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Disbiome database: linking the microbiome to disease



Recent research has provided fascinating indications and evidence that the host health is linked to its microbial inhabitants. Due to the development of high-throughput sequencing technologies, more and more data covering microbial composition changes in different disease types are emerging. However, this information is dispersed over a wide variety of medical and biomedical disciplines.


Disbiome is a database which collects and presents published microbiota-disease information in a standardized way. The diseases are classified using the MedDRA classification system and the micro-organisms are linked to their NCBI and SILVA taxonomy. Finally, each study included in the Disbiome database is assessed for its reporting quality using a standardized questionnaire.


Disbiome is the first database giving a clear, concise and up-to-date overview of microbial composition differences in diseases, together with the relevant information of the studies published. The strength of this database lies within the combination of the presence of references to other databases, which enables both specific and diverse search strategies within the Disbiome database, and the human annotation which ensures a simple and structured presentation of the available data.


For many years, it has been believed that the human body has a microbial cell content which exceeds the total amount of human somatic cells by tenfold [1]. More recently, it has been estimated that this ratio between microbial and human cells is closer to 1:1 [2]. The collection of these microorganisms is termed ‘microbiota’ and the collective genomes of all the microorganisms of these microbiota are defined as the microbiome [3]. The main part of this microbiota is situated in the gut, in which the numbers and complexity increases from the stomach to the colon [4, 5]. Other anatomical sites which have their own microbiome are the lungs, skin, vagina, eyes, placenta, ear, oral cavity and sino-nasal compartment. The composition of the microbiome varies by anatomical site (e.g. between the gut and skin), between individuals and even over time [6, 7]. The microbiome composition can change due to factors such as dietary changes including pre- and probiotic use, antibiotic and other medicine use, age or disease and is moreover dynamic on its own [8, 9] . The microbiota composition and its correlation with health/disease is thus considered a multifactorial process. An active lifestyle can influence the gut microbiota composition, enhancing diversity and promoting bacterial communities associated with healthy individuals, which tend to be dominated by species such as Faecalibacterium prausnitzii, Roseburia hominis and Akkermansia muciniphila [10, 11]. Moreover, the mode of delivery has a major influence on the microbiome composition of the new born. After vaginal delivery, the baby’s microbiome resembles the mother’s genital and gastrointestinal tract while bacteria of the skin appear to be more abundant after caesarian section [12]. Several intrinsic and extrinsic factors influence the development and variation of bacteria in infants. Genetics and epigenetics, environmental factors like geography and diet (breastmilk or formula fed) all affect development of the microbial population [13, 14]. However, a lot of questions concerning the development of the fetus and neonate microbiome are still open [15].

While it was initially thought that microbes are mainly commensals whose only benefit is controlling the population of pathogenic bacteria, there is compelling evidence that the gut microbiome also has health-influencing effects, playing roles in i.a. digestion, inflammation, intestinal integrity and development of the immune system [16]. Production of microbial metabolites are a key driver in these processes. Host health thus appears to be closely related to a homeostatic balanced relationship with the microbial inhabitants. Several diseases are associated with an altered microbiota composition, such as obesity [17], diabetes [18], Crohn’s disease [19], ulcerative colitis [20], autism [21], bacterial vaginosis [22] and psoriasis [23]. These alterations are not limited to the location of the disease. As an example, alterations of the gut microbiota are seen in a variety of central nervous disorders, supporting the presence of a gut-brain axis [24, 25]. At this point, it remains to be elucidated whether the observed microbiota differences in various disease states are a symptom of the disease or have a more causal effect [6]. Suppressing clinical dysbiosis and restoring the altered microbiome to a ‘healthy’ microbiome can be a potential approach to improve host health. Potential therapeutic options include narrow spectrum antibiotics, probiotics, prebiotics, dietary interventions and fecal transplantation [16].

Different microbiology databases for research are available. There are databases covering different microbial subjects such as genomic resources (e.g. IMG) [26], protein families (e.g. Pfam) [27], diversity (e.g. SILVA) [28], model organisms (e.g. EcoCyc) [29], pathogenesis (e.g. EuPathDB) [30], transport and metabolism (e.g. TCDB) [31] and signal transduction and gene regulation (e.g. MiST) [32]. However, a database covering microbiome differences in different disease states is, to our knowledge, currently missing. Seen the exploding data of microbiome alterations in different disease states, we present the Disbiome database, collecting and organizing this information ( Disbiome encompasses microbiome differences between patients and controls together with the used detection method and sample type. This database differs from other comparative tools such as MG-RAST as it presents comparisons between patient and control data in a clear and concise manner to the broader audience in a programmatically accessible way using the JSON export format [33]. Disbiome can be valuable for every researcher in the field of microbiology to rapidly and easily find bacterial species possibly correlated to specific diseases to further explore its mode of actions and interaction mechanisms with the host. It can speed up translational research in microbiome modulations (by either probiotics, prebiotics and microbiota transplantation) for treating a variety of diseases. In addition, it can serve as a new disease classification system based on microbiome changes. Currently, the database includes over 190 different diseases and 800 different organisms. Changes in organisms are detected by over 25 different detection methods (e.g. qPCR, next-generation sequencing,…) in 50 different sample types (e.g. faeces, skin swabs, tissue biopsies,…).

Construction and content

To list all relevant data, a relational database was constructed [34]. A relational database separates the design of the data from its physical representation. Data are designed as tables where the rows represent distinct entities and the columns represent various attributes of those entities [35]. The schematic database model is given in Fig. 1. This visual representation represents the structure of the database. A block represents an entity type, where the table’s columns represent the entity type’s different attributes. Such entity type can have an unlimited number of entities. In the physical database, each entity type translates to a table where each attribute represents a column of that table and each entity is represented by a row. The central entity in the Disbiome database is ‘Experiment’, representing the microbiome difference between a patient and control. Every experiment has a qualitative outcome (elevated or reduced) and is related to different parameters. The experiment is linked to the appropriate publication (Publication ID), showing a microbial (Organism ID) difference between a sample (Sample ID) of a patient (Disease ID, Host ID) and a control subject (Control ID) using a specific detection method (Method ID). This sample originates from a specific location (Location ID). The microbial difference can be presented by absolute quantities between the patient and control or by a ratio. In the case of absolute data, a specific response unit, dependent on the used detection method, is present (Response ID). When absolute data is not available, the microbial differences are only presented by the qualitative outcome. Every host and publication is linked to different host and publication parameters respectively and diseases are linked to their classification in the Medical dictionary for Regulatory Activities (MedDRA). The storage of Disbiome was implemented using PostgreSQL, an open source database. This is accessed by LimeDS, a framework developed at Ghent University, providing a web service with which the website ( communicates [36]. Several search options are present in the Disbiome database. Organisms, diseases and detection methods can be used as queries and will give an overview of the experiments related to this organism, disease or detection method. From this overview page, detailed information about the experiment can be obtained.

Fig. 1

Database scheme. RCD = Reference Classification Database

Literature data was collected by using the search engine PubMed, covering the period 2009–2018. The search queries [(‘microbiota’ OR ‘microbiome) AND (‘health’ OR ‘disease’)] and [microbiome alterations] were used. This search gave approximately 20.000 publications. Based on title (exclusion of duplicates, reviews, animal studies, studies on effect of medication, symposium/meeting abstracts as well as non-English language papers), only around 1.000 publications were withheld and based on the abstract (only case-control studies) around 500 publications were found to be suitable for insertion in the database. The obtained literature was processed manually and all relevant information was put in the database. Currently only human data is incorporated but information of other species (e.g. mice, rats,…) will be included as well. The database will be updated manually every 3 months. An automated updating system is being developed for further versions of the database. Additionally, authors of publications can inform us of missing manuscripts by using the ‘Submission’ link.

Utility and discussion

Our main objective was to construct a database giving a clear and rapid overview of all bacterial species which are differentially present in a particular disease.


The experiment section is the central table of the database. It contains all the relevant information about the experiment (disease, detected organism, quantitative data of the patient and control, control type, response type and detection method), all other data is linked to this experiment as well (e.g. publication info, host details, methodological details and sample type). The sample type is of great importance because it can influence the detected microbiome composition. Tedjo et al. demonstrated a higher microbial diversity using fecal swabs compared to stool samples in the same subjects [37].


The diseases are classified using the classification of the Medical Dictionary for Regulatory Activities (MedDRA). It is developed by the International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) to provide a single standardized international medical terminology to facilitate sharing of regulatory information for medical products used by humans. MedDRA consists of a five-level structural hierarchy, arranged from very specific to very general levels. There are the Lowest Level Term (LLT), Preferred Term (PT), High Level Term (HLT), High Level Group Term (HLGT) and System Organ Class (SOC) [38]. Disbiome uses the PT to link the disease to its classification or where more appropriate the LLT. Selecting a certain classification term will give an overview of all the experiments linked to diseases classified in that specific term (e.g. all Gastrointestinal disorders (SOC)). This classification and an overview of all the related experiments linked to a certain disease is presented in the disease detail page.


The microbial organisms are classified using the NCBI and SILVA taxonomy and are linked to its corresponding databases. The NCBI taxonomy is the standard nomenclature and classification repository for the International Nucleotide Sequence Database Collaboration (INSDC). It includes organism names and taxonomic lineages for each of the sequences represented in the INSDC’s nucleotide and protein sequence databases [39]. The SILVA database contains taxonomic information of Bacteria, Archaea and Eukarya and is based on small subunit rRNA sequence information [28]. These taxonomies are chosen because NCBI is the most extensive taxonomy (other taxonomies are for the most part contained in the NCBI taxonomy) and goes down to the species level while SILVA’s taxonomic classification is very reliable due to its manual curation. In addition, these databases are updated regularly [40]. An overview of all related experiments linked to a certain organism is given in the organism detail page.

Detection method

Traditional studies of the microbiome remained largely dependent on cultivation techniques. However, these culture methods are able to detect only 10–30% of the microbiota [41]. Due to rapid development of culture-independent molecular technologies such as PCR-denaturating gradient gel electrophoresis (DGGE), restriction fragment length polymorphism (RFLP), DNA microarray, etc., non-cultivatable organisms could be detected [42,43,44]. More recently, several next-generation sequencing (NGS) technologies have been developed which make it possible to detect even low abundant micro-organisms [45]. Most recent techniques such as shotgun metagenomic sequencing are able to not only reveal abundancy changes, but also functional changes in the microbiome [41]. The choice of detection method in a microbiome case-control study is of great importance. A NGS method is able to detect certain bacteria where other techniques fail, resulting in different relative proportions of the microbial composition [46]. However, different NGS platforms can produce different microbial profiles. So, it may be necessary to use different platforms to correctly unravel the microbial profile and it is important to use the same platform(s) to make comparisons possible [47].


Every experiment in the Disbiome database is linked to the publication of the original research in PubMed. When sequencing data are deposited in a repository, a link to this repository with the accession numbers is given. Due to the rise of NGS technologies, immense amounts of sequencing data are generated. These experimental data should be archived for this is key to the progress of reproducible science. The Sequence Read Archive (SRA) was one of the first archives for sequencing data and was established as a part of the INSDC [48], other data repositories are: GenBank [49], European Nucleotide Archive (ENA) [50] and the DNA Data Bank of Japan (DDBJ) [39]. Additionally, all the data from the Human Microbiome Project (HMP) is freely available through its portal [51].

To ensure that the used methods and results can be reviewed, analyzed and repeated, a minimum amount of relevant information must be included in scientific publications. Numerous standards for conducting and reporting clinical trials have been implemented for years. Examples of these standards are the Cochrane, CONSORT (CONsolidated Standards Of Reporting Trials) [52] and STROBE (STrengthening the Reporting OBservational studies in Epidemiology) initiatives [53]. These measures have improved the reporting quality of clinical trials [54]. In experimental life sciences, such guidelines are implemented more recently. In 2009, Kilkenny et al. performed a survey of the reporting quality of scientific research using animals. This survey identified some issues that need to be addressed in order to improve scientific research [55]. This resulted in the establishment of the ARRIVE reporting guidelines for animal in vivo experiments [56]. Publications in the Disbiome database are assessed for different reporting parameters based on a survey performed by Vesterinen et al. [54]. This survey consists of 16 questions all assessing different aspects of the reporting quality (Table 1). These data are all presented in the publication detail page.

Table 1 Questionnaire for assessing reporting parameters of Disbiome publications


Literature data on microbiome alterations in different disease states is vastly increasing. Disbiome ( provides an organized overview of this rapidly expanding field of knowledge. Together with the used sample, detection method, methodological details and host information, quantitative data of micro-organisms in patients and controls from a specific experiment are presented. In addition, different reporting parameters of the concerned publications are presented. This is the first database giving a relation between the health status of the host and its microbiota composition.

Availability and requirements

Project name: Disbiome.

Project home page:

Browser: Google Chrome, Microsoft Internet Explorer, Mozilla Firefox.

Lisence: none.

Restrictions for non-academic users: none.



CONsolidated Standards Of Reporting Trials


DNA Data Bank of Japan


Denaturating gradient gel electrophoresis


European Nucleotide Archive


Highest level group term


High level term


International council for harmonisation of technical requirements for pharmaceuticals for human use


International nucleotide sequence database collaboration


Lowest level term


Medical dictionary for regulatory activities


Next-generation sequencing


Preferred term


Restriction fragment length polymorphism


System organ class


STrengthening the Reporting OBservational studies in Epidemiology


  1. 1.

    Belkaid Y, Naik S. Compartmentalized and systemic control of tissue immunity by commensals. Nat Immunol. 2013;14(7):646–53.

    Article  PubMed  CAS  Google Scholar 

  2. 2.

    Sender R, Fuchs S, Milo R. Are we really vastly outnumbered? Revisiting the ratio of bacterial to host cells in humans. Cell. 2016;164(3):337–40.

    Article  PubMed  CAS  Google Scholar 

  3. 3.

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. 4.

    Ghaisas S, Maher J, Kanthasamy A. Gut microbiome in health and disease: linking the microbiome-gut-brain axis and environmental factors in the pathogenesis of systemic and neurodegenerative diseases. Pharmacol Ther. 2015;158:52–62.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. 5.

    Tojo R, Suarez A, Clemente MG, de los Reyes-Gavilan CG, Margolles A, Gueimonde M, et al. Intestinal microbiota in health and disease: role of bifidobacteria in gut homeostasis. World J Gastroenterol. 2014;20(41):15163–76.

    Article  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Cho I, Blaser MJ. The human microbiome: at the interface of health and disease. Nat Rev Genet. 2012;13(4):260–70.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. 7.

    Ursell LK, Clemente JC, Rideout JR, Gevers D, Caporaso JG, Knight R. The interpersonal and intrapersonal diversity of human-associated microbiota in key body sites. J Allergy Clin Immunol. 2012;129(5):1204–8.

    Article  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Caporaso G, Lauber C, Costello E, Berg-Lyons D, Gonzalez A, Stombaugh J, et al. Moving pictures of the human microbiome. Genome Biol. 2011;12(5):R50.

    Article  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Marti JM, Martinez-Martinez D, Rubio T, Gracia C, Pena M, Latorre A, et al. Health and disease imprinted in the time variability of the human microbiome. mSystems. 2017;2(2):e00144-16.

  10. 10.

    Monda V, Villano I, Messina A, Valenzano A, Esposito T, Moscatelli F, et al. Exercise modifies the gut microbiota with positive health effects. Oxidative Med Cell Longev. 2017;2017:3831972.

    Article  CAS  Google Scholar 

  11. 11.

    Bressa C, Bailen-Andrino M, Perez-Santiago J, Gonzalez-Soltero R, Perez M, Montalvo-Lominchar MG, et al. Differences in gut microbiota profile between women with active lifestyle and sedentary women. PLoS One. 2017;12(2):e0171352.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. 12.

    Dominguez-Bello MG, Costello EK, Contreras M, Magris M, Hidalgo G, Fierer N, et al. Delivery mode shapes the acquisition and structure of the initial microbiota across multiple body habitats in newborns. Proc Natl Acad Sci U S A. 2010;107(26):11971–5.

    Article  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Adlerberth I, Wold AE. Establishment of the gut microbiota in western infants. Acta Paediatr. 2009;98(2):229–38.

    Article  PubMed  CAS  Google Scholar 

  14. 14.

    Lee SA, Lim JY, Kim BS, Cho SJ, Kim NY, Kim OB, et al. Comparison of the gut microbiota profile in breast-fed and formula-fed Korean infants using pyrosequencing. Nutr Res Pract. 2015;9(3):242–8.

    Article  PubMed  Google Scholar 

  15. 15.

    Mueller NT, Bakacs E, Combellick J, Grigoryan Z, Dominguez-Bello MG. The infant microbiome development: mom matters. Trends Mol Med. 2015;21(2):109–17.

    Article  PubMed  Google Scholar 

  16. 16.

    Walker AW, Lawley TD. Therapeutic modulation of intestinal dysbiosis. Pharmacol Res. 2013;69(1):75–86.

    Article  PubMed  CAS  Google Scholar 

  17. 17.

    Turnbaugh PJ, Hamady M, Yatsunenko T, Cantarel BL, Duncan A, Ley RE, et al. A core gut microbiome in obese and lean twins. Nature. 2009;457(7228):480–4.

    Article  PubMed  CAS  Google Scholar 

  18. 18.

    Larsen N, Vogensen FK, van den Berg FW, Nielsen DS, Andreasen AS, Pedersen BK, et al. Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults. PLoS One. 2010;5(2):e9085.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. 19.

    Willing BP, Dicksved J, Halfvarson J, Andersson AF, Lucio M, Zheng Z, et al. A pyrosequencing study in twins shows that gastrointestinal microbial profiles vary with inflammatory bowel disease phenotypes. Gastroenterology. 2010;139(6):1844–54. e1

    Article  PubMed  Google Scholar 

  20. 20.

    Michail S, Durbin M, Turner D, Griffiths AM, Mack DR, Hyams J, et al. Alterations in the gut microbiome of children with severe ulcerative colitis. Inflamm Bowel Dis. 2012;18(10):1799–808.

    Article  PubMed  Google Scholar 

  21. 21.

    Parracho HM, Bingham MO, Gibson GR, McCartney AL. Differences between the gut microflora of children with autistic spectrum disorders and that of healthy children. J Med Microbiol. 2005;54(Pt 10):987–91.

    Article  PubMed  Google Scholar 

  22. 22.

    Spear GT, Sikaroodi M, Zariffard MR, Landay AL, French AL, Gillevet PM. Comparison of the diversity of the vaginal microbiota in HIV-infected and HIV-uninfected women with or without bacterial vaginosis. J Infect Dis. 2008;198(8):1131–40.

    Article  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Alekseyenko AV, Perez-Perez GI, De Souza A, Strober B, Gao Z, Bihan M, et al. Community differentiation of the cutaneous microbiota in psoriasis. Microbiome. 2013;1(1):31.

    Article  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Wang Y, Kasper LH. The role of microbiome in central nervous system disorders. Brain Behav Immun. 2014;38:1–12.

    Article  PubMed  CAS  Google Scholar 

  25. 25.

    Dinan TG, Cryan JF. The microbiome-gut-brain Axis in health and disease. Gastroenterol Clin N Am. 2017;46(1):77–89.

    Article  Google Scholar 

  26. 26.

    Markowitz VM, Chen IM, Palaniappan K, Chu K, Szeto E, Grechkin Y, et al. IMG: the integrated microbial genomes database and comparative analysis system. Nucleic Acids Res. 2012;40:D115–22.

    Article  PubMed  CAS  Google Scholar 

  27. 27.

    Finn RD, Bateman A, Clements J, Coggill P, Eberhardt RY, Eddy SR, et al. Pfam: the protein families database. Nucleic Acids Res. 2014;42:D222–30.

    Article  PubMed  CAS  Google Scholar 

  28. 28.

    Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.

    Article  PubMed  CAS  Google Scholar 

  29. 29.

    Karp PD, Riley M, Paley SM, Pelligrini-Toole A. EcoCyc: an encyclopedia of Escherichia coli genes and metabolism. Nucleic Acids Res. 1996;24(1):32–9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. 30.

    Aurrecoechea C, Barreto A, Brestelli J, Brunk BP, Cade S, Doherty R, et al. EuPathDB: the eukaryotic pathogen database. Nucleic Acids Res. 2013;41:D684–91.

    Article  PubMed  CAS  Google Scholar 

  31. 31.

    Saier MH Jr, Reddy VS, Tamang DG, Vastermark A. The transporter classification database. Nucleic Acids Res. 2014;42:D251–8.

    Article  PubMed  CAS  Google Scholar 

  32. 32.

    Ulrich LE, Zhulin IB. MiST: a microbial signal transduction database. Nucleic Acids Res. 2007;35:D386–90.

    Article  PubMed  CAS  Google Scholar 

  33. 33.

    Meyer F, Paarmann D, D'Souza M, Olson R, Glass EM, Kubal M, et al. The metagenomics RAST server - a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinformatics. 2008;9:386.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. 34.

    Codd EF. A relational model of data for large shared data banks. Commun ACM. 1970;13(6):377–87.

    Article  Google Scholar 

  35. 35.

    Strawn G, Strawn C. Relational databases: Codd, Stonebraker, and Ellison. IT Prof. 2016;18(2):63–5.

    Article  Google Scholar 

  36. 36.

    Verstichel S KW, Dupont T, Volckaert B, Ongenae F, De Turck F and Demeester P. LimeDS and the TraPIST project: a case study. 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering, and Knowledge Management; 12/11/2015–14/11/2015; Lisbon, Portugal. 2015. p. 501–508.

  37. 37.

    Tedjo DI, Jonkers DM, Savelkoul PH, Masclee AA, van Best N, Pierik MJ, et al. The effect of sampling and storage on the fecal microbiota composition in healthy and diseased subjects. PLoS One. 2015;10(5):e0126685.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. 38.

    (ICH): MedDRA. Available from: Accessed 11 Apr 2017.

  39. 39.

    Federhen S. The NCBI taxonomy database. Nucleic Acids Res. 2012;40:D136–43.

    Article  PubMed  CAS  Google Scholar 

  40. 40.

    Balvociute M, Huson DH. SILVA, RDP, Greengenes, NCBI and OTT - how do these taxonomies compare? BMC Genomics. 2017;18(Suppl 2):114.

    Article  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Wang WL, Xu SY, Ren ZG, Tao L, Jiang JW, Zheng SS. Application of metagenomics in the human gut microbiome. World J Gastroenterol. 2015;21(3):803–14.

    Article  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Ranjbar R, Behzadi P, Najafi A, Roudi R. DNA microarray for rapid detection and identification of food and water borne Bacteria: from dry to wet lab. Open Microbiol J. 2017;11:330–8.

    Article  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Siqueira JF Jr, Sakamoto M, Rosado AS. Microbial community profiling using terminal restriction fragment length polymorphism (T-RFLP) and denaturing gradient gel electrophoresis (DGGE). Methods Mol Biol. 2017;1537:139–52.

    Article  PubMed  CAS  Google Scholar 

  44. 44.

    Sjoberg F, Nowrouzian F, Rangel I, Hannoun C, Moore E, Adlerberth I, et al. Comparison between terminal-restriction fragment length polymorphism (T-RFLP) and quantitative culture for analysis of infants’ gut microbiota. J Microbiol Methods. 2013;94(1):37–46.

    Article  PubMed  CAS  Google Scholar 

  45. 45.

    Ansorge WJ. Next-generation DNA sequencing techniques. New Biotechnol. 2009;25(4):195–203.

    Article  CAS  Google Scholar 

  46. 46.

    Samarajeewa AD, Hammad A, Masson L, Khan IU, Scroggins R, Beaudette LA. Comparative assessment of next-generation sequencing, denaturing gradient gel electrophoresis, clonal restriction fragment length polymorphism and cloning-sequencing as methods for characterizing commercial microbial consortia. J Microbiol Methods. 2015;108:103–11.

    Article  PubMed  CAS  Google Scholar 

  47. 47.

    Hahn A, Sanyal A, Perez GF, Colberg-Poley AM, Campos J, Rose MC, et al. Different next generation sequencing platforms produce different microbial profiles and diversity in cystic fibrosis sputum. J Microbiol Methods. 2016;130:95–9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. 48.

    Kodama Y, Shumway M, Leinonen R. The sequence read archive: explosive growth of sequencing data. Nucleic Acids Res. 2012;40:D54–6.

    Article  PubMed  CAS  Google Scholar 

  49. 49.

    Benson DA, Cavanaugh M, Clark K, Karsch-Mizrachi I, Lipman DJ, Ostell J, et al. GenBank. Nucleic Acids Res. 2017;45:D37–42.

    Article  PubMed  CAS  Google Scholar 

  50. 50.

    Leinonen R, Akhtar R, Birney E, Bower L, Cerdeno-Tarraga A, Cheng Y, et al. The European nucleotide archive. Nucleic Acids Res. 2011;39:D28–31.

    Article  PubMed  CAS  Google Scholar 

  51. 51.

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. 52.

    Schulz KF. The quest for unbiased research: randomized clinical trials and the CONSORT reporting guidelines. Ann Neurol. 1997;41(5):569–73.

    Article  PubMed  CAS  Google Scholar 

  53. 53.

    von Elm E, Altman DG, Egger M, Pocock SJ, Gotzsche PC, Vandenbroucke JP. The Strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. PLoS Med. 2007;4(10):e296.

    Article  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Vesterinen HM, Egan K, Deister A, Schlattmann P, Macleod MR, Dirnagl U. Systematic survey of the design, statistical analysis, and reporting of studies published in the 2008 volume of the journal of cerebral blood flow and metabolism. J Cereb Blood Flow Metab. 2011;31(4):1064–72.

    Article  PubMed  Google Scholar 

  55. 55.

    Kilkenny C, Parsons N, Kadyszewski E, Festing MF, Cuthill IC, Fry D, et al. Survey of the quality of experimental design, statistical analysis and reporting of research using animals. PLoS One. 2009;4(11):e7824.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. 56.

    Kilkenny C, Browne WJ, Cuthi I, Emerson M, Altman DG. Improving bioscience research reporting: the ARRIVE guidelines for reporting animal research. Vet Clin Pathol. 2012;41(1):27–31.

    Article  PubMed  Google Scholar 

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The authors like to thank Annatachja De Grande, Pearl Choi, Karen Vermeulen, Kirsten Leurs, Marisol Aguirre Morales, Evy Goossens, Lonneke Onrust, Fien Demeyer, Nathalie Goethals, Evelien Dierick, Griet Driesschaert, Petra Van Wassenhove, Hilde Devlies and Venessa Eeckhaut for reviewing the literature and helping to construct the database. The authors also would like to thank Prof. Dr. Mario Vaneechoutte, Tessa Gryp and Prof. Dr. Simon Van Belle for the interesting scientific discussions and all the reviewers of a beta-version of the database.


The authors thank the ‘Research Foundation – Flanders (FWO)’ (grant number 1S21017N to Nathan Debunne) and the ‘Institute for the Promotion of Innovation through Science and Technology in Flanders (IWT-Vlaanderen)’ (grant number 131356 to Frederick Verbeke). The funding bodies had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

Availability of data and materials

Disbiome is freely accessible at

Author information




YJ and BDS conceived the idea for this manuscript and wrote the manuscript. YJ, EW, FVI and BDS designed the database. JN, AB and GDT built the database and website. YJ, ND, FV, EW, FVI, YPV made some major contributions in data-input. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Bart De Spiegeleer.

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Janssens, Y., Nielandt, J., Bronselaer, A. et al. Disbiome database: linking the microbiome to disease. BMC Microbiol 18, 50 (2018).

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  • Dysbiosis
  • Database
  • MedDRA
  • Health status