THIS RESOURCE IS NO LONGER IN SERVICE.Documented on April 14,2022. Database of comprehensive information on the approximately 600 prokaryote species that are present in the human oral cavity. The majority of these species are uncultivated and unnamed, recognized primarily by their 16S rRNA sequences. The HOMD presents a provisional naming scheme for the currently unnamed species so that strain, clone, and probe data from any laboratory can be directly linked to a stably named reference entity. The HOMD links sequence data with phenotypic, phylogenetic, clinical, and bibliographic information. Full and partial oral bacterial genome sequences determined as part of this project and the Human Microbiome Project, are being added to the HOMD as they become available. HOMD offers easy to use tools for viewing all publicly available oral bacterial genomes. Data is also downloadable.

This is the working version of the Fly Microbiome Diet Database, a compilation of published Drosophila diets used by laboratories in the field of fly microbiome research. In addition to listing dietary components as they are described in published research methods, we calculate macronutrient content and protein to carbohydrate ratio of each diet for the purpose of making comparisons between dietary nutrient content across studies. The source files for each diet component's nutrition facts are accessible at https://doi.org/10.6084/m9.figshare.11920743.v1.The database is subject to change as new studies are added or nutritional information is updated to be more accurate. In database, N.S. for Yeast or Cornmeal type means "not specified"; N/A means that ingredient was not used.

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:
  

Studying the microbial composition of internal organs and their associations with disease remains challenging due to the difficulty of acquiring clinical biopsies. We designed a statistical model to analyze the prevalence of species across sample types from The Cancer Genome Atlas (TCGA), revealing that species equiprevalent across sample types are predominantly contaminants, bearing unique signatures from each TCGA-designated sequencing center. Removing such species mitigated batch effects and isolated the tissue-resident microbiome, which was validated with original TCGA samples. "Mixed-evidence"species can be further distinguished by gene copy and nucleotide variants. We thus present The Cancer Microbiome Atlas (TCMA), a collection of curated, decontaminated microbial compositions of oropharyngeal, esophageal, gastrointestinal, and colorectal tissues. This led to discovery of prognostic species and blood signatures of mucosal barrier injuries, and enabled systematic matched microbe-host multi-omics analyses, which will help guide future studies of the microbiome's role in human health and disease. ... [Read More]

This dataset compiles all the data of the Seed Microbiota Database associated to the publication Simonin et al. 2021 (BioRxiv) Seed microbiota revealed by a large-scale meta-analysis including 50 plant species. This database includes metabarcoding data from 63 seed microbiota studies on 50 plant species ( total of 3190 seed samples) based on 5 different molecular markers (16S rRNA gene - V4 region, 16S rRNA gene - V5-V6 region, gyrB gene, ITS1 region, ITS2 region). All the studies were re-processed from the fastq files (raw data) using DADA2 and Qiime2 and merged in 5 different datasets depending on the molecular marker targeted. The README file presents the structure of the database (Subsets) and files available. This database can be queried online without downloading it on the Askomics instance : https://askomics-192-168-100-151.vm.openstack.genouest.org/ For a full access to the results, you can log to the Askomics instance with the following credentials: Username: consult Password: OcOU83D5

The inflammatory bowel diseases (IBD), which include Crohn’s disease (CD) and ulcerative colitis (UC), are multifactorial, chronic conditions of the gastrointestinal tract. While IBD has been associated with dramatic changes in the gut microbiota, changes in the gut metabolome -- the molecular interface between host and microbiota -- are less-well understood. To address this gap, we performed untargeted LC-MS metabolomic and shotgun metagenomic profiling of cross-sectional stool samples from discovery (n=155) and validation (n=65) cohorts of CD, UC, and non-IBD control subjects. Metabolomic and metagenomic profiles were broadly correlated with fecal calprotectin levels (a measure of gut inflammation). Across >8,000 measured metabolite features, we identified chemicals and chemical classes that were differentially abundant (DA) in IBD, including enrichments for sphingolipids and bile acids, and depletions for triacylglycerols and tetrapyrroles. While >50% of DA metabolite features were uncharacterized, many could be assigned putative roles through metabolomic “guilt-by-association” (covariation with known metabolites). DA species and functions from the metagenomic profiles reflected adaptation to oxidative stress in the IBD gut, and were individually consistent with previous findings. Integrating these data, however, we identified 122 robust associations between DA species and well-characterized DA metabolites, indicating possible mechanistic relationships that are perturbed in IBD. Finally, we found that metabolome- and metagenome-based classifiers of IBD status were highly accurate and, like the vast majority of individual trends, generalized well to the independent validation cohort. Our findings thus provide an improved understanding of perturbations of the microbiome-metabolome interface in IBD, including identification of many potential diagnostic and therapeutic targets.

There are abundant endophytic bacteria, fungi and actinomycetes in medicinal plants. The microorganisms of medicinal plants are inseparable from the growth, reproduction and metabolic activities of their host plants, which can not only affect the formation and content of medicinal components of plants, but also affect the authenticity of Chinese medicinal materials. Angelicae Sinensis Radix, Astragali Radix, Codonopsis Radix, Glycyrrhizae Radix et Rhizoma and Rhei Radix et Rhizoma are traditional Chinese medicinal materials and important sources of clinical medicine. In recent years, more and more research has been done on the microbiome of this medicinal plant. In order to integrate data resources and results of numerous studies and promote comparative studies, literature review and information extraction analysis were carried out, so as to construct a knowledge base of medicinal plant microbiome to assist the research of medicinal plant quality and authenticity. The database covers medicinal plant microorganisms by name, host plant, plant source in literature, classification, genus, family, order, class, phylum, function/biological role, technique, sequence length, NCBI reference serial number /GenBank, references and corresponding links. This interface supports the query function of the microbiome content of the above medicinal plants. Therefore, the database will help to provide a research basis for the development and utilization of the microbiome of medicinal plants and provide a reference for the creation of new methods for quality control and authenticity evaluation of medicinal plants.In Version 2, an additional 11 pieces of information have been incorporated for Codonopsis Radix to consider.In Version 3, the number of endophytes in the database was updated to 350.In Version 4, in order to distinguish the origin of host plants, category 'plant source in literature' was added. Meanwhile, the names of host plants were unified as the Latin names, and one duplicate data has been removed.In Version 5, we added a processing file for the data in MPMD, in which we counted the frequency of each endophyte and analyzed parameters such as the proportion of high-frequency endophytes occurring in the five traditional medicinal plants.In Version 6, we corrected the errors that appeared in the description of the past few versions and a new version of MPMD was provided.

The fasta file (BM_OTU.fasta) contain the sequences of the bacterial 16S rRNA-encoding V4 region gene (≈250 nt) for each Operational Taxonomic Unit (OTU). This dataset is associated with the following publication: Gomez-Alvarez, V., and R. Revetta. Monitoring of Nitrification in Chloraminated Drinking Water Distribution Systems With Microbiome Bioindicators Using Supervised Machine Learning. Frontiers in Microbiology. Frontiers, Lausanne, SWITZERLAND, 11: 2254-2267, (2020).

The Human Oral Microbiome Database (HOMD) provides a site-specific comprehensive database for the more than 600 prokaryote species that are present in the human oral cavity. It contains genomic information based on a curated 16S rRNA gene-based provisional naming scheme, and taxonomic information. This datatype contains taxonomic information.

The Hungry Microbiome is an exciting biomedical animation that takes the audience on a journey through the digestive tract to reveal the world of the human gut microbiome. The video explores how interactions between a human and their gut microbiota plays a key role in colorectal cancer risk. The dataset comprises a 4 minute mp4 video and a thumbnail.

Serofluid dish is one of the most popular probiotic fermented foods in northern China with a long history. It has not only unique flavor, but also rich nutrition, which is beneficial to human health. There are a lot of probiotics resources in Serofluid dish, such as Lactobacillus and Acetobacter are its dominant species. The lactic acid bacteria contained in it can promote gastrointestinal peristalsis, digestion and absorption after entering the human digestive tract; it also can reduce cholesterol and enhance the body's immunity. In Lanzhou, it is not only a local specialty representative of the region, but also a symbol of the local food culture, attracting a large number of domestic food lovers. Based on the systematic study of the microbial community structure of Serofluid dish, the separation and identification results of natural fermentation Serofluid dish samples collected from different geographical locations were summarized, and the database was compiled. The main contents of the database include: species and genera of culturable microorganisms isolated from Serofluid dish, and GenBank accession, corresponding media and other information.

Supporting data for "Role of the gut microbiome in mental health". The dataset serves as the supporting information and provides the raw data for the thesis "Role of the gut microbiome in mental health". This dataset includes nine files for sample metadata, microbiome taxonomy abundance, KO abundance, gut-brain modules abundance, gut metabolic modules abundance, metabolites concentration, and cytokine concentration data.

Database used in Sci Rep. 2021 Aug 2;11(1):15646. doi: 10.1038/s41598-021-95228-8.Bacteria list processed to contain only bacteria from the oral cavity.The original files (without processing) can be downloaded from the Human Oral Microbiome Database: HOMD (http://www.homd.org/)

The Human Microbiome Compendium is an ongoing project to build a large collection of human microbiome sequencing data processed with a uniform pipeline. Currently, the compendium contains 16S rRNA amplicon sequencing data for human gut microbiome samples retrieved from the Sequence Read Archive. Our website at microbiomap.org has more information about the project and links to related resources. This data is freely available under a CC-BY license; if you use it in your work, please cite our preprint, "Integration of 168,000 samples reveals global patterns of the human gut microbiome" (doi: 10.1101/2023.10.11.560955).

If you are using this dataset in conjunction with your own results, it's important to note that starting in version 1.0.1, the nomenclature used in this taxonomic table diverges from the output generated by DADA2 and the SILVA database. See the v1.0.1 release notes directly below for details.

Version history

1.0.1: The "asv_assignments" table was corrected to fix entries in which the taxonomic levels were incorrectly inferred from the reference database by DADA2 (e.g. genus "Brassicibacter" was listed as a family, genus "Gelria" was listed as an order). The problem is documented in issues attached to repositories for DADA2, DADA2 reference databases, and our MicroBioMap library. In short, problems were noted in v138 of the SILVA database in which taxonomic names were not recorded properly if they were missing levels (e.g. a taxon has been assigned a proposed genus, but not a family). This was addressed in v138.1, which we originally used for generating this dataset. However, several dozen entries remain incorrectly annotated in v138.1—our 1.0.1 release corrects these by filling in the nomenclature gaps with "(unclassified)" and moving the existing data to the correct level. 2881 ASV assignments were affected out of about 4.3 million. The new file "taxa_corrections.tsv" is a copy of the "bad-taxa.csv" list generated by Michael McLaren, with notes added to reflect what we changed.

1.0.0: Added README.md file to the repository, and added a link to the preprint and title/author metadata for the Zenodo entry

0.2.1: "sample_metadata.tsv" was missing (Note: This was accidentally tagged "0.2.0" in the version history.)

0.2.0: Replacing "country" column in sample_metadata.tsv with an "iso" column using the country code rather than name.

0.1.0: Prepping for public release

A database which provides ribosome related data services to the scientific community, including online data analysis, rRNA derived phylogenetic trees, and aligned and annotated rRNA sequences. It specifically contains information on quality-controlled, aligned and annotated bacterial and archaean 16S rRNA sequences, fungal 28S rRNA sequences, and a suite of analysis tools for the scientific community. Most of the RDP tools are now available as open source packages for users to incorporate in their local workflow.

Additional file 4. Table S1. Metagenenomes metadata. Table S2. Genomes metadata.

Viruses are increasingly viewed as vital components of the human gut microbiota, while their roles in health and diseases remain incompletely understood. Here, we first sequenced and analyzed the 37 metagenomic and 18 host metabolomic samples related to irritable bowel syndrome (IBS) and found that some shifted viruses between IBS and controls covaried with shifted bacteria and metabolites. Especially, phages that infect beneficial lactic acid bacteria depleted in IBS covaried with their hosts. We also retrieved public whole-genome metagenomic datasets of another four diseases (type 2 diabetes, Crohn’s disease, colorectal cancer, and liver cirrhosis), totaling 438 samples including IBS, and performed uniform analysis of the gut viruses in diseases. By constructing disease-specific co-occurrence networks, we found viruses actively interacting with bacteria, negatively correlated with possible dysbiosis-related and inflammation-mediating bacteria, increasing the connectivity between bacteria modules, and contributing to the robustness of the networks. Functional enrichment analysis showed that phages interact with bacteria through predation or expressing genes involved in the transporter and secretion system, metabolic enzymes, etc. We further built a viral database to facilitate systematic functional classification and explored the functions of viral genes on interacting with bacteria. Our analyses provided a systematic view of the gut virome in the disease-related microbial community and suggested possible positive roles of viruses concerning gut health.

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.

[Keywords] Market include Biomathematica, TrueBac, CoreBiome (OraSure), One Codex, CosmosID

Viruses are increasingly viewed as vital components of the human gut microbiota, while their roles in health and diseases remain incompletely understood. Here, we first sequenced and analyzed the 37 metagenomic and 18 host metabolomic samples related to irritable bowel syndrome (IBS) and found that some shifted viruses between IBS and controls covaried with shifted bacteria and metabolites. Especially, phages that infect beneficial lactic acid bacteria depleted in IBS covaried with their hosts. We also retrieved public whole-genome metagenomic datasets of another four diseases (type 2 diabetes, Crohn’s disease, colorectal cancer, and liver cirrhosis), totaling 438 samples including IBS, and performed uniform analysis of the gut viruses in diseases. By constructing disease-specific co-occurrence networks, we found viruses actively interacting with bacteria, negatively correlated with possible dysbiosis-related and inflammation-mediating bacteria, increasing the connectivity between bacteria modules, and contributing to the robustness of the networks. Functional enrichment analysis showed that phages interact with bacteria through predation or expressing genes involved in the transporter and secretion system, metabolic enzymes, etc. We further built a viral database to facilitate systematic functional classification and explored the functions of viral genes on interacting with bacteria. Our analyses provided a systematic view of the gut virome in the disease-related microbial community and suggested possible positive roles of viruses concerning gut health.