]. Schaan AP, Sarquis D, Cavalcante GC, et al. The structure of Brazilian Amazonian gut microbiomes in the process of urbanisation. NPJ Biofilms Microbiomes 2021; 7: 65. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [67]. Sánchez-Quinto A, Cerqueda-García D, Falcón LI, et al. Gut microbiome in children from indigenous and urban communities in México: Different subsistence models, different microbiomes. Microorganisms 2020; 8: 1592. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [68]. Barratt MJ, Nuzhat S, Ahsan K, et al. Bifidobacterium infantis treatment promotes weight gain in Bangladeshi infants with severe acute malnutrition. Sci Transl Med 2022; 14: eabk1107. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [69]. Chang H-W, Lee EM, Wang Y, et al. Prevotella copri and microbiota members mediate the beneficial effects of a therapeutic food for malnutrition. Nat Microbiol 2024; 9: 922–937. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [70]. Hartman SJ, Hibberd MC, Mostafa I, et al. A microbiome-directed therapeutic food for children recovering from severe acute malnutrition. Sci Transl Med 2024; 16: eadn2366. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [71]. Gordon JI, Barratt MJ, Hibberd MC, et al. Establishing human microbial observatory programs in low- and middle-income countries. Ann N Y Acad Sci 2024; 1540: 13–20. [ DOI ] [ PubMed ] [ Google Scholar ] [72]. Abrudan M, Matimba A, Nikolic D, et al. Train-the-Trainer as an effective approach to building global networks of experts in genomic surveillance of antimicrobial resistance (AMR). Clin Infect Dis 2021; 73: S283–S289. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [73]. Mulder N, Abimiku A ‘le, Adebamowo SN, et al. H3Africa: current perspectives. Pharmgenomics Pers Med 2018; 11: 59–66. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [74]. Aron S, Chauke PA, Ras V, et al. The development of a sustainable bioinformatics training environment within the H3Africa bioinformatics network (H3ABioNet). Front Educ 2021; 6: 725702. [ Google Scholar ] [75]. Oduaran OH, Foláyan MO, Kamng’ona AW, et al. Microbiome research in Africa must be based on equitable partnerships. Nat Med 2024; 30: 2715–2717. [ DOI ] [ PubMed ] [ Google Scholar ] [76]. Bader AC, Van Zuylen EM, Handsley-Davis M, et al. A relational framework for microbiome research with Indigenous communities. Nat Microbiol 2023; 8: 1768–1776. [ DOI ] [ PubMed ] [ Google Scholar ] [77]. Handsley-Davis M, Anderson MZ, Bader AC, et al. Microbiome ownership for Indigenous peoples. Nat Microbiol 2023; 8: 1777–1786. [ DOI ] [ PubMed ] [ Google Scholar ] [78]. Mangola SM, Lund JR, Schnorr SL, et al. Ethical microbiome research with Indigenous communities. Nat Microbiol 2022; 7: 749–756. [ DOI ] [ PubMed ] [ Google Scholar ] [79]. Clemente JC, Pehrsson EC, Blaser MJ, et al. The microbiome of uncontacted Amerindians. Sci Adv; 1. Epub ahead of print 3 April 2015. DOI: 10.1126/sciadv.1500183. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [80]. Wilkinson MD, Dumontier M, Aalbersberg IJJ, et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 2016; 3: 160018. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [81]. Hudson M, Garrison NA, Sterling R, et al. Rights, interests and expectations: Indigenous perspectives on unrestricted access to genomic data. Nat Rev Genet 2020; 21: 377–384. [ DOI ] [ PubMed ] [ Google Scholar ] [82]. Claw KG, Anderson MZ, Begay RL, et al. A framework for enhancing ethical genomic research with Indigenous communities. Nat Commun 2018; 9: 2957. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [83]. Carroll SR, Garba I, Figueroa-Rodríguez OL, et al. The CARE Principles for Indigenous Data Governance. In: Open Scholarship Press Curated Volumes: Policy. PubPub, 2023. [ Google Scholar ] [84]. Why Nature is updating its advice to authors on reporting race or ethnicity. Nature 2023; 616: 219. [ DOI ] [ PubMed ] [ Google Scholar ] [85]. Zachariasen T, Russel J, Petersen C, et al. MAGinator enables accurate profiling of de novo MAGs with strain-level phylogenies. Nat Commun 2024; 15: 5734. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [86]. Allert M, Ferretti P, Johnson KE, et al. Assembly, stability, and dynamics of the infant gut microbiome are linked to bacterial strains and functions in mother’s milk. bioRxiv. Epub ahead of print 28 January 2024. DOI: 10.1101/2024.01.28.577594. [ DOI ] [ Google Scholar ] [87]. Ranjan R, Rani A, Metwally A, et al. Analysis of the microbiome: Advantages of whole genome shotgun versus 16S amplicon sequencing. Biochem Biophys Res Commun 2016; 469: 967–977. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [88]. Lamoureux C, Surgers L, Fihman V, et al. Prospective comparison between shotgun metagenomics and Sanger sequencing of the 16S rRNA gene for the etiological diagnosis of infections. Front Microbiol 2022; 13: 761873. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [89]. Durazzi F, Sala C, Castellani G, et al. Comparison between 16S rRNA and shotgun sequencing data for the taxonomic characterization of the gut microbiota. Sci Rep 2021; 11: 3030. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [90]. Peterson D, Bonham KS, Rowland S, et al. Comparative analysis of 16S rRNA gene and metagenome sequencing in pediatric gut microbiomes. Front Microbiol 2021; 12: 670336. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [91]. McDonald D, Jiang Y, Balaban M, et al. Greengenes2 unifies microbial data in a single reference tree. Nat Biotechnol 2023; 42: 715–718. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [92]. Tessler M, Neumann JS, Afshinnekoo E, et al. Large-scale differences in microbial biodiversity discovery between 16S amplicon and shotgun sequencing. Sci Rep 2017; 7: 6589. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [93]. Hillmann B, Al-Ghalith GA, Shields-Cutler RR, et al. Evaluating the information content of shallow shotgun metagenomics. mSystems 2018; 3: e00069–18. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [94]. Lee AJ, Park Y, Doing G, et al. Correcting for experiment-specific variability in expression compendia can remove underlying signals. Gigascience; 9. Epub ahead of print 3 November 2020. DOI: 10.1093/gigascience/giaa117. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [95]. Shin H, Price K, Albert L, et al. Changes in the eye Microbiota associated with contact lens wearing. MBio 2016; 7: e00198. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [96]. Carlino N, Blanco-Míguez A, Punčochář M, et al. Unexplored microbial diversity from 2,500 food metagenomes and links with the human microbiome. Cell 2024; 187: 5775–5795.e15. [ DOI ] [ PubMed ] [ Google Scholar ] [97]. Parks DH, Chuvochina M, Rinke C, et al. GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy. Nucleic Acids Res 2022; 50: D785–D794. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [98]. Nayfach S, Shi ZJ, Seshadri R, et al. New insights from uncultivated genomes of the global human gut microbiome. Nature 2019; 568: 505–510. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [99]. Callahan BJ, McMurdie PJ, Holmes SP. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J 2017; 11: 2639–2643. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [100]. Schechter MS, Trigodet F, Veseli IA, et al. Ribosomal protein phylogeography offers quantitative insights into the efficacy of genome-resolved surveys of microbial communities. bioRxivorg. Epub ahead of print 15 January 2025. DOI: 10.1101/2025.01.15.633187. [ DOI ] [ Google Scholar ] [101]. Knight R, Vrbanac A, Taylor BC, et al. Best practices for analysing microbiomes. Nat Rev Microbiol 2018; 16: 410–422. [ DOI ] [ PubMed ] [ Google Scholar ] [102]. Tamames J, Cobo-Simón M, Puente-Sánchez F. Assessing the performance of different approaches for functional and taxonomic annotation of metagenomes. BMC Genomics 2019; 20: 960. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [103]. Schaan AP, Vidal A, Zhang A-N, et al. Temporal dynamics of gut microbiomes in non-industrialized urban Amazonia. mSystems 2024; 9: e0070723. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [104]. Seco-Hidalgo V, Witney AA, Chico ME, et al. Rurality and relative poverty drive acquisition of a stable and diverse gut microbiome in early childhood in a non-industrialized setting. Sci Rep 2025; 15: 5601. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [105]. Ryu EP, Gautam Y, Proctor DM, et al. Nepali oral microbiomes reflect a gradient of lifestyles from traditional to industrialized. Microbiome 2024; 12: 228. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [106]. Lim MY, Park Y-S, Kim J-H, et al. Evaluation of fecal DNA extraction protocols for human gut microbiome studies. BMC Microbiol 2020; 20: 212. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [107]. Maghini DG, Dvorak M, Dahlen A, et al. Quantifying bias introduced by sample collection in relative and absolute microbiome measurements. Nat Biotechnol 2024; 42: 328–338. [ DOI ] [ PubMed ] [ Google Scholar ] [108]. Blekhman R, Tang K, Archie EA, et al. Common methods for fecal sample storage in field studies yield consistent signatures of individual identity in microbiome sequencing data. Sci Rep 2016; 6: 31519. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [109]. Galla G, Praeg N, Rzehak T, et al. Comparison of DNA extraction methods on different sample matrices within the same terrestrial ecosystem. Sci Rep 2024; 14: 8715. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [110]. Kennedy NA, Walker AW, Berry SH, et al. The impact of different DNA extraction kits and laboratories upon the assessment of human gut microbiota composition by 16S rRNA gene sequencing. PLoS One 2014; 9: e88982. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [111]. Deissová T, Zapletalová M, Kunovský L, et al. 16S rRNA gene primer choice impacts off-target amplification in human gastrointestinal tract biopsies and microbiome profiling. Sci Rep 2023; 13: 12577. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [112]. Gonzalez A, Navas-Molina JA, Kosciolek T, et al. Qiita: rapid, web-enabled microbiome meta-analysis. Nat Methods 2018; 15: 796–798. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [113]. Sinha R, Abu-Ali G, Vogtmann E, et al. Assessment of variation in microbial community amplicon sequencing by the Microbiome Quality Control (MBQC) project consortium. Nat Biotechnol 2017; 35: 1077–1086. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [114]. Chanderraj R, Brown CA, Hinkle K, et al. The bacterial density of clinical rectal swabs is highly variable, correlates with sequencing contamination, and predicts patient risk of extraintestinal infection. Microbiome 2022; 10: 2. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [115]. Davis NM, Proctor DM, Holmes SP, et al. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 2018; 6: 226. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [116]. Schloss PD, Gevers D, Westcott SL. Reducing the effects of PCR amplification and sequencing artifacts on 16S rRNA-based studies. PLoS One 2011; 6: e27310. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [117]. Panek M, Čipčić Paljetak H, Barešić A, et al. Methodology challenges in studying human gut microbiota - effects of collection, storage, DNA extraction and next generation sequencing technologies. Sci Rep 2018; 8: 5143. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [118]. Yeoh YK, Chen Z, Hui M, et al. Impact of inter- and intra-individual variation, sample storage and sampling fraction on human stool microbial community profiles. PeerJ 2019; 7: e6172. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [119]. Cotillard A, Cartier-Meheust A, Litwin NS, et al. A posteriori dietary patterns better explain variations of the gut microbiome than individual markers in the American Gut Project. Am J Clin Nutr 2022; 115: 432–443. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [120]. Johnson AJ, Zheng JJ, Kang JW, et al. A guide to diet-microbiome study design. Front Nutr 2020; 7: 79. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [121]. Johnson AJ, Vangay P, Al-Ghalith GA, et al. Daily sampling reveals personalized diet-microbiome associations in humans. Cell Host Microbe 2019; 25: 789–802.e5. [ DOI ] [ PubMed ] [ Google Scholar ] [122]. Choi Y, Hoops SL, Thoma CJ, et al. A guide to dietary pattern-microbiome data integration. J Nutr 2022; 152: 1187–1199. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [123]. Song SJ, Amir A, Metcalf JL, et al. Preservation methods differ in fecal microbiome stability, affecting suitability for field studies. mSystems 2016; 1: e00021–16. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] ACTIONS View on publisher site PDF (331.7 KB) Cite Collections Permalink RESOURCES Similar articles Cited by other articles Links to NCBI Databases Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top

Related documents

• Developing Non-Communicable Disease Registries in Africa : A Step Towards Providing Quality Data for Improving Patient Outcomes#841661
• Microbiome-Mediated Gut Dysfunction in Non-Alcoholic Fatty Liver Disease#1047061
• Gut fungal signatures in colorectal cancer and their potential for supporting diagnosis: a multi-cohort metagenomic analysis.#297233
• Robust taxonomic classification in gut and vaginal microbiomes demonstrated through benchmarking with age-specific synthetic communities#1099956
• National Cohort Study on Infant Gut Microbiota and Nutrition#874341