Skip to main content
Download PDF

Twnbiome: a public database of the healthy Taiwanese gut microbiome

BMC Bioinformatics volume 24, Article number: 474 (2023) Cite this article

Abstract

With new advances in next generation sequencing (NGS) technology at reduced costs, research on bacterial genomes in the environment has become affordable. Compared to traditional methods, NGS provides high-throughput sequencing reads and the ability to identify many species in the microbiome that were previously unknown. Numerous bioinformatics tools and algorithms have been developed to conduct such analyses. However, in order to obtain biologically meaningful results, the researcher must select the proper tools and combine them to construct an efficient pipeline. This complex procedure may include tens of tools, each of which require correct parameter settings. Furthermore, an NGS data analysis involves multiple series of command-line tools and requires extensive computational resources, which imposes a high barrier for biologists and clinicians to conduct NGS analysis and even interpret their own data. Therefore, we established a public gut microbiome database, which we call Twnbiome, created using healthy subjects from Taiwan, with the goal of enabling microbiota research for the Taiwanese population. Twnbiome provides users with a baseline gut microbiome panel from a healthy Taiwanese cohort, which can be utilized as a reference for conducting case-control studies for a variety of diseases. It is an interactive, informative, and user-friendly database. Twnbiome additionally offers an analysis pipeline, where users can upload their data and download analyzed results. Twnbiome offers an online database which non-bioinformatics users such as clinicians and doctors can not only utilize to access a control set of data, but also analyze raw data with a few easy clicks. All results are customizable with ready-made plots and easily downloadable tables. Database URL: http://twnbiome.cgm.ntu.edu.tw/.

Peer Review reports

Background

With the recent advancements in next generation sequencing (NGS) technology, the study of microbiota has been growing rapidly [1,2,3,4]. Traditionally, microbial genomics utilized cultivation-based methods to study genomes of microbes in the environment [5]. However, such traditional methods miss out on capturing much of the microbial diversity, as it is challenging to culture large groups of microorganisms properly. With the advent of 16S ribosomal RNA (16S rRNA) sequencing techniques, microbiota-related research, databases, and publications have all been growing exponentially [6]. Moreover, since 2008, with the initiation of the Human Microbiome Project (HMP), which has already released more than 2,200 microbial genome sequences isolated from various human body sites, including the feces, nasal cavity, throat, gut, and so on (https://portal.hmpdacc.org), microbiome research has undergone immense and rapid development [7].

Among all sites, the gut microbiota from the gastrointestinal tract are considered to be one of the biggest ensembles of microbial flora in the human body. The adult human gut contains about 1014 bacterial cells from more than 1,000 different bacterial species, which are involved in numerous human metabolic and physiological functions [8,9,10]. The host benefits from the homeostatic balance provided by the bacteria; however, a change in the microbial composition can lead to a severe imbalance between the beneficial and potentially pathogenic bacteria, thus making the gut vulnerable to microbial alterations. This imbalance, also known as “dysbiosis”, can cause physical symptoms or even diseases [11, 12], which have spawned a plethora of research endeavors towards unraveling the relationship between different diseases and microbiota [13,14,15,16]. Such studies aimed to provide descriptions of characteristic alterations in the composition of the microbiome that may prove useful as diagnostic biomarkers [17]. Furthermore, to elucidate the therapeutic potential of the human gut microbiome through manipulation, administration of a solution of fecal matter from a donor into the intestinal tract of a recipient has been used to directly change the recipient’s microbial composition in attempts to confer a health benefit [18,19,20]. This procedure, known as “fecal microbiota transplantation”, has already been extrapolated from animal models to human beings and has been used to successfully treat recurrent Clostridium difficile infection [21]. Preliminary findings indicate that it may also carry therapeutic potential for other conditions such as inflammatory bowel disease, obesity, metabolic syndrome, and functional gastrointestinal disorders. Therefore, microbiota have implications for human health, making them potential biomarkers for clinical applications.

It is well-known that the microbiome is diverse and heterogeneous in different individuals, even in the same tissue type. Several important factors, such as diet, disease phenotype, and environmental exposures, can have great impact on the abundance and composition of the microbiome [22, 23]. According to a recent study, the geographical location of the recruited subjects had a greater effect on human gut microbiota variations than disease phenotype in the constructed model [24]. This suggests that cross-national studies of potential microbiota biomarkers is not a good strategy. Therefore, it becomes challenging to identify consistent signatures as the geographic scale becomes larger, making it more difficult to establish a standard or a normal reference baseline that would be applicable globally [25]. Hence, as the microbiome segregated across ethnic and geographic populations could potentially lead to differences in disease severity [26, 27], developing population-specific databases containing the microbiome data from healthy individuals is a priority.

Furthermore, combining the demand for fast computation speed and large data storage, NGS analyses are mostly performed remotely on powerful servers using command-line interfaces. Therefore, the tools involved are often developed without a graphical user interface (GUI) to maximize computation efficiency. This kind of terminal-based environment imposes a considerable barrier, especially for biologists and clinicians not trained in bioinformatics. Furthermore, the output of most analysis tools is log files or other file formats that are friendly for computers to parse but may not be easily interpreted by humans without data cleaning and transformation. Therefore, for convenience of access and interpretation, to facilitate specific queries by users without advanced bioinformatics skills, creating a user-friendly database from metagenome resources is a current need in the field of microbiome research.

In this study we established the first public gut microbiota database, from healthy subjects in Taiwan, titled Twnbiome (http://Twnbiome.cgm.ntu.edu.tw), that enables microbiome research for populations in Taiwan. Researchers can easily access and compare the composition of microbiota from diseased subjects using Twnbiome as the healthy reference dataset. Twnbiome further provides a 16S rRNA analysis pipeline and statistically comparable metrics with a GUI, for easy access and convenient interpretation.

Construction and content

Database overview

Figure 1 provides an overview of the Twnbiome database. The database primarily offers two main utilities: (i) Twnbiome healthy baseline and (ii) User uploaded data analysis. The Twnbiome database provides users with information regarding the microbiota composition of healthy Taiwanese subjects. The users can further explore all information under different classifications such as gender, age, body mass index (BMI), and so on (Table 1), through 3 different functions: (a) overview, (b) summary, and (c) browser (Fig. 2). The ‘Data Analysis’ utility requires users to upload their 16S ribosomal RNA (rRNA) sequence data, where the built-in bioinformatics pipeline is utilized to conduct analysis. Twnbiome was developed with Django 2.2 and runs on Python 3.6.7 and MySQL 5.7.29.

Fig. 1
figure 1

Overview of the Twnbiome database

Full size image
Table 1 Demographic characteristics of 119 healthy Taiwanese subjects in Twnbiome
Full size table
Fig. 2
figure 2

Screenshot of the welcome page of the Twnbiome database

Full size image

Database content

Twnbiome provides a comprehensive gut microbiome landscape of 119 healthy subjects from Taiwan. The healthy volunteers were recruited via the Health Management Center of National Taiwan University Hospital (NTUH), and all participants provided informed consent during their routine health checkups. Fecal samples were collected from each volunteer and all information from the subjects was recorded by a trained interviewer. The study was approved by the institutional review board of NTUH (#201801085RINB).

Subject inclusion and clinical information

Individuals aged 18 years and above, who self-reported as healthy and were of Taiwanese Han Chinese ancestry, were recruited. Clinical information from all subjects was obtained from NTUH. Additionally, each subject was asked to self-report information on their physical activity, meal timings, probiotic/nutritional supplement intake, diet type, frequency of eating out, and sleep/wake regularity (lifestyle), through a customized questionnaire. Participants self-reported as healthy if they did not have any of the following diseases: autoimmune disease (rheumatoid arthritis, systemic lupus erythematosus, ankylosing spondylitis, hyper/hypothyroidism, psoriasis, type I diabetes, and multiple sclerosis), neuropsychiatric disease (mood disorders, schizophrenia, autism spectrum disorder), gastrointestinal disease (diarrhea), metabolic diseases (hypertension, atherosclerosis, type II diabetes, non-alcoholic fatty acid), cancers, or other major illnesses. Other basic demographic information including age, sex, and body mass index (BMI) was reported. Considering the potential effects of BMI on microbiota, subjects with BMI < 18.5 (underweight) or BMI ≥ 27 (obese) were excluded from the study. Lastly, subjects with recent drug usage (less than 3 months), including antibiotics, anti-hypertensives, hypolipidemic agents, steroids, antipsychotic and gastrointestinal drugs, were further excluded. Finally, 213 subjects passed all inclusion criteria, out of which 119 fulfilled the criteria of healthy subjects (Table 1).

Biosample collections and 16s rRNA sequencing

One-gram fecal samples were collected with user-friendly sterilized kits, and the sample transition process was performed by transferring the samples to -80 ℃ refrigerators immediately, for undergoing cold-chain transportation. QIAamp DNA Stool Mini Kit (QIAGEN Inc., USA) was used to extract the microbiota DNA via lysis, and purification procedures were conducted following the manufacturer’s protocol. For quality control, all DNA samples were evaluated by NanoDrop Microvolume Spectrophotometers. Sequencing of 16S rRNA was performed with the Illumina HiSeq platform, and library preparation was done according to the manufacturer’s instructions (Illumina, USA). Briefly, 12.5 ng of DNA was used for PCR (polymerase chain reaction) amplification of the V3 and V4 regions of the 16S rRNA gene.

The PCR products were purified with AMPure XP beads (Beckman Coulter, USA) and subjected to a secondary PCR reaction with primers from the Nextera XT Index kit (Illumina, USA) by adding dual indices and Illumina sequencing adapters. The final libraries (~ 630 bp) were purified with AMPure XP beads and sequenced by the Illumina HiSeq machine with paired-end sequencing (2*300 bp).

Bioinformatic analysis

Once the 16S rRNA sequencing experiments were complete, FASTQ files were generated by HiSeq Report and sequence quality control was conducted using FastQC software (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) on the raw FASTQ files. For further analysis, the analysis pipeline in Quantitative Insights Into Microbial Ecology (QIIME, version 1.9.1) was used [28]. First, PEAR (version 0.9.8) merged the paired-end reads [29], where the reads that did not overlap with more than 30 bp were discarded. The merged sequences then underwent the filtering step of QIIME for quality control with the following criteria: (1) maximum 3 consecutive low-quality base calls were allowed before truncating the reads, (2) at least 75% of consecutive high-quality base calls were included in a read, and (3) no ambiguous characters were allowed in the sequence, while setting < Q20 as the low quality threshold. After filtering, the sequences were clustered with USEARCH [30] to identify the operational taxonomic units (OTUs) with 97% similarity to the reference from the Greengenes taxonomic database (May 2013 version, http://greengenes.lbl.gov/). Then the OTUs were classified into different taxonomic levels: kingdom, phylum, class, order, family, genus, and species. The OTU information was then used to calculate the relative abundance and the bacterial diversity using alpha and beta diversity indexes. The alpha diversity was mainly calculated using the observed OTUs, whereas the beta diversity was calculated using two different methods: unweighted and weighted UniFrac. Furthermore, the enterotype was determined by previously established methods [31].

Features of Twnbiome database

Figure 2 provides a screen shot of the welcome page of the Twnbiome database (http://Twnbiome.cgm.ntu.edu.tw). The content of the database is offered to users through various tabs on the navigation bar. The information includes (1) exclusion criteria of our recruited subjects; (2) the basic quality-control information for the sequencing data, including raw reads, effective reads, and Q30 value; (3) detailed sample information and classifications using pie charts; (4) beta diversity plots; (5) detailed taxonomic composition under different classifications; and (6) browsers for specific samples and taxa searches. Details of the sample exclusion criteria are provided under the “About” tab. The “Overview” tab includes quality control distribution plots and gut microbiota composition pie charts for healthy subjects, with classifications based on gender, age, BMI, physical activity, alpha-diversity phylum level, enterotypes, probiotic and supplement intake, diet type, meal on-time frequency, frequency of eating out, and lifestyle. Each pie chart is coded with an interactive function that allows users to see the exact number of the recruited subjects. The "Overview" section further offers the beta diversity principal co-ordinate analysis (PCoA) plot of the 119 healthy subjects, to visualize similarities or dissimilarities between the samples. The PCoA plots are also interactive, can be rotated or zoomed in/out, and can be modified by adding legends. In the “Summary” section, a detailed taxonomic composition, from phylum to genus, among the groups with different gender or BMI is provided. Users can use the drop-down list to choose the taxa of interest to obtain the detailed composition. Finally, in the “Browser” section, specific taxa searches are offered where the users can directly type the name of the taxa (Kingdom, Phylum, Class, Order, Family, Genus, and Species) that they are interested in to explore the average and median abundance among the healthy subjects.

16S rRNA sequencing data upload and analysis

For the advancement of metagenomic research among the Taiwanese population, the database further offers a user-friendly interface in the “Tools” section for users to analyze their own data. In the upload data section, the user is required to upload their FASTQ files, and when the analysis is done, the user receives a link to the results for them to explore and download. All analyses are conducted using the same protocol as described before in the ‘methods’ section.

Utility and discussion

Subject demography

Table 1 gives full demographic details of the subjects in the database. The majority of participants were female (68.07%) and the age of the participants ranged between 18 and 94 years, with 55 adults, 45 middle aged (45 to < 65), and 19 old aged (65 to < 95) participants with a mean age of 47.1 years. Participants were further stratified based on their BMI, physical activity, dietary habits, and lifestyle/sleep patterns. A majority of subjects were in the normal range for BMI (68.07%), had never used probiotics/nutritional supplements (57.98%), reported a balanced diet (59.66%) with regular meal times (51.26%), and had abnormal sleep patterns (58.82%).

Twnbiome database

Twnbiome was created with the expectation of enhancing microbiome research in Taiwan and facilitating the improvement of patients suffering from microbial-related health problems. Background information on the project and the subject inclusion/exclusion criteria are provided in the “About” section of the web site (Fig. 2).

Overview section

This section covers the quality plots for the sequencing reads (Fig. 3a), pie charts giving a pictorial description of subject demography as described before (Fig. 3b) under different classifications, a sample browser for users to stratify samples based on the demographics and lifestyle factors and beta diversity plots depicting the amount of species change among subjects with various stratifications (Fig. 3c). The average number of raw reads per sample was 169,252, the average Q30 distribution was 87.42%, and the average number of effective reads was 120,666. Overall, the most abundant phylum among healthy Taiwanese subjects was Firmicutes (51.76%) followed by Bacteroidetes (35.25%) (Fig. 3b). The dominating presence of Firmicutes and Bacteroidetes in the gut was consistent with prior findings from studies focusing on healthy microbiome composition [32, 33]. Specifically, when stratified by enterotype composition, it was found that the healthy subjects were segregated into three different enterotypes enriched with Bacteroides, Prevotella, and Ruminococcus (Fig. 3b), again consistent with prior enterotype research findings [34, 35]. Furthermore, the beta diversity PCoA plots for the study subjects displayed no visible significant clusters when stratified by age (Fig. 3c) or any other variable (results not shown), indicating the homogeneity of the healthy subjects that were recruited for this study.

Fig. 3
figure 3

Screenshot of the “Overview” section of the Twnbiome database. a Quality plots for the sequencing reads. b Pie charts giving a pictorial description of subject-demography under different classifications. c Beta diversity plots depicting the amount of species change among subjects with various stratifications

Full size image

Summary section

Figure 4a displays a screenshot of the distribution of microbiome abundance at the genus level for the study subjects when stratified by sex and BMI. These figures can be accessed under the “Summary” section of the Twnbiome web site. The user can similarly select any of the other taxonomic levels (phylum, class, order, or family) to get the distribution of microbiome abundance for both sex and BMI stratification. The user can move the mouse over each of the colored bars to view the name of the corresponding microbiome and its relative abundance. Bacteroides, Faecalbacterium, Acidobacteria, Chlostridiales, Lachnospiraceae, Prevotella, Megamonas, Blautia, Phascolarotobacterium, Megasphaera, Ruminococcus, and Bifidobacterium were among the most abundant genera that were observed in both males/females and normal/overweight groups.

Fig. 4
figure 4

Screen shots of “Summary” and “Browser” sections of the Twnbiome database. a “Summary” section showing distribution of microbiome abundance at the genus level for the study subjects when stratified by BMI and sex. b “Browser” section, showing taxa with average abundance and median abundance

Full size image

Browser section: example 1

Figure 4b provides a screenshot of the “Browser” section of the Twnbiome database. The figure shows the search results for Bacteroides at the genus level, ranked by average abundance. The web page displays both average abundance and median abundance for Bacteroides as > 20%, consistent with the findings in the “Summary” section, thus making it the most abundant genera among healthy Taiwanese subjects. Furthermore, Plebeius and Uniformis, as the top abundant species for genus Bacterioides, have been found to exist in the guts of healthy Asian individuals and have been extracted for various research purposes in prior studies [36,37,38].

Data analysis: example 2

Figure 5 gives an example of the procedure for conducting data analysis on a user-uploaded data, along with the analysis output. The user needs to first click on the “Tools” link at top-right corner of the welcome page (Fig. 2), and will be taken to a data upload page (Fig. 5a) where the user is required to upload an e-mail address and the paired-end FASTQ files. On clicking the submit button, the user will be forwarded to the next page (Fig. 5b), which provides a notification of the status of the submission. Once the analysis is complete the user will receive an e-mail with a link to download the results of the analysis. The results include the information of the data analyzed (Fig. 5c) along with an OTU table containing the taxa information and the corresponding relative abundance (Fig. 5d). Users can download the results as .xls or .csv tables with a single click.

Fig. 5
figure 5

Screen shot displaying an example of the Analysis function of the Twnbiome database. Users can download the results as .xls or .csv tables with a single click. a Data upload page where the user is required to upload his/her e-mail address and the paired-end FASTQ files. b Screenshot of the page notifying users of the status of their submission. c Details of the analysis sent to the users via e-mail. d Screenshot of the results: an OTU table with taxonomic information and the corresponding relative abundance

Full size image

Research on microbiota is on the rise like never before. There are more than 50,000 research articles, just on the gut microbiome, in the Web of Science database since 2000, and as of December 18, 2019, there are already more than 9,500 publications in 2019, a growth of about 30-fold since 2000, sixfold since 2010, and twofold since 2015 [39]. A search in the Web of Science database further reveals a total of 16,716 research articles since 2020 and a total of 5609 publications in 2023 alone (as of 13th November 2023). With such a huge accumulation of research, it is common knowledge that microbiotic composition is affected by many factors, including diet, geographic location, genetics, antibiotics, lifestyle, and so on. Therefore, for conducting microbiome studies under a case-control design, it is very important to have the right comparative groups to explore the potential targets. This along with recent advances in high-throughput sequencing techniques at affordable costs, has led to an enormous amount of gut microbiome data being generated and curated [40]. One significantly important human gut microbiome database is GMrepo [41, 42], which is manually curated from 33 sources with special focus on disease markers. It allows cross-dataset or cross phenotype comparisons of identified markers enabling systematic demonstration of consistent and inconsistent disease-associated microbial markers across datasets. Human gut microbiome atlas (HGMA) (https://www.microbiomeatlas.org/) is another repository that contains health and disease datasets from 20 different countries across five continents and provides region enriched microbial species for different geographical locations. However, both GMrepo and HGMA yet lacks an analysis platform, which can enable users to upload data to conduct metagenomics analyses. Twnbiome is one of the first public databases that shares the microbiota information from more than a hundred healthy Taiwanese individuals and provides population specific enriched species of the gut microbiota. The database, which is still growing, can not only be used to query and obtain summary level data based on phylum, class, order, family, genus, species, but it is the first time that the comprehensive summary statistics of microbiome diversity related to age, sex, lifestyle, and other factors are provided for healthy Taiwanese individuals. Users can further customize the figures and results through interactive platforms for research purposes. It is an unprecedented effort in microbiome research with accurate data acquisition, an established pipeline for data analyses, and the ability to organize, store, access, and share/integrate processed datasets. The release of the database would firmly accelerate microbiota research in Taiwan.

Conclusion

Twnbiome provides users with a baseline gut microbiome panel from a healthy Taiwanese cohort, which can be utilized as a reference control for conducting case-control studies for a variety of diseases. Twnbiome is an interactive, informative, and user-friendly database that not only provides users with a control dataset, ready to be utilized, but also offers an analysis pipeline, which non-bioinformatics users such as clinicians and doctors can utilize to conduct their analysis with a few clicks, and obtain customizable ready-made plots and easily downloadable tables.

Data availability

The database contains all summary statistics and analyzed findings. The raw data can be available on reasonable request from the corresponding author.

Abbreviations

NGS:

Next generation sequencing

rRNA:

Ribosomal RNA

HMP:

Human microbiome project

GUI:

Graphical user interface

NTUH:

National Taiwan University Hospital

BMI:

Body mass index

QIIME:

Quantitative insights into microbial ecology

OTU:

Operational taxonomic units

PCoA:

Principal co-ordinate analysis

References

  1. Venter JC, Remington K, Heidelberg JF, Halpern AL, Rusch D, Eisen JA, et al. Environmental genome shotgun sequencing of the Sargasso Sea. Science. 2004;304(5667):66–74.

    Article  CAS  PubMed  Google Scholar 

  2. Thomas T, Gilbert J, Meyer F. Metagenomics-a guide from sampling to data analysis. Microb Inform Exp. 2012;2(1):1–12.

    Article  Google Scholar 

  3. Jovel J, Patterson J, Wang W, Hotte N, O’Keefe S, Mitchel T, et al. Characterization of the gut microbiome using 16S or shotgun metagenomics. Front Microbiol. 2016;7:459.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Caporaso JG, Lauber CL, Costello EK, Berg-Lyons D, Gonzalez A, Stombaugh J, et al. Moving pictures of the human microbiome. Genome Biol. 2011;12(5):1–8.

    Article  Google Scholar 

  5. Hugenholtz P, Goebel BM, Pace NR. Impact of culture-independent studies on the emerging phylogenetic view of bacterial diversity. J Bacteriol. 1998;180(18):4765–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Jones S. Trends in microbiome research. Nature Publishing Group; 2013.

    Book  Google Scholar 

  7. Turnbaugh PJ, Ley RE, Hamady M, Fraser-Liggett CM, Knight R, Gordon JI. The human microbiome project. Nature. 2007;449(7164):804–10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Quigley EM. Gut bacteria in health and Disease. Gastroenterol Hepatol (N Y). 2013;9(9):560.

    PubMed  Google Scholar 

  9. DeGruttola AK, Low D, Mizoguchi A, Mizoguchi E. Current understanding of dysbiosis in Disease in human and animal models. Inflamm Bowel Dis. 2016;22(5):1137–50.

    Article  PubMed  Google Scholar 

  10. Clarke G, Stilling RM, Kennedy PJ, Stanton C, Cryan JF, Dinan TG. Minireview: gut microbiota: the neglected endocrine organ. Mol Endocrinol. 2014;28(8):1221–38.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Moos WH, Faller DV, Harpp DN, Kanara I, Pernokas J, Powers WR, et al. Microbiota and neurological disorders: a gut feeling. BioRes open Access. 2016;5(1):137–45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Tamboli CP, Neut C, Desreumaux P, Colombel JF. Dysbiosis in inflammatory bowel Disease. Gut. 2004;53(1):1–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Stecher B, Hardt W-D. The role of microbiota in Infectious Disease. Trends Microbiol. 2008;16(3):107–14.

    Article  CAS  PubMed  Google Scholar 

  14. Round JL, Mazmanian SK. The gut microbiota shapes intestinal immune responses during health and Disease. Nat Rev Immunol. 2009;9(5):313–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Scheperjans F, Aho V, Pereira PA, Koskinen K, Paulin L, Pekkonen E, et al. Gut microbiota are related to Parkinson’s Disease and clinical phenotype. Mov Disord. 2015;30(3):350–8.

    Article  PubMed  Google Scholar 

  16. Kamada N, Seo S-U, Chen GY, Núñez G. Role of the gut microbiota in immunity and inflammatory Disease. Nat Rev Immunol. 2013;13(5):321–35.

    Article  CAS  PubMed  Google Scholar 

  17. Berry D, Reinisch W. Intestinal microbiota: a source of novel biomarkers in inflammatory bowel Diseases? Best Pract Res Clin Gastroenterol. 2013;27(1):47–58.

    Article  CAS  PubMed  Google Scholar 

  18. Kang D-W, Adams JB, Gregory AC, Borody T, Chittick L, Fasano A, et al. Microbiota transfer therapy alters gut ecosystem and improves gastrointestinal and autism symptoms: an open-label study. Microbiome. 2017;5(1):1–16.

    Article  Google Scholar 

  19. Smits LP, Bouter KE, de Vos WM, Borody TJ, Nieuwdorp M. Therapeutic potential of fecal microbiota transplantation. Gastroenterology. 2013;145(5):946–53.

    Article  PubMed  Google Scholar 

  20. Gupta S, Allen-Vercoe E, Petrof EO. Fecal microbiota transplantation: in perspective. Th Adv Gastroenterol. 2016;9(2):229–39.

    Article  Google Scholar 

  21. Youngster I, Russell GH, Pindar C, Ziv-Baran T, Sauk J, Hohmann EL. Oral, capsulized, frozen fecal microbiota transplantation for relapsing Clostridium difficile Infection. JAMA. 2014;312(17):1772–8.

    Article  CAS  PubMed  Google Scholar 

  22. Zmora N, Suez J, Elinav E. You are what you eat: diet, health and the gut microbiota. Nat Rev Gastroenterol Hepatol. 2019;16(1):35–56.

    Article  CAS  PubMed  Google Scholar 

  23. Rothschild D, Weissbrod O, Barkan E, Kurilshikov A, Korem T, Zeevi D, et al. Environment dominates over host genetics in shaping human gut microbiota. Nature. 2018;555(7695):210–5.

    Article  CAS  PubMed  Google Scholar 

  24. He Y, Wu W, Zheng H-M, Li P, McDonald D, Sheng H-F, et al. Regional variation limits applications of healthy gut microbiome reference ranges and Disease models. Nat Med. 2018;24(10):1532–5.

    Article  CAS  PubMed  Google Scholar 

  25. Gupta VK, Paul S, Dutta C. Geography, ethnicity or subsistence-specific variations in human microbiome composition and diversity. Front Microbiol. 2017;8:1162.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Deschasaux M, Bouter KE, Prodan A, Levin E, Groen AK, Herrema H, et al. Depicting the composition of gut microbiota in a population with varied ethnic origins but shared geography. Nat Med. 2018;24(10):1526–31.

    Article  CAS  PubMed  Google Scholar 

  27. King CH, Desai H, Sylvetsky AC, LoTempio J, Ayanyan S, Carrie J, et al. Baseline human gut microbiota profile in healthy people and standard reporting template. PLoS ONE. 2019;14(9):e0206484.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Lawley B, Tannock GW. Analysis of 16S rRNA gene amplicon sequences using the QIIME software package. Berlin: Springer Oral Biology; 2017. pp. 153–63.

    Google Scholar 

  29. Zhang J, Kobert K, Flouri T, Stamatakis A. PEAR: a fast and accurate Illumina paired-end reAd mergeR. Bioinformatics. 2014;30(5):614–20.

    Article  CAS  PubMed  Google Scholar 

  30. Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26(19):2460–1.

    Article  CAS  PubMed  Google Scholar 

  31. Le Chatelier E, Nielsen T, Qin J, Prifti E, Hildebrand F, Falony G, et al. Richness of human gut microbiome correlates with metabolic markers. Nature. 2013;500(7464):541–6.

    Article  PubMed  Google Scholar 

  32. Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010;464(7285):59–65.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Huttenhower C, Gevers D, Knight R, Abubucker S, Badger JH, Chinwalla AT, et al. Structure, function and diversity of the healthy human microbiome. Nature. 2012;486(7402):207.

    Article  CAS  Google Scholar 

  34. Arumugam M, Raes J, Pelletier E, Le Paslier D, Yamada T, Mende DR, et al. Enterotypes of the human gut microbiome. Nature. 2011;473(7346):174–80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Costea PI, Hildebrand F, Arumugam M, Bäckhed F, Blaser MJ, Bushman FD, et al. Enterotypes in the landscape of gut microbial community composition. Nat Microbiol. 2018;3(1):8–16.

    Article  CAS  PubMed  Google Scholar 

  36. Wang T, Cai G, Qiu Y, Fei N, Zhang M, Pang X, et al. Structural segregation of gut microbiota between Colorectal cancer patients and healthy volunteers. ISME J. 2012;6(2):320–9.

    Article  CAS  PubMed  Google Scholar 

  37. Benítez-Páez A, Gómez del Pulgar EM, Sanz Y. The glycolytic versatility of Bacteroides uniformis CECT 7771 and its genome response to oligo and polysaccharides. Front Cell Infect Microbiol. 2017;7:383.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Kitahara M, Sakamoto M, Ike M, Sakata S, Benno Y. Bacteroides plebeius sp. nov. and Bacteroides coprocola sp. nov., isolated from human faeces. Int J Syst Evol Microbiol. 2005;55(5):2143–7.

    Article  CAS  PubMed  Google Scholar 

  39. Li D, Gao C, Zhang F, Yang R, Lan C, Ma Y et al. Seven facts and five initiatives for gut microbiome research. Protein Cell. 2020:1–10.

  40. Sengupta P, Sivabalan SKM, Mahesh A, Palanikumar I, Kuppa Baskaran DK, Raman K. Big data for a small world: a review on databases and resources for studying microbiomes. J Indian Inst Sci. 2023:1–17.

  41. Dai D, Zhu J, Sun C, Li M, Liu J, Wu S, et al. GMrepo v2: a curated human gut microbiome database with special focus on disease markers and cross-dataset comparison. Nucleic Acids Res. 2022;50(D1):D777–D84.

    Article  CAS  PubMed  Google Scholar 

  42. Wu S, Sun C, Li Y, Wang T, Jia L, Lai S, et al. GMrepo: a database of curated and consistently annotated human gut metagenomes. Nucleic Acids Res. 2020;48(D1):D545–D53.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We acknowledge National Taiwan University’s Centers of Genomic and Precision Medicine for conducting the next-generation sequencing. We thank Melissa Stauffer, Ph.D. for English editing our manuscript.

Funding

This work was partly supported by Grant No. 111RB07, Central Taiwan Science Park Bureau, National Science and Technology Council; Research and Development Center for Medical Devices, National Taiwan University (112KKZA3T1); and Population Health and Welfare Research Center from Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan (NTU-112L9004) .

Author information

Authors and Affiliations

  1. Bioinformatics and Biostatistics Core, Centers of Genomic and Precision Medicine, National Taiwan University, Taipei, Taiwan

    Amrita Chattopadhyay, Liang-Chuan Lai, Mong-Hsun Tsai, Tzu-Pin Lu & Eric Y. Chuang

  2. Department of Biomedical Engineering, China Medical University, Taichung, Taiwan

    Chien-Yueh Lee

  3. Department of Public Health, Institute of Health Data Analytics and Statistics, National Taiwan University, Taipei, Taiwan

    Ya-Chin Lee & Tzu-Pin Lu

  4. Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan

    Chiang-Lin Liu, Yung-Hua Li & Eric Y. Chuang

  5. Center for Biotechnology, National Taiwan University, Taipei, Taiwan

    Hsin-Kuang Chen & Mong-Hsun Tsai

  6. Graduate Institute of Physiology, National Taiwan University, Taipei, Taiwan

    Liang-Chuan Lai

  7. Institute of Biotechnology, National Taiwan University, Taipei, Taiwan

    Mong-Hsun Tsai

  8. College of Medicine, National Taiwan University, Taipei, Taiwan

    Yen-Hsuan Ni & Han-Mo Chiu

  9. Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan

    Han-Mo Chiu

  10. Institute of Health Data Analytics and Statistics, National Taiwan University, Taipei, Taiwan

    Tzu-Pin Lu

  11. Biomedical Technology and Device Research Laboratories, Industrial Technology Research Institute, Hsinchu, Taiwan

    Eric Y. Chuang

  12. Division Research and Development Center for Medical Devices, National Taiwan University, Taipei, Taiwan

    Eric Y. Chuang

Authors
  1. Amrita Chattopadhyay
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chien-Yueh Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ya-Chin Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chiang-Lin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hsin-Kuang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yung-Hua Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Liang-Chuan Lai
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Mong-Hsun Tsai
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yen-Hsuan Ni
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Han-Mo Chiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Tzu-Pin Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Eric Y. Chuang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

T.P.L, E.Y.C conceived the study. Y.C.L., H.K.C., Y.H.L., Y.H.N., and H.M.C. collected samples and curated the data. A.C. and C.Y.L. did the formal analysis and interpreted the results. C.Y.L., C.L.L. and A.C. developed the database. T.P.L, E.Y.C, M.H.T, and L.C.L. provided the administrative support and supervised the project. A.C prepared the manuscript. A.C., C.Y.L. and T.P.L revised the manuscript. All authors reviewed the manuscript.

Corresponding authors

Correspondence to Tzu-Pin Lu or Eric Y. Chuang.

Ethics declarations

Ethics approval and consent to participate

The study was approved by the institutional review board of National Taiwan University Hospital (#201801085RINB) and all patients gave informed consent. All methods were performed in accordance with the relevant guidelines and regulations.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chattopadhyay, A., Lee, CY., Lee, YC. et al. Twnbiome: a public database of the healthy Taiwanese gut microbiome. BMC Bioinformatics 24, 474 (2023). https://doi.org/10.1186/s12859-023-05585-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12859-023-05585-6

Keywords

BMC Bioinformatics

ISSN: 1471-2105

Contact us