These guidelines were drafted by the authors.

In order to test for the differential abundance of taxa that may drive the differences observed between inferred microbial communities derived from the different DNA isolation procedures, we performed DESeq2 analyses. Here we provide an example for such an analysis from human fecal specimen, examined using 16S rRNA gene profiling. This workflow relates to the article: Berith E. Knudsen, Lasse Bergmark, Patrick Munk, Oksana Lukjancenko, Anders Priemé, Frank M. Aarestrup, Sünje J. Pamp (2016) Impact of Sample Type and DNA Isolation Procedure on Genomic Inference of Microbiome Composition. mSystems Oct 2016, 1 (5) e00095-16; DOI: 10.1128/mSystems.00095-16

http://msystems.asm.org/content/1/5/e00095-16

These files are associated with the following publication:https://doi.org/10.1210/jendso/bvaa173

And the sequence data are available at the European Nucleotide Archive: PRJEB40801

This link contains the metadata, sequences reads, and analysis files used in the study "Alterations in gut microbiota do not play a causal role in diet-independent weight gain caused by ovariectomy."

Alpha_diversity files:
File: AlphaDiversity_analysis_sham_ovex
Description: R statistical analysis file for Faith's Phylogenetic Diversity (Faith's PD) and Observed
Sequence Variant (SV) alpha diversity metrics
File: faith_pd_sham_ovex
Description: QIIME2 output file for Faith's PD alpha diversity measurements for sham/ovex samples
File: obserevd_svs_sham_ovex
Description: QIIME2 output file for Observed SVs alpha diversity measurements for sham/ovex samples

Beta_diversity files:
File: BetaDiversity_analysis_sham_ovex
Description: R statistical analysis file for beta diversiy metrics
File: merged.sv.sham.ovex
Description: Combined SV table and taxa table for sham/ovex samples
File: sv.sham.ovex
Description: SV table for sham/ovex samples
File: table.sham.ovex.biom
Description: BIOM formated file for combined SV and taxa data. (For import into Phyloseq)
File: tax.sham.ovex
Description: Taxa table for sham/ovex samples
File: tree.nwk
Description: Phylogentic tree for sham/ovex data (For import into Phyloseq)

DeSeq2 Analysis files:
File: merged.sv.sham.ovex.trimmed
Description: Combined SV table and taxa table for sham/ovex samples. SVs found in 4 samples or less removed.
File: sv.table.sham.ovex.trimmed
Description: SV table for sham/ovex samples. SVs found in 4 samples or less removed.
File: sham.ovex.trimmed.biom
Description: BIOM formated file for combined SV and taxa data. SVs found in 4 samples or less removed.(For import into Phyloseq)
File: tax.sham.ovex.trimmed
Description: Taxa table for sham/ovex samples. SVs found in 4 samples or less removed.
File: tree.trimmed.nwk
Description: Phylogentic tree for sham/ovex data. SVs found in 4 samples or less removed. (For import into Phyloseq)
File: Phyloseq.DeSeq2.Ovex.Sham
Description: Log2 Fold change analysis (relative species abundance) done in DESeq2 for time points 1-5.
File: Phyloseq.DeSeq2.Ovex.Sham.week3
Description: Log2 Fold change analysis (relative species abundance) done in DESeq2 for time point 3.
File: Phyloseq.DeSeq2.Ovex.Sham.week4
Description: Log2 Fold change analysis (relative species abundance) done in DESeq2 for time point 4.
File: Phyloseq.DeSeq2.Ovex.Sham.week5
Description: Log2 Fold change analysis (relative species abundance) done in DESeq2 for time point 5.


Mapping_files including metadata (for use with sequences below):
File: ovex_mapping
Description: Mapping file - maps barcodes to samples
File: ovex_mapping_samples removed
Description: Mapping file - maps barcodes to reads. Two samples removed for low sequence count.
1. Plate2 A08 806rcbc103 GCG AGC GAA GTA CCG GAC TAC HVG GGT WTC TAA T 8 870 (T2) Ovex F
2. Plate2 C02 806rcbc121 GCA ATT AGG TAC CCG GAC TAC HVG GGT WTC TAA T 26 888 (T2) Co-Sham O
File: ovex_mapping_sham_ovex_samples removed
Description: Mapping file - maps barcodes to reads. Sham/ovex samples only. One sample removed for low sequence count.
1. Plate2 A08 806rcbc103 GCG AGC GAA GTA CCG GAC TAC HVG GGT WTC TAA T 8 870 (T2) Ovex F

QIIME2 Script:

File: QIIME2_sham_ovex
Description: This file includes the commands used in the QIIME2 pipeline.

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)

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:
  

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.

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)

The global human microbiome analysis market is experiencing robust growth, driven by increasing awareness of the gut-brain axis and the microbiome's role in health and disease. Advances in sequencing technologies, such as 16S rRNA sequencing and shotgun metagenomics, are enabling deeper understanding of microbial communities and their functional implications. This has fueled the development of personalized medicine approaches, including microbiome-based diagnostics and therapeutics, particularly in areas like inflammatory bowel disease, diabetes, and mental health. The market is segmented by application (hospitals, research institutes, pharmaceutical companies) and by type of sequencing technology (16S rRNA, shotgun metagenomics, metatranscriptomics, and others). Key players like Illumina, Qiagen, and others are driving innovation through the development of advanced sequencing platforms and bioinformatics tools. While the market faces challenges related to high cost and data interpretation complexity, the long-term outlook remains positive due to ongoing research, increasing clinical applications, and expanding collaborations between academia, industry, and healthcare providers. The substantial growth in the market is projected to continue through 2033, fueled by ongoing research, technological advances, and a growing understanding of the microbiome’s significance in human health. Regional growth is expected to be strong across North America, Europe, and Asia Pacific, driven by increased healthcare spending and early adoption of microbiome-based solutions. The market's growth is projected to be fueled by several factors, including the increasing prevalence of chronic diseases linked to gut microbiome imbalances, the development of novel therapeutic interventions targeting the microbiome, and the growing adoption of personalized medicine approaches. However, challenges remain in standardizing data analysis methods and ensuring regulatory approval for microbiome-based products. Nonetheless, significant investment in research and development, coupled with the potential for lucrative applications in disease prevention and treatment, indicates a substantial and sustained market expansion in the coming years. The growing demand for personalized diagnostics and therapeutics, alongside the continuous development of advanced sequencing technologies, is expected to be a key driver of this growth, leading to a market that is both innovative and rapidly expanding.

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.

The global human microbiome analysis market is experiencing robust growth, driven by advancements in sequencing technologies and a burgeoning understanding of the gut microbiome's impact on human health. The market, currently valued at approximately $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This significant expansion is fueled by several key factors. Increased investments in research and development are leading to more sophisticated analytical tools and a deeper understanding of the complex interactions within the human microbiome. The rising prevalence of chronic diseases, such as inflammatory bowel disease and type 2 diabetes, directly linked to gut microbiome imbalances, is creating substantial demand for diagnostic and therapeutic interventions. Furthermore, the growing adoption of personalized medicine approaches, tailored to individual microbiome profiles, is further accelerating market growth. The market is segmented by technology (16S rRNA Sequencing, Shotgun Metagenomics, Metatranscriptomics, and Others) and application (Hospitals and Research Institutes), reflecting the diverse methodologies and user base within the field. Major players like Illumina, Qiagen, and other specialized companies are actively contributing to technological advancements and market expansion through innovative product development and strategic partnerships. The market's growth trajectory is anticipated to remain strong throughout the forecast period. However, challenges such as the high cost of advanced sequencing technologies and the complexity of data analysis could potentially moderate the growth rate. Regulatory hurdles and data privacy concerns associated with handling sensitive genomic information also pose challenges. Nevertheless, the substantial potential for personalized healthcare interventions stemming from microbiome analysis and the continued expansion of research in this area suggest a positive long-term outlook for the human microbiome analysis market. The development of cost-effective, high-throughput sequencing technologies and user-friendly data analysis platforms are key factors that will continue to drive market growth in the coming years.

Gut microbiome profiling was performed using 16S rRNA sequencing after human subjects consumed different doses of a dietary fiber, partically hydrolyzed guar gum.

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

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)

Preterm birth (PTB) is defined as the birth of an infant before 37 weeks of gestational age. It is the leading cause of perinatal morbidity and mortality worldwide, with 15 million preterm births per year. Vaginal microbiome more specifically is known to change throughout pregnancy, and can even affect the fetus before delivery. This study presents the first meta-analysis of vaginal microbiome in preterm birth. This study integrated raw 16S ribosomal RNA vaginal microbiome data from three pregnancy related studies consisting of over 300 pregnant patients sampled longitudinally over the three trimesters. It was found that women who later have a preterm delivery, have a higher variance in their vaginal microbiome, with the largest, most significant difference between term and preterm patients being in samples collected during the first trimester. While several of the microbial genera have been reported previously, three of those nine microbial genera are newly reported here. New hypotheses emerging from such an integrative analysis can lead to novel diagnostics to identify women who are at higher risk for PTB and potentially inform new therapeutic interventions.

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.

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

This item contains supplemental data from the publication "Metagenomic analysis with strain-level resolution reveals fine-scale variation in the human pregnancy microbiome". It includes assemblies from the three body sites studied for all subjects, Genbank files for genome bins from vaginal samples, protein sequences in fasta format predicted from the original assemblies, gene and protein abundance data tables, and R code.

Additional file 24: Table S5. Comparison of the stratification of the AGP microbiomes between tmap and PAM based clustering.

Datasets to be used in the Carpentry-style lesson on [microbiota data analysis](https://scienceparkstudygroup.github.io/microbiome-lesson/).

Datasets from the paper: Zancarini, A., Echenique-Subiabre, I., Debroas, D. et al. Deciphering biodiversity and interactions between bacteria and microeukaryotes within epilithic biofilms from the Loue River, France. Sci Rep 7, 4344 (2017). https://doi.org/10.1038/s41598-017-04016-w. https://rdcu.be/b3CZh

The human gut microbiome has been linked to health and disease. Investigation of the human microbiome has largely employed 16S amplicon sequencing, with limited ability to distinguish microbes at the species level. Herein, we describe the development of Reference-based Exact Mapping (RExMap) of microbial amplicon variants that enables mapping of microbial species from standard 16S sequencing data. RExMap analysis of 16S data captures ~75% of microbial species identified by whole-genome shotgun sequencing, despite hundreds-fold less sequencing depth. RExMap re-analysis of existing 16S data from 29,349 individuals across sixteen regions from around the world reveals a detailed landscape of gut microbial species across populations and geography. Moreover, RExMap identifies a core set of fifteen gut microbes shared by humans. Core microbes are established soon after birth and closely associate with BMI across multiple independent studies. RExMap and the human microbiome dataset are presented as resources with which to explore the role of the human microbiome.