These guidelines were drafted by the authors.

Preprocessing report generated automatically by the iMAP to provide a summary of quality control of the reads. The iMAP pipeline automatically saved the output in the “reports” folder as “report2_read_preprocessing.html”. (HTML 3463 kb)

Metadata profiling report generated automatically by the iMAP to provide a summary of the samples and the associated metadata. This report is the initial step in the RAYG (review-as-go) process. The report also displays the R-commands that demonstrates how to reproduce the report. The pipeline is set to automatically save the output in the “reports” folder as “report1_metadata_profiling.html”. (HTML 953 kb)

Multidimensional scaling (MDS) is a widely used dimensionality reduction technique in microbial ecology data analysis that captures the multivariate structure of the data while preserving pairwise distances between samples. While improvements in MDS have enhanced the ability to reveal group-specific data patterns, these MDS-based methods require prior assumptions for inference, limiting their application in general microbiome analysis. In this study, we introduce a new MDS-based ordination method, "F-informed MDS," which configures the data distribution based on the F-statistic, the ratio of dispersion between groups sharing common and different characteristics. Using semisynthetic datasets, we demonstrate that the proposed method is robust to hyperparameter selection while maintaining statistical significance throughout the ordination process. Various quality metrics for evaluating dimensionality reduction confirm that F-informed MDS is comparable to state-of-the-art methods in preserv..., , # Multidimensional scaling informed by F-statistic: Visualizing grouped microbiome data with inference

• Software: https://bioconductor.org/packages/FinfoMDS
• File or folder names are italicized. Package or variable names are monospaced.Â

File: Data.zip

Description:Â Raw data used in this study. Includes 4 folders and 4 files (see below).

1. Folder Simulated
   • Contains pairwise distances and ordination results. Includes 6 subfolders and 20 files. See below.
   • Folder F-MDS contains traning log by epoch (folder TrainingLog) and resulting representations Z (folder Results).
     • File names inside the folder are formatted as "sim_rev_{x}-N{n}-{method}-{param}-{type}.csv". Formatting rule is described in table below.
     • "-Z.csv" file is tabulated by each sample and its location in 2D coordinate in each row and column, respectively.
     • "-log.csv" file is tabulated at each row by training e..., , Changes after May 7, 2025:

File: Data.zip

Folder Alga and files alga.R, simulated.R, ternary.R have newly been added. Folders Simulated and Ternary have been revised.

Newly added files/folders

1. Folder Alga
   • Ordination results from algal microbiome dataset (Kim et al., 2022).
   • Microbiome dataset can be obtained elsewhere, e.g., https://github.com/soob-kim/FinfoMDS
2. File alga.R
3. File simulated.R
4. File ternary.R

Revised folders

1. Folder Simulated
   • Previous 6 files have been replaced with new 20 files.
   • The replacement represents new simulation datasets with revised conditions, i.e., data size, dimension.
   • Previous folder MDS has been removed as it is not used in revised manuscript version.
   • All other folders (F-MDS, Isomap, superMDS, t-SNE, UMAP-S, UMAP-U) contains newly replaced files after performing the ordinations with the new simulation da...

Video on understanding microbiome data from the Research Experiences in Microbiomes Network

This repository provides the raw data, analysis code, and results generated during a systematic evaluation of the impact of selected experimental protocol choices on the metagenomic sequencing analysis of microbiome samples. Briefly, a full factorial experimental design was implemented varying biological sample (n=5), operator (n=2), lot (n=2), extraction kit (n=2), 16S variable region (n=2), and reference database (n=3), and the main effects were calculated and compared between parameters (bias effects) and samples (real biological differences). A full description of the effort is provided in the associated publication.

Although metagenomic sequencing is now the preferred technique to study microbiome-host interactions, analyzing and interpreting microbiome sequencing data presents challenges primarily attributed to the statistical specificities of the data (e.g., sparse, over-dispersed, compositional, inter-variable dependency). This mini review explores preprocessing and transformation methods applied in recent human microbiome studies to address microbiome data analysis challenges. Our results indicate a limited adoption of transformation methods targeting the statistical characteristics of microbiome sequencing data. Instead, there is a prevalent usage of relative and normalization-based transformations that do not specifically account for the specific attributes of microbiome data. The information on preprocessing and transformations applied to the data before analysis was incomplete or missing in many publications, leading to reproducibility concerns, comparability issues, and questionable results. We hope this mini review will provide researchers and newcomers to the field of human microbiome research with an up-to-date point of reference for various data transformation tools and assist them in choosing the most suitable transformation method based on their research questions, objectives, and data characteristics.

Sequence processing report generated automatically by the iMAP to provide a summary of the output. The report was automatically saved in the “reports” folder as “report3_sequence_processing.html”. (HTML 4205 kb)

Overview

MicrobiomeHD is a standardized database of human gut microbiome studies in health and disease. This database includes publicly available 16S data from published case-control studies and their associated patient metadata. Raw sequencing data for each study was downloaded and processed through a standardized pipeline.

To be included in MicrobiomeHD, datasets have:

• publicly available raw sequencing data (fastq or fasta)
• publicly available metadata with at least case and control labels for each patient
• at least 15 case patients

Currently, MicrobiomeHD is focused on stool samples. Additional samples may be included in certain datasets, as indicated in the metadata.

Files

Additional information about the datasets included in this MicrobiomeHD release are in the MicrobiomeHD github repo https://github.com/cduvallet/microbiomeHD, in the file db/dataset_info.yaml. Top-level identifiers correspond to the dataset IDs used in Duvallet et al. 2017. Sample sizes in the yaml file are those that were described in the papers, and may not exactly reflect the actual data (due to missing/extra data, samples which didn't pass quality control, etc).

Each dataset was downloaded and processed through a standardized pipeline. The raw processing results are available in the *.tar.gz files here. Each file has the same directory structure and files, as described in the pipeline documentation: http://amplicon-sequencing-pipeline.readthedocs.io/en/latest/output.html.

Specific files of interest include:

• summary_file.txt: this file contains a summary of all parameters used to process the data
• datasetID.metadata.txt: the metadata associated with the samples. Note that some samples in the metadata may not have sequencing data, and vice versa.
• RDP/datasetID.otu_table.100.denovo.rdp_assigned: the 100% OTU tables with Latin taxonomic names assigned using the RDP classifier.
• datasetID.otu_seqs.100.fasta: representative sequences for each OTU in the 100% OTU table. OTU labels in the OTU table end with d_denovoID - these denovoIDs correspond to the sequences in this file. Processing

The raw data was acquired as described in the supplementary materials of Duvallet et al.'s "Meta analysis of microbiome studies identifies shared and disease-specific patterns".

Raw sequencing data was processed with the Alm lab's in-house 16S processing pipeline: https://github.com/thomasgurry/amplicon_sequencing_pipeline

Pipeline documentation is available at: http://amplicon-sequencing-pipeline.readthedocs.io/

Metadata was extracted from the original papers and/or data sources, and formatted manually.

Contributing

MicrobiomeHD is a resource that can be used to extract disease-specific microbiome signals in individual case-control studies. Many microbes respond non-specifically to health and disease, and the majority of bacterial associations within individual studies overlap with this "core" response. Researchers should cross-check their results with the data presented here to ensure that their identified microbial associations are specific to their disease under study.

We provide an updated list of "core" microbes here, as well as the raw OTU tables for anyone who wishes to reproduce and adapt this analysis to their study question.

If you would like to include your case-control dataset in MicrobiomeHD, please email duvallet[at]mit.edu.

For us to process your data through our standard pipeline, you will need to provide the following files and information about your data:

• raw sequencing data in fastq or fasta format (preferably fastq)
• information about which processing steps will be required (e.g. removing primers or barcodes, merging paired-end reads, etc)
• sample IDs associated with the sequencing data (either mapped to barcodes still in the sequences, or to each de-multiplexed sequencing file)
• case/control metadata of each sample
• other relevant metadata (e.g. sampling site, if not all samples are stool; sampling time point, if multiple samples per patient were taken; etc)

By using MicrobiomeHD in your own analyses, you agree to contribute your dataset to this database and to make your raw sequencing data (i.e. fastq files) publicly available.

Citing MicrobiomeHD

The MicrobiomeHD database and original publications for each of these datasets are described in Duvallet et al. (2017): http://biorxiv.org/content/early/2017/05/08/134031

If you use any of these datasets in your analysis, please cite both MicrobiomeHD (Duvallet et al. (2017)) and the original publication for each dataset that you use.

The code used to process and analyze this data in Duvallet et al. (2017) is available on github: https://github.com/cduvallet/microbiomeHD

Files

Core genera

file-S3.core_genera.txt: Supplemental Table 3 from Duvallet et al. (2017), listing the core health- and disease-associated microbes.

Datasets

Note that MicrobiomeHD contains all 28 datasets from Duvallet et al. (2017), as well as additional datasets which did not meet the inclusion criteria for the meta-analysis presented in the paper. Additional information about the datasets included in this MicrobiomeHD release are in the original publications and the MicrobiomeHD github repo https://github.com/cduvallet/microbiomeHD, in the file db/dataset_info.yaml.

The sample sizes listed here reflect what was reported in the original publications. Some may have discrepancies between what is reported and what is in the actual data due to missing data, quality issues, barcode mismatches, etc.

• asd_son_results.tar.gz (asd_son): NT: 44, ASD: 59
  • http://dx.doi.org/10.1371/journal.pone.0137725

  </li>
  <li><strong>autism_kb_results.tar.gz</strong> (<em>asd_kang</em>): H: 20, ASD: 20
  <ul>
    <li>http://dx.doi.org/10.1371/journal.pone.0068322</li>
  </ul>
  </li>
  <li><strong>cdi_schubert_results.tar.gz</strong> (<em>noncdi_schubert</em>): H: 155, nonCDI: 89, CDI: 94
  <ul>
    <li>http://dx.doi.org/10.1128/mBio.01021-14</li>
  </ul>
  </li>
  <li><strong>cdi_vincent_v3v5_results.tar.gz</strong> (<em>cdi_vincent</em>): H: 25, CDI: 25
  <ul>
    <li>http://dx.doi.org/10.1186/2049-2618-1-18</li>
  </ul>
  </li>
  <li><strong>cdi_youngster_results.tar.gz</strong> (<em>cdi_youngster</em>): H: 4, CDI: 19
  <ul>
    <li>http://dx.doi.org/10.1093/cid/ciu135</li>
  </ul>
  </li>
  <li><strong>crc_baxter_results.tar.gz</strong> (<em>crc_baxter</em>): adenoma: 198, H: 172, CRC: 120
  <ul>
    <li>http://dx.doi.org/10.1186/s13073-016-0290-3</li>
  </ul>
  </li>
  <li><strong>crc_xiang_results.tar.gz</strong> (<em>crc_chen</em>): H: 22, CRC: 21
  <ul>
    <li>http://dx.doi.org/10.1371/journal.pone.0039743</li>
  </ul>
  </li>
  <li><strong>crc_zackular_results.tar.gz</strong> (<em>crc_zackular</em>): adenoma: 30, H: 30, CRC: 30
  <ul>
    <li>http://dx.doi.org/10.1158/1940-6207.CAPR-14-0129</li>
  </ul>
  </li>
  <li><strong>crc_zeller_results.tar.gz</strong> (<em>crc_zeller</em>): H: 75, CRC: 41
  <ul>
    <li>http://dx.doi.org/10.15252/msb.20145645</li>
  </ul>
  </li>
  <li><strong>crc_zhao_results.tar.gz</strong> (<em>crc_wang</em>): H: 56, CRC: 46
  <ul>
    <li>http://dx.doi.org/10.1038/ismej.2011.109}</li>
  </ul>
  </li>
  <li><strong>edd_singh_results.tar.gz</strong> (<em>edd_singh</em>): STEC: 28, CAMP: 71, SALM: 66, SHIG: 34, H: 75
  <ul>
    <li>http://dx.doi.org/10.1186/s40168-015-0109-2</li>
  </ul>
  </li>
  <li><strong>hiv_dinh_results.tar.gz</strong> (<em>hiv_dinh</em>): H: 16, HIV: 21
  <ul>
    <li>http://dx.doi.org/10.1093/infdis/jiu409</li>
  </ul>
  </li>
  <li><strong>hiv_lozupone_results.tar.gz</strong> (<em>hiv_lozupone</em>): H: 13, HIV: 25
  <ul>
    <li>http://dx.doi.org/10.1016/j.chom.2013.08.006</li>
  </ul>
  </li>
  <li><strong>hiv_noguerajulian_results.tar.gz</strong> (<em>hiv_noguerajulian</em>): H: 34, HIV: 206
  <ul>
    <li>https://doi.org/10.1016%2Fj.ebiom.2016.01.032</li>
  </ul>
  </li>
  <li><strong>ibd_alm_results.tar.gz</strong> (<em>ibd_papa</em>): IBDundef: 1, nonIBD: 24, UC: 43, CD: 23
  <ul>
    <li>http://dx.doi.org/10.1371/journal.pone.0039242</li>
  </ul>
  </li>
  <li><strong>ibd_engstrand_maxee_results.tar.gz</strong> (<em>ibd_willing</em>): CCD: 12, H: 35, ICD: 15, UC: 16, ICCD: 2
  <ul>
    <li>http://dx.doi.org/10.1053/j.gastro.2010.08.049</li>
  </ul>
  </li>
  <li><strong>ibd_gevers_2014_results.tar.gz</strong> (<em>ibd_gevers</em>): H: 31, CD: 224
  <ul>
    <li>http://dx.doi.org/10.1016/j.chom.2014.02.005</li>
  </ul>
  </li>
  <li><strong>ibd_huttenhower_results.tar.gz</strong> (<em>ibd_morgan</em>): H: 18, UC: 48, CD: 62
  <ul>
    <li>http://dx.doi.org/10.1186/gb-2012-13-9-r79</li>
  </ul>
  </li>
  <li><strong>mhe_zhang_results.tar.gz</strong> (<em>liv_zhang</em>): CIRR: 25, H: 26, MHE: 26
  <ul>
    <li>http://dx.doi.org/10.1038/ajg.2013.221</li>
  </ul>
  </li>
  <li><strong>nash_chan_results.tar.gz</strong> (<em>nash_wong</em>): H: 22, NASH: 16
  <ul>
    <li>http://dx.doi.org/10.1371/journal.pone.0062885</li>
  </ul>
  </li>
  <li><strong>nash_ob_baker_results.tar.gz</strong> (<em>nash_zhu</em>): H: 16, NASH: 22, OB: 25
  <ul>
    <li>http://dx.doi.org/10.1002/hep.26093</li>
  </ul>
  </li>
  <li><strong>ob_goodrich_results.tar.gz</strong> (<em>ob_goodrich</em>): OW: 322, H: 433, OB: 183
  <ul>
    <li>http://dx.doi.org/10.1016/j.cell.2014.09.053</li>
  </ul>
  </li>
  <li><strong>ob_gordon_2008_v2_results.tar.gz</strong> (<em>ob_turnbaugh</em>): H: 61, OB:
  

Periodontal disease (PD) is a chronic, progressive polymicrobial disease that induces a strong host immune response. Culture-independent methods, such as next-generation sequencing (NGS) of bacteria 16S amplicon and shotgun metagenomic libraries, have greatly expanded our understanding of PD biodiversity, identified novel PD microbial associations, and shown that PD biodiversity increases with pocket depth. NGS studies have also found PD communities to be highly host-specific in terms of both biodiversity and the response of microbial communities to periodontal treatment. As with most microbiome work, the majority of PD microbiome studies use standard data normalization procedures that do not account for the compositional nature of NGS microbiome data. Here, we apply recently developed compositional data analysis (CoDA) approaches and software tools to reanalyze multiomics (16S, metagenomics, and metabolomics) data generated from previously published periodontal disease studies. CoDA methods, such as centered log-ratio (clr) transformation, compensate for the compositional nature of these data, which can not only remove spurious correlations but also allows for the identification of novel associations between microbial features and disease conditions. We validated many of the studies’ original findings, but also identified new features associated with periodontal disease, including the genera Schwartzia and Aerococcus and the cytokine C-reactive protein (CRP). Furthermore, our network analysis revealed a lower connectivity among taxa in deeper periodontal pockets, potentially indicative of a more “random” microbiome. Our findings illustrate the utility of CoDA techniques in multiomics compositional data analysis of the oral microbiome.

This dataset was compiled for a Master's thesis project focused on investigating the gut microbiota response in fish exposed to microplastics. It contains cleaned and annotated metadata along with taxonomic abundance information and exposure features, prepared for predictive machine learning modeling.

Context Microplastics (MPs) are emerging pollutants in aquatic ecosystems. Numerous studies have shown that MPs can impact the gut microbial composition of fish. This dataset integrates data from multiple studies through a meta-analysis approach, standardized using bioinformatics and machine learning pipelines.

Source Sequences and metadata were extracted from public BioProject entries in the NCBI SRA database.

Data processing: QIIME2, Python (pandas, scikit-learn), Google Colab

Total size: ~648 FASTQ files → summarized into machine learning-ready tabular format

Applications Microbiome classification modeling

Environmental ecotoxicology analysis

Meta-analysis benchmarking

Feature importance and interpretability (SHAP, feature selection)

The global microbiome sequencing services market is experiencing robust growth, with a market size of $1.71 billion in 2025 and a projected Compound Annual Growth Rate (CAGR) of 6.70% from 2025 to 2033. This expansion is driven by several key factors. Advancements in sequencing technologies, such as Sequencing by Ligation (SBL), Sequencing by Synthesis (SBS), Shotgun Sequencing, and Targeted Gene Sequencing, are reducing costs and increasing throughput, making microbiome analysis more accessible for research and clinical applications. The rising prevalence of chronic diseases like gastrointestinal disorders, infectious diseases, CNS diseases, and cancer, coupled with a growing understanding of the microbiome's role in these conditions, fuels demand for these services. Furthermore, increasing investments in research and development, coupled with the growing adoption of personalized medicine approaches which leverage microbiome data for diagnosis and treatment, are significant drivers. Key market trends include the emergence of cloud-based microbiome analysis platforms, the development of novel bioinformatics tools for data interpretation, and the increasing integration of microbiome sequencing into clinical workflows. However, challenges remain, including the high cost of advanced sequencing technologies, the complexity of data analysis, and the lack of standardized protocols for microbiome research, which act as market restraints. The market is segmented by technology and application, with Sequencing by Synthesis (SBS) currently dominating the technology segment, and Gastrointestinal Diseases and Oncology leading the application segment. Geographically, North America and Europe currently hold significant market shares, driven by robust healthcare infrastructure and substantial research funding. The competitive landscape is characterized by a mix of established players and emerging companies, including ZIFO, Baseclear BV, Metabiomics, Zymo Research, Microbiome Insights Inc, CosmosID, Shanghai Realbio Technology (RBT) Co Ltd, Rancho Biosciences, Merieux Nutrisciences Corporations (Biofortis), Clinical Microbiomics AS, MR DNA, and Locus Biosciences (EPIBIOME), among others. These companies are actively engaged in developing innovative technologies, expanding their service offerings, and forging strategic partnerships to gain a competitive edge. The market is expected to witness increased consolidation and strategic acquisitions in the coming years. Future growth will be significantly influenced by the development of more accurate and cost-effective sequencing technologies, the expansion of clinical applications, the establishment of standardized data analysis pipelines, and the growing adoption of microbiome-based therapeutics. The Asia Pacific region presents a significant growth opportunity due to rising healthcare expenditure, increasing awareness of microbiome research, and a growing prevalence of chronic diseases. Continued research into the complex interplay between the microbiome and human health will undoubtedly shape the future trajectory of this rapidly expanding market, driving further innovation and market penetration across various geographical regions and application areas. This report provides a detailed analysis of the Microbiome Sequencing Services market, projected to reach multi-billion dollar valuations in the coming years. It examines market concentration, key trends, dominant segments, leading players, and significant recent developments. Recent developments include: November 2023: QIAGEN NV launched the Microbiome WGS (whole-genome sequencing) SeqSets which is a comprehensive Sample to Insight workflow designed to provide an easy-to-use solution that maximizes efficiency and reproducibility in microbiome research., June 2023: Zymo Research launched its full-length 16S sequencing service offering researchers high-quality, full-length 16S rRNA gene sequencing for microbiome analysis.. Key drivers for this market are: Huge Investment in Microbiome Research, Rise in Demand for NGS Services; Surge in Genomic Research and Widening Application Area of Microbiome Sequencing. Potential restraints include: Ethical and Legal Issues Related to Genome Sequencing, Lack of Skilled Technicians for NGS Data Analysis. Notable trends are: The Oncology Segment is Expected to Hold a Significant Market Share Over the Forecast Period.

Recent advances in bioinformatics have made high-throughput microbiome data widely available, and new statistical tools are required to maximize the information gained from these data. For example, analysis of high-dimensional microbiome data from designed experiments remains an open area in microbiome research. Contemporary analyses work on metrics that summarize collective properties of the microbiome, but such reductions preclude inference on the fine-scale effects of environmental stimuli on individual microbial taxa. Other approaches model the proportions or counts of individual taxa as response variables in mixed models, but these methods fail to account for complex correlation patterns among microbial communities. In this article, we propose a novel Bayesian mixed-effects model that exploits cross-taxa correlations within the microbiome, a model we call microbiome mixed model (MIMIX). MIMIX offers global tests for treatment effects, local tests and estimation of treatment effects on individual taxa, quantification of the relative contribution from heterogeneous sources to microbiome variability, and identification of latent ecological subcommunities in the microbiome. MIMIX is tailored to large microbiome experiments using a combination of Bayesian factor analysis to efficiently represent dependence between taxa and Bayesian variable selection methods to achieve sparsity. We demonstrate the model using a simulation experiment and on a 2 × 2 factorial experiment of the effects of nutrient supplement and herbivore exclusion on the foliar fungal microbiome of Andropogon gerardii, a perennial bunchgrass, as part of the global Nutrient Network research initiative. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Preliminary analysis report generated automatically by the iMAP to provide a summary of conserved taxonomy assigned to OTUs and the initial analysis of OTUs and taxa data. The preliminary analysis report was automatically saved in the “reports” folder as “report4_preliminary_analysis.html”. (HTML 20379 kb)

The Human Microbiome Sequencing market is experiencing robust growth, driven by the increasing understanding of the microbiome's role in human health and disease. Advancements in next-generation sequencing (NGS) technologies are significantly contributing to this expansion, offering higher throughput, reduced costs, and improved accuracy compared to traditional methods. The market is segmented by sequencing technology (NGS, traditional methods, others) and application (genome, metabolome, transcriptome, and other omics analyses). The rising prevalence of chronic diseases like inflammatory bowel disease (IBD) and colorectal cancer, coupled with growing demand for personalized medicine approaches, fuels market expansion. Furthermore, substantial investments in research and development, coupled with the increasing adoption of microbiome-based therapeutics, are propelling growth. Assuming a conservative CAGR of 15% (based on typical growth in related genomics markets) and a 2025 market size of $2.5 Billion, the market is projected to reach approximately $7 Billion by 2033. This growth is expected across all regions, with North America and Europe holding significant market shares initially, followed by a substantial rise in the Asia-Pacific region due to increasing healthcare investments and growing awareness. The market faces certain challenges, such as the high cost of sequencing, the complexity of data analysis, and regulatory hurdles for microbiome-based therapeutics. However, ongoing technological advancements, decreasing sequencing costs, and the development of user-friendly data analysis tools are expected to mitigate these limitations. The competitive landscape is characterized by a mix of large multinational corporations and specialized smaller companies, fostering innovation and ensuring a diverse range of services and technologies. The future of the Human Microbiome Sequencing market is bright, fueled by ongoing research, technological advancements, and the increasing recognition of the microbiome's importance in human health. This growth will likely be sustained by the development of novel diagnostic tools and therapeutic interventions targeting the microbiome.

According to Cognitive Market Research, the global Microbiome Sequencing Services market size will be USD 1529.8 million in 2025. It will expand at a compound annual growth rate (CAGR) of 11.50% from 2025 to 2033.

North America held the major market share for more than 40% of the global revenue with a market size of USD 566.03 million in 2025 and will grow at a compound annual growth rate (CAGR) of 9.3% from 2025 to 2033.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 443.64 million.
APAC held a market share of around 23% of the global revenue with a market size of USD 367.15 million in 2025 and will grow at a compound annual growth rate (CAGR) of 13.5% from 2025 to 2033.
South America has a market share of more than 5% of the global revenue with a market size of USD 58.13 million in 2025 and will grow at a compound annual growth rate (CAGR) of 10.5% from 2025 to 2033.
The Middle East had a market share of around 2% of the global revenue and was estimated at a market size of USD 61.19 million in 2025 and will grow at a compound annual growth rate (CAGR) of 10.8% from 2025 to 2033.
Africa had a market share of around 1% of the global revenue and was estimated at a market size of USD 33.66 million in 2025 and will grow at a compound annual growth rate (CAGR) of 11.2% from 2025 to 2033.
Sequencing by Synthesis category is the fastest growing segment of the Microbiome Sequencing Services industry

Market Dynamics of Microbiome Sequencing Services Market

Key Drivers for Microbiome Sequencing Services Market

Rising Prevalence of Chronic Diseases and Lifestyle Disorders to Boost Market Growth

The increasing incidence of chronic conditions such as obesity, diabetes, gastrointestinal disorders, and autoimmune diseases is a major driver of the microbiome sequencing services market. Research increasingly shows that gut microbiota plays a significant role in immune system regulation, metabolism, and inflammation pathways—critical factors in the development and progression of chronic illnesses. This has heightened the interest of healthcare providers and researchers in microbiome analysis to understand disease mechanisms, identify microbial biomarkers, and develop microbiome-targeted therapies. Additionally, lifestyle changes, poor dietary habits, and environmental exposures further disrupt the gut microbial balance, leading to demand for advanced diagnostic services. Microbiome sequencing enables high-resolution analysis of microbial diversity, composition, and function, helping to tailor personalized treatment plans. For instance, OraSure Technologies, under its Diversigen arm, introduced a service for gut microbiota sample metatranscriptomic sequencing and analysis, advancing capabilities in understanding microbiome dynamics for research and clinical applications.

https://orasure.com/

Advancements in Next-Generation Sequencing (NGS) Technologies To Boost Market Growth

Technological innovations, particularly in next-generation sequencing (NGS), are significantly accelerating the growth of microbiome sequencing services. Modern NGS platforms offer rapid, high-throughput, and cost-effective methods to analyze complex microbial communities with unmatched accuracy and depth. These advancements allow researchers to sequence millions of DNA fragments simultaneously, leading to comprehensive profiling of microbial genomes and their functional genes. Furthermore, the integration of bioinformatics and cloud-based data analysis tools enhances the interpretation of massive datasets generated through sequencing, enabling more meaningful insights into microbiome roles in health and disease.

Restraint Factor for the Microbiome Sequencing Services Market

High Cost of Sequencing and Data Analysis Will Limit Market Growth

The major restraining factor for the microbiome sequencing services market is the high cost associated with sequencing procedures and subsequent data analysis. Although the cost of sequencing technologies like 16S rRNA and whole-genome shotgun sequencing has decreased over time, it still remains substantial, particularly for large-scale or longitudinal studies. Additionally, the infrastructure required for sample processing, high-throughput sequencing platforms, and advanced bioinformatics tools significantly increases the overall project cost. Many small- to mid-sized research labs, clinical settings, or biotech st...

*******************************************************************
MetaDrugs workflow
*******************************************************************

Data analysis pipeline for investigating drug-host-microbiome relationships in cardiometabolic disease (MetaCardis cohort).

For questions and requests, please contact:
Sofia K. Forslund (sofia.forslund@mdc-berlin.de)
and Till Birkner (till.birkner@mdc-berlin.de)

*******************************************************************
Contents:
-------------------------------------------------------------------
Data files:
metadata.tar.gz - archived cohort metadata files*
input_features.tar.gz - archived preprocessed serum and urine metabolome and gut microbiome features
output_complete.tar.gz - archived example analysis output files for each of the input feature file
output_rerun.tar.gz - archived empty directory for generating test output files as described in this document

*Please note: Due to conflicts with Danish Data Protection laws, metadata from the Danish subset of the cohort were removed in this repository. Please reach out for a potential case-by-case access request for access to the complete set of metadata.
-------------------------------------------------------------------
Text files:
archived in feature_names.tar.gz:
atcs_names - full names for atcs drug compounds
contrast_names - full names for disease comparison groups
file_names - brief description of the files in input_features folder
gmm_names - full names of GMM modules
kegg_names - full names of KEGG modules
ko_names - full names of KO modules
metadata_names - full names of metadata features
mOTU_names - species names for metagenomics data
taxon_names - taxon names for metagenomics data
-------------------------------------------------------------------
Scripts:
-------------------------------------------------------------------
runFrame.r - main wrapper script envoking the analysis pipeline
-------------------------------------------------------------------
runFrame_rel_comb.r - script calculating drug combination effects
runFrame_rel.r - script calculating dosage effects
testCombPresenceSeparate.r - testing of significant drug combination effects beyond single drug effects
testDosagePresenceSeparate.pl - testing of significant drug dosage effects beyond single drug effects
testDosagePresenceSeparateNegative.pl - testing of unique drug dosage effects beyond single drug effects
-------------------------------------------------------------------
prettifyResults_uncollapsed.pl - wrapper scripts to create and format a single analysis output file
makeTables.r - wrapper script to make excel tables with analysis results
-------------------------------------------------------------------
Example output file:
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output_all_formatted_noc_uncollapsed_complete.tsv - contains all disease-drug-host-microbiome feature analysis results in one place.
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Gut Microbiome Sequencing Market Outlook

As per our latest research, the global gut microbiome sequencing market size reached USD 1.48 billion in 2024, reflecting robust momentum in the healthcare genomics sector. The market is expanding at a CAGR of 18.2% and is forecasted to achieve a value of USD 6.23 billion by 2033. This remarkable growth is driven by increasing awareness of the gut microbiome’s role in health and disease, technological advancements in sequencing platforms, and the rising prevalence of chronic diseases that necessitate precision diagnostics and personalized medicine approaches. As the importance of gut microbiome insights becomes more evident in clinical settings, demand for sequencing solutions continues to surge globally.

The primary growth factor propelling the gut microbiome sequencing market is the escalating prevalence of gastrointestinal disorders, metabolic diseases, and immune-related conditions. With a growing body of research linking the gut microbiome to a spectrum of diseases, from inflammatory bowel disease to diabetes and neurological disorders, healthcare providers are increasingly integrating microbiome sequencing into their diagnostic and therapeutic workflows. The integration of next-generation sequencing (NGS) technologies allows for high-throughput, accurate, and cost-effective analysis of microbial communities, enabling clinicians and researchers to uncover novel biomarkers, therapeutic targets, and personalized treatment regimens. This trend is further amplified by the surge in consumer interest in gut health, which is driving the adoption of microbiome sequencing in both clinical and consumer wellness settings.

Another significant driver is the rapid evolution and democratization of sequencing technologies. Innovations in 16S rRNA sequencing, shotgun metagenomic sequencing, and whole genome sequencing have drastically reduced the cost and turnaround time of microbiome analysis. This technological leap has made advanced sequencing accessible to a broader range of end-users, including academic research centers, hospitals, pharmaceutical and biotechnology companies, and even direct-to-consumer testing services. The introduction of user-friendly bioinformatics platforms and cloud-based data analytics further enhances the utility and scalability of microbiome sequencing, empowering stakeholders to derive actionable insights from complex datasets. As a result, the gut microbiome sequencing market is witnessing a surge in research collaborations, clinical trials, and commercial product launches.

Moreover, the global focus on personalized medicine and nutrition is catalyzing the adoption of gut microbiome sequencing across multiple sectors. Pharmaceutical and biotechnology companies are leveraging microbiome data for drug discovery, development of microbiome-based therapeutics, and patient stratification in clinical trials. Simultaneously, personalized nutrition companies are utilizing sequencing insights to develop tailored dietary recommendations, functional foods, and probiotic formulations. Regulatory agencies are also recognizing the clinical relevance of microbiome data, paving the way for standardized protocols and quality benchmarks. This multifaceted demand, coupled with supportive policy frameworks, is expected to sustain the market’s double-digit growth trajectory over the forecast period.

Regionally, North America remains the dominant force in the gut microbiome sequencing market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The region’s leadership is attributed to its advanced healthcare infrastructure, high R&D investments, and a strong presence of genomics companies. Meanwhile, Asia Pacific is emerging as a high-growth market, driven by rising healthcare expenditure, expanding research capabilities, and increasing government support for genomics initiatives. Europe continues to make significant strides through public-private partnerships and collaborative research projects focused on microbiome science. These regional dynamics underscore the global nature of the gut microbiome sequencing market and its far-reaching impact on healthcare innovation.

This dataset contains the outputs of a metagenomic Galaxy workflow run on the raw data of the project PRJNA977416, including the CSV file of associated metadata and the workflow.ga used for the analysis.

Firstly, it has information on taxonomic assignment with :

• the reports of all samples for Kraken2, Bracken, and MetaPhlan taxonomic profilers.
• two tabular files obtained with Taxpasta, which merge samples and standardize taxonomic abundances.
• for the Bracken standardised abundance, a file with the measures of alpha diversity calculated
• two HTML files giving access to the Krona diagram for this taxonomic composition.

Secondly, it contains functional informations with :

• a tabular file with the relative abundance of all GO terms for all samples
• a directory detailing pathways and genes families detected.

Common repository for diverse human microbiome datsets and minimum reporting standards for Common Fund Human Microbiome Project.