These guidelines were drafted by the authors.

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)

This dataset contains raw source data and scripts written to generate figures for the thesis entitled "Supporting data for “Computational analysis of shotgun metagenomic data from human gut microbiota". This includes raw species abundance tables used for statistical analysis and methods evaluation, clinical patient records and blood/stool biochemical measurements. Files are separated by their chapter contribution. Chapter 2 files are related to methods comparison and evaluation in gut metagenomics species abundance estimation. Chapter 3 primarily contains raw patient clinical data and their gut microbial compositions for statistical analysis. Chapter 4 contains files related to the scripts utilized to investigate the phenomenon of read coverage bias in human gut metagenomic data.

Multidimensional scaling (MDS) is a dimensionality reduction technique for microbial ecology data analysis that represents the multivariate structure while preserving pairwise distances between samples. While its improvements have enhanced the ability to reveal data patterns by sample groups, 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, “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 simulated compositional 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 preserving both local and ..., , # Multidimensional scaling informed by F-statistic: Visualizing grouped microbiome data with inference

• Dataset DOI: 10.5061/dryad.vmcvdnd3x
• Software: https://github.com/soob-kim/FinfoMDS
  (also in prep for Bioconductor submission)
• 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 3 folders and 1 file (see below).

1. Folder Simulated contains pairwise distances and ordination results from three simulated datasets. Includes 7 subfolders and 6 files.
   • Six files are the original dataset and its associated labels set. The names are formatted as "sim_<*x*>-<*type*>.*csv*" where <*x*> is the replicate number and <*type*> indicates whether the file is the design matrix ("data") or response vector ("Y").
   • Seven subfolders are grouped by the ordination method. Likewise, the file ...,

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

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.

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.

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

*******************************************************************
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:
-------------------------------------------------------------------
output_all_formatted_noc_uncollapsed_complete.tsv - contains all disease-drug-host-microbiome feature analysis results in one place.
*******************************************************************

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.

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.

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)

Snapshot img

Microbiomes Market Size and Forecast 2025-2029

The microbiomes market size estimates the market to reach by USD 824.3 million, at a CAGR of 18.3% between 2024 and 2029. North America is expected to account for 53% of the growth contribution to the global market during this period. In 201,9 the probiotics segment was valued at USD 117.80 million and has demonstrated steady growth since then.

    Report Coverage


    Details




    Base year


    2024




    Historic period

    2019-2023



    Forecast period


    2025-2029



    Market structure
    Fragmented



    Market growth 2025-2029


    USD 824.3 million






The market is experiencing significant growth, driven by the increasing prevalence of diseases that can be addressed through microbiome-based therapies. The potential of microbiomes in treating various health conditions, from gastrointestinal disorders to skin conditions, is fueling extensive research and development efforts. A noteworthy trend in this market is the growing number of collaborations between academic institutions, biotech companies, and pharmaceutical giants to develop microbiome therapeutics. However, the market is not without challenges. Manufacturing and formulation of microbiome therapeutic products pose significant obstacles. Ensuring the stability and viability of live microorganisms during production and delivery is a complex process that requires advanced technology and expertise.
Additionally, the need for standardization in microbiome research and product development is crucial to ensure consistent results and patient safety. Companies seeking to capitalize on this market's opportunities must address these challenges effectively through strategic partnerships, investments in research and development, and the adoption of innovative technologies.

What will be the Size of the Microbiomes Market during the forecast period?

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The microbiome market continues to evolve, driven by advancements in technology and growing applications across various sectors. Soil microbiome composition, for instance, is a burgeoning area of research, with next-generation sequencing enabling a deeper understanding of antimicrobial resistance genes and microbial diversity indices. Microbiome engineering is another promising field, with potential applications in microbiome-based diagnostics and therapeutics. Microbial community structure, including gut microbiota modulation, is a significant focus in human health. The Human Microbiome Project and metagenomic sequencing have led to the discovery of numerous microbial consortia and interactions networks. Functional microbiome profiling and phages in microbiome are also gaining attention, offering new insights into microbiome-host interaction and microbial metabolomics.

Environmental microbiome sampling and viral community profiling are essential components of microbiome research, providing valuable data for understanding microbial ecology studies and bacterial community dynamics. Bioinformatics pipelines are crucial for microbiome data analysis, ensuring accurate and efficient processing of vast amounts of data. Microbiome restoration strategies, such as personalized microbiome therapy and microbiome stability assessment, are becoming increasingly important in restoring balance to microbial communities. The market for microbiome therapeutics is expected to grow at a robust rate, with industry analysts projecting a 15% annual increase in demand for microbiome-targeted treatments. Probiotic efficacy testing and microbial biomass quantification are essential for assessing the impact of probiotics on microbial communities.

Phages in microbiome and microbial interactions networks are also critical areas of research, with potential applications in microbiome-based therapies and bioengineering. An example of the market's dynamism can be seen in the field of gut microbiota modulation. A recent study revealed that a specific probiotic strain increased the abundance of beneficial bacteria by 50% in patients with irritable bowel syndrome, highlighting the potential of microbiome-targeted therapies in improving health outcomes.

How is this Microbiomes Industry segmented?

The microbiomes industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Probiotics
  Foods
  Prebiotics
  Medical food
  Others


Application

  Therapeutics
  Diagnostics


Therapeutics

  Infectious
  Gastrointestinal
  Endocrine & Metabolic


Type

  BCT/FMT
  Live Biotherapeutics


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  Middle East and Africa

    UAE


  AP

The global microbiome sequencing services market, valued at $1.71 billion in 2025, is poised for robust growth, exhibiting a Compound Annual Growth Rate (CAGR) of 6.70% from 2025 to 2033. This expansion is driven by several key factors. The increasing prevalence of chronic diseases linked to gut microbiome imbalances, such as inflammatory bowel disease (IBD) and certain cancers, fuels demand for accurate and comprehensive microbiome analysis. Advancements in next-generation sequencing (NGS) technologies are making microbiome sequencing faster, more cost-effective, and higher-throughput, further accelerating market growth. Growing research into the microbiome's role in personalized medicine and drug development is also a significant driver, as companies seek to tailor treatments based on an individual's unique microbial profile. Furthermore, the rising awareness among consumers about gut health and its connection to overall well-being is boosting demand for microbiome testing services, contributing to market expansion across both clinical and direct-to-consumer sectors. The market's growth trajectory is expected to be influenced by several trends. The integration of artificial intelligence (AI) and machine learning (ML) in microbiome data analysis is leading to more accurate and insightful interpretations, enhancing the clinical utility of microbiome sequencing. The development of standardized protocols and data sharing initiatives within the research community will improve the reliability and comparability of microbiome studies. However, challenges remain, including the high cost of sequencing and analysis, the lack of standardized clinical guidelines for microbiome testing, and concerns surrounding data privacy and security. Despite these restraints, the continued technological advancements, expanding research efforts, and rising demand are projected to propel significant growth in the microbiome sequencing services market during the forecast period. The competitive landscape is dynamic, with established players such as Merieux Nutrisciences and Zymo Research alongside emerging companies like Locus Biosciences and Microbiome Insights vying for market share. 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: Huge Investment in Microbiome Research, Rise in Demand for NGS Services; Surge in Genomic Research and Widening Application Area of Microbiome Sequencing. Notable trends are: The Oncology Segment is Expected to Hold a Significant Market Share Over the Forecast Period.

Microbiomes play a critical role in host health, disease, and the environment.. Functional microbiome analysis which estimates the functional groups expressed by microbial community enables researchers to look beyond taxonomic composition and correlation with the condition under study. Using microbial community RNA-Seq data and subsequent metatranscriptomics workflows to elucidate the functional complement of the microbiome is gaining interest in the field. This tutorial from Galaxy training network will introduce researchers to the basic concepts of metatranscriptomics data analysis. It takes in paired-end datasets of raw shotgun sequences (in FastQ format) as an input and: preprocess extract and analyze the community structure (taxonomic information) extract and analyze the community functions (functional information) combine taxonomic and functional information to offer insights into taxonomic contribution to a function or functions expressed by a particular taxonomy. The dataset used in the tutorial comes from a time-serie analysis of a microbial community inside a bioreactor (Kunath et al, ISME, 2018). Only the data for one time point (1st) and a biological replicate (A) is analyzed here, after having been trimmed out the original file for the purpose of saving time and resources.

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:
  

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 NIH-funded Human Microbiome Project (HMP) is a collaborative effort of over 300 scientists from more than 80 organizations to comprehensively characterize the microbial communities inhabiting the human body and elucidate their role in human health and disease. To accomplish this task, microbial community samples were isolated from a cohort of 300 healthy adult human subjects at 18 specific sites within five regions of the body (oral cavity, airways, urogenital track, skin, and gut). Targeted sequencing of the 16S bacterial marker gene and/or whole metagenome shotgun sequencing was performed for thousands of these samples. In addition, whole genome sequences were generated for isolate strains collected from human body sites to act as reference organisms for analysis. Finally, 16S marker and whole metagenome sequencing was also done on additional samples from people suffering from several disease conditions.

Format of input files. Includes sample-metadata mapping (sheet 1), sample-read-file mapping in mothur-format (sheet2), and sample-variable mapping (sheet 3, 4 and 5). (XLSX 69 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.