Skip to main content

mapMECFS: a portal to enhance data discovery across biological disciplines and collaborative sites



Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating disease which involves multiple body systems (e.g., immune, nervous, digestive, circulatory) and research domains (e.g., immunology, metabolomics, the gut microbiome, genomics, neurology). Despite several decades of research, there are no established ME/CFS biomarkers available to diagnose and treat ME/CFS. Sharing data and integrating findings across these domains is essential to advance understanding of this complex disease by revealing diagnostic biomarkers and facilitating discovery of novel effective therapies.


The National Institutes of Health funded the development of a data sharing portal to support collaborative efforts among an initial group of three funded research centers. This was subsequently expanded to include the global ME/CFS research community. Using the open-source comprehensive knowledge archive network (CKAN) framework as the base, the ME/CFS Data Management and Coordinating Center developed an online portal with metadata collection, smart search capabilities, and domain-agnostic data integration to support data findability and reusability while reducing the barriers to sustainable data sharing.


We designed the mapMECFS data portal to facilitate data sharing and integration by allowing ME/CFS researchers to browse, share, compare, and download molecular datasets from within one data repository. At the time of publication, mapMECFS contains data curated from public data repositories, peer-reviewed publications, and current ME/CFS Research Network members.


mapMECFS is a disease-specific data portal to improve data sharing and collaboration among ME/CFS researchers around the world. mapMECFS is accessible to the broader research community with registration. Further development is ongoing to include novel systems biology and data integration methods.


Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex, debilitating disease [1] estimated to affect as many as 2.5 million Americans [2]. Affected individuals often have incapacitating fatigue, nonrefreshing sleep or other sleep difficulties, and cognitive impairment that may leave them unable to leave the house or bed. The disease is characterized by the worsening of symptoms following even minor physical or mental exertion, known as post-exertional malaise [3]. Although the underlying disease etiology remains unknown [1, 4] there is evidence that multiple body systems are involved. When comparing ME/CFS cases to controls, researchers have observed differences in the immune system [5, 6], blood metabolites [7,8,9,10,11,12], the gut microbiome [13,14,15], and mitochondrial DNA genetic variants [16]. Integrating findings across these domains promises to reveal a more complete picture of the disease, thereby detecting diagnostic biomarkers and facilitating discovery of novel effective therapies.

To support data sharing, the ME/CFS Research Network [17] comprising three Collaborative Research Centers and a Data Management and Coordinating Center (DMCC) was funded in 2017 by multiple National Institutes of Health (NIH) Institutes, Offices, and Centers, including the National Institute of Neurological Disorders and Stroke and the National Institute of Allergy and Infectious Diseases to encourage collaborative research to lead to better diagnosis and treatment for ME/CFS. One of the goals of the DMCC is to help ME/CFS researchers discover new disease insights by promoting data sharing. To this end, we developed the mapMECFS [18] data portal, which is built on a flexible database structure and optimized to handle multiple data types (e.g., gene expression, methylation, metabolomics, cytokine measures, proteomics, microbiome, survey/questionnaire). This portal contains specialized features to support cross-disciplinary and cross-study ME/CFS research generated from multiple research domains including cascading forms to collect key study metadata, intuitive dataset filtering, and smart search capabilities with synonym tagging with embedded mapping of synonymous feature terminologies.


Website framework and infrastructure

mapMECFS is built on the comprehensive knowledge archive network (CKAN) framework, an open-source tool designed to support data storage and sharing [19]. The portal includes an ecosystem of custom plugins hosted on a novel computing infrastructure built on Amazon Web Services technologies [16]. CKAN [20] includes a customizable containerization schema using Docker [21] in conjunction with Amazon Web Services [22] technologies to engineer a more performant, resilient, and affordable solution. Docker containers are minimal computing environments that can run on a personal computer, a dedicated server, or cloud computing. Docker containers are created from Docker Images, container definitions, that allow the portal developers to define the computing environment, network connections, and source code needed for each component of the portal. These images can be tested locally and then pushed to the cloud computing instance and activated as containers running the most up-to-date version of the software in a stable and predictable fashion. mapMECFS’ database components are hosted using Amazon’s Relational Database Service Aurora [23] database-as-a-service platform, which only incurs cost during times of usage and automatically scales to the size required by the contents of the database. User-uploaded files are stored using Amazon’s Elastic File System [24], a scalable and managed file system. By using these two services, we minimize cost of infrastructure and maintenance, as both services scale to usage for size and require no maintenance or management.

For the computation needed to run the application, mapMECFS utilizes Amazon’s Elastic Container Service (ECS) [25] running on top of Elastic Compute Cloud (EC2) [24]. mapMECFS’ EC2 compute server functions as a provisioned virtual machine with adequate and scalable resources for handling loads from both web traffic and more resource-intensive asynchronous tasks such as our custom extensions. ECS allows developers to dynamically allocate resources throughout the containerized computation services, including the web server, a Redis cache for managing queues, and an instance of Apache Solr [26] used for building search indexes. Additionally, mapMECFS leverages Amazon’s Cloudfront [27] content delivery network and Route53 [28] domain name service to maintain availability across the world wide web.

mapMECFS access and site organization

mapMECFS is accessible to the research community at [18]. To obtain full access to the mapMECFS portal, new users must register for an account, provide a brief description of how they will use the data on the portal, and agree to the data use agreement. Account registration must be approved by the NIH data access committee. Upon approval, a user account will be created granting the user permission to explore and upload data.

mapMECFS site users are grouped together within an Organization which designates the institute, research center, or individual research lab that users and the datasets (a collection of data files) they upload are associated with (Additional file 1: Figure S1). A user must be part of an Organization to upload data. Independent or unaffiliated users are assigned to an Organization where they are the only member. Datasets are designated as either public (available to all mapMECFS site users) or private (only available to users within an Organization). Each user account has a defined role within an Organization. Organization members can view private datasets within an Organization on a read-only basis. Organization editors have all the abilities of members in addition to the ability to create and edit datasets they created and request a private dataset to be made public.


We created mapMECFS to facilitate sharing of ME/CFS data among the broader research community. To expedite that process, we populated the portal with current publications and publicly available data. An overview of the portal is shown in Fig. 1. Registered site users can upload their own de-identified primary research data and research results and share with other approved site users by creating a dataset and uploading associated files.

Fig. 1
figure 1

mapMECFS website overview. The user uploads files (data, phenotype, results, and supporting files) to a Dataset along with metadata. All datafiles are access protected to public or private depending on the user’s preference. mapMECFS processes the data to generate summary statistics, conduct synonym tagging, and compile results files to compare findings across datasets. The user can search available data and results files along with viewing, filtering, or downloading files

mapMECFS data and curation

mapMECFS was designed to store de-identified demographic, survey, and health data coupled with molecular data (e.g., transcriptomics, metabolomics, methylation). The DMCC has curated ME/CFS data with open-access publications [5,6,7,8,9, 11, 13,14,15,16, 29,30,31,32,33,34,35,36,37,38]; gene expression, methylation, and micro-RNA (miRNA) datasets from the Gene Expression Omnibus [19, 39,40,41,42,43,44,45,46,47,48]; and metabolomic data from MetaboLights [49]. Active curation by the DMCC is ongoing (Additional file 1: Table S1).

Metadata, such as dataset title, description, tags, cohort selection, and case definition, are captured during the upload process for all datasets. The dynamic upload process prompts users for other key metadata based on the data type. Thus, metadata prompts for gene expression datasets will be different than metadata prompts for DNA methylation experiments. Metadata collection is performed using prepopulated, easy-to-use, drop-down menus to describe the experimental assay and data measurement unit with supplemental open-ended text boxes. Uploaded data are categorized by tags, which are suggested by natural language processing in real time based on a description provided by the user. Users can select the most appropriate suggested tags or supply their own as free text. These tags are used to filter and sort datasets to enable easy findability.

Data are uploaded into datasets. The definition of a dataset is flexible; it can contain one or many relevant file types (data file, phenotype file, results file, supporting files), as described in Table 1. To take advantage of the custom tools described below, the data, phenotype, and results files must follow the specified file format requirements described in Additional file 2: Figure S2. A dataset can contain an unlimited number of results and supporting files, thus enabling the sharing of standard operating procedures, external links (e.g., publication, sequencing read archive, data availability), and results from two or more analyses. The data upload process is flexible, allowing for multiple file formats. We encourage researchers to share datasets with other mapMECFS site users by making their datasets Public (i.e., viewable to all approved mapMECFS users). The process of approving public data requests will be conducted in accordance with the mapMECFS policies (“Methods” section).

Table 1 File types uploaded to mapMECFS, including data file (e.g., processed data), phenotype file (e.g., clinical data), results file (e.g., summary statistics), and support file (e.g., link to publication)

Custom analysis and search tools

We optimized the mapMECFS search functionality to maximize data discovery and created custom analysis and search tools that run automatically once data is uploaded. The search feature recognizes user-specified terms describing multiple aspects of a dataset, including a dataset name, descriptions, key metadata, data file contents, and data synonyms generated from the Synonym Tagging tool [50] (Additional file 1: Table S2). Synonym Tagging is an automated backend feature of mapMECFS that labels molecules with known synonyms to enhance the searchability of analytes on the portal. By tagging both the indicated annotation and all recognized synonyms, mapMECFS extends the search space for each entered query. Synonyms are assigned based on well-established databases: National Center for Biotechnology Information’s Entrez [51] for gene expression data; InChIKey [52], ChEBI [53] ID, and HMDB [54] ID for metabolomic data; miRBase [55] for miRNA data; and manifest files for methylation Illumina array data (Illumina, Inc., San Diego, CA). For transparency, the results of the Synonym Tagging are available on the dataset page (Additional file 3: Figure S3) via the 'View Additional Search Terms' hyperlink. For example, if a researcher is interested in the cytokine interleukin 17, searches of “IL-17” or “IL-17A” will return all datasets containing the desired molecule, effectively standardizing terminology from different research domains and ontologies.

If the uploaded dataset contains both a data file with sample-level molecule values and a phenotype file with participant-level variables (e.g., case–control status), the Calculated Summary Statistics tool [56] will calculate and display a set of summary statistics for each molecule compared for each phenotypic group (e.g., case vs. control, multiple subtypes). These two-group comparison tests are performed using the nonparametric Wilcoxon Rank Sum test [57]. The Calculated Summary Statistics tool generates (1) sample sizes in each group, (2) median value for each group, (3) standard deviation for each group, (4) Wilcoxon rank-sum test statistics, (4) Wilcoxon rank-sum p-value, and (5) Wilcoxon rank-sum Bonferroni Corrected p-value, allowing researchers to quickly to characterize how analytes may differ between phenotype groups.

mapMECFS also includes a customized data search tool, the Results File Explorer, to facilitate cross-dataset searches. This tool is designed to allow users to quickly evaluate the reproducibility of a given result across multiple studies by comparing results to other analyses or subset analyses. Additional file 4: Figure S4 shows an example search for the metabolite 4-hydroxyglutamate. This amino acid, part of the glutamate metabolism pathway, has substantial implications in brain function [7] and has been shown to be elevated in ME/CFS patients [7]. The Results File Explorer output shown in Additional file 4: Figure S4 contain three separate tables, “Data Files and Calculated Summary Statistics”, “Results Files”, and “Other Datasets”. “Data Files and Calculated Summary Statistics” contains search results only from the mapMECFS Calculated Summary Statistics. The “Results Files” table contains search results only from the user-uploaded results files. The “Other Datasets” table contains search results from other elements of the dataset, including the title, description, or metadata. The tables contain key metadata so researchers can view analytic results while comparing analysis endpoints and applied methods. With the Results File Explorer tool and the mapMECFS search functionality, users can identify datasets to validate their findings and identify datasets to integrate with data they have collected. This process enhances the collaborative nature of ME/CFS research.


To promote international sharing of ME/CFS data across researchers and repositories, we created mapMECFS, a ME/CFS domain-specific data repository that conforms to NIH’s Findable, Accessible, Interoperable, and Reusable (FAIR) [58]. Guiding Principles and its strategic plan for data science [59]. mapMECFS achieves this by (1) being findable with persistent identifiers for each table within a dataset (and the dataset itself) and providing rich metadata, (2) making all data and metadata accessible via both an intuitive user interface and application programming interface, (3) providing data and metadata that are interoperable with vocabulary and language which are common in the field, and (4) using clear reuse license and data provenance. The mapMECFS portal is developed with a scalable infrastructure that delivers scientific impact to the research community, supports good data management practices, and actively engages the user community.

mapMECFS use cases may include (1) identifying new collaborators by identifying researchers working on a specific data type or by browsing descriptions of individual researchers’ interests, (2) validating a research finding by searching for a molecule of interest (in the Dataset explorer or Results File Explorer) to discover independent datasets containing that molecule, (3) meeting a funder or journal’s requirements of data sharing by uploading new experimental data or summary statistics, and (4) increasing statistical power by identifying an appropriate dataset for meta-analysis or data integration using the captured metadata, dataset description, and other support documentation. The DMCC plans to improve data integration by standardizing sample identifiers and clinical data capture to support systems biology and data integration approaches for ME/CFS research. Investigating multi-omics may help with identification for disease subtyping, diagnosis, and predictive outcome [60].


mapMECFS [18] is available to the broad research community with registration as described above. To facilitate sharing of MECFS data, we encourage new users to consider mapMECFS for their research needs. This unique, domain-specific data repository was designed considering NIH’s FAIR Guiding Principles and strategic plan for data science. We encourage mapMECFS users to provide feedback and to request new features. mapMECFS is continuously expanding the types of data included on the site and welcomes feedback on the types of data researchers would like to share.

Availability of data and materials

The mapMECFS data portal is open to the research community at Code for the custom analyses and tools available in mapMECFS is available at



Chemical Entities of Biological Interest


Comprehensive knowledge archive network


Data Management and Coordinating Center

FAIR Principles:

Findable, Accessible, Interoperable, Reproducible Principles


Human Metabolomics Database


International Chemical Identifier Key


Myalgic encephalomyelitis/chronic fatigue syndrome




National Institutes of Health


  1. Center for Disease Control and Prevention. Myalgic Encephalomyelitis/Chronic Fatigue Syndrome 2020.

  2. Committee on the Diagnostic Criteria for Myalgic Encephalomyelitis/Chronic Fatigue S, Board on the Health of Select P, Institute of M. The National Academies Collection: Reports funded by National Institutes of Health. Beyond Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Redefining an Illness. Washington (DC): National Academies Press (US). Copyright 2015 by the National Academy of Sciences. All rights reserved.; 2015.

  3. Carruthers BM, Jain AK, De Meirleir KL, Peterson DL, Klimas NG, Lerner AM, et al. Myalgic encephalomyelitis/chronic fatigue syndrome. J Chronic Fatigue Syndr. 2003;11(1):7–115.

    Article  Google Scholar 

  4. Cortes Rivera M, Mastronardi C, Silva-Aldana CT, Arcos-Burgos M, Lidbury BA. Myalgic encephalomyelitis/chronic fatigue syndrome: a comprehensive review. Diagnostics (Basel). 2019;9:3.

    Google Scholar 

  5. Hornig M, Gottschalk CG, Eddy ML, Che X, Ukaigwe JE, Peterson DL, et al. Immune network analysis of cerebrospinal fluid in myalgic encephalomyelitis/chronic fatigue syndrome with atypical and classical presentations. Transl Psychiatry. 2017;7(4):e1080.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Hornig M, Montoya JG, Klimas NG, Levine S, Felsenstein D, Bateman L, et al. Distinct plasma immune signatures in ME/CFS are present early in the course of illness. Sci Adv. 2015;1:1.

    Article  Google Scholar 

  7. Germain A, Barupal DK, Levine SM, Hanson MR. Comprehensive circulatory metabolomics in ME/CFS reveals disrupted metabolism of acyl lipids and steroids. Metabolites. 2020;10:1.

    Article  Google Scholar 

  8. Germain A, Ruppert D, Levine SM, Hanson MR. Metabolic profiling of a myalgic encephalomyelitis/chronic fatigue syndrome discovery cohort reveals disturbances in fatty acid and lipid metabolism. Mol Biosyst. 2017;13(2):371–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Germain A, Ruppert D, Levine SM, Hanson MR. Prospective biomarkers from plasma metabolomics of myalgic encephalomyelitis/chronic fatigue syndrome implicate redox imbalance in disease symptomatology. Metabolites. 2018;8:4.

    Article  Google Scholar 

  10. Mandarano AH, Maya J, Giloteaux L, Peterson DL, Maynard M, Gottschalk CG, et al. Myalgic encephalomyelitis/chronic fatigue syndrome patients exhibit altered T cell metabolism and cytokine associations. J Clin Invest. 2020;130(3):1491–505.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Nagy-Szakal D, Barupal DK, Lee B, Che X, Williams BL, Kahn EJR, et al. Insights into myalgic encephalomyelitis/chronic fatigue syndrome phenotypes through comprehensive metabolomics. Sci Rep. 2018;8(1):10056.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Karhan E, Gunter CL, Ravanmehr V, Horne M, Kozhaya L, Renzullo S, et al. Perturbation of effector and regulatory T cell subsets in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). BioRxiv. 2019;2019:887505.

    Google Scholar 

  13. Giloteaux L, Goodrich JK, Walters WA, Levine SM, Ley RE, Hanson MR. Reduced diversity and altered composition of the gut microbiome in individuals with myalgic encephalomyelitis/chronic fatigue syndrome. Microbiome. 2016;4(1):30.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Mandarano AH, Giloteaux L, Keller BA, Levine SM, Hanson MR. Eukaryotes in the gut microbiota in myalgic encephalomyelitis/chronic fatigue syndrome. Peer J. 2018;6:e4282.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Nagy-Szakal D, Williams BL, Mishra N, Che X, Lee B, Bateman L, et al. Fecal metagenomic profiles in subgroups of patients with myalgic encephalomyelitis/chronic fatigue syndrome. Microbiome. 2017;5(1):44.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Billing-Ross P, Germain A, Ye K, Keinan A, Gu Z, Hanson MR. Mitochondrial DNA variants correlate with symptoms in myalgic encephalomyelitis/chronic fatigue syndrome. J Transl Med. 2016;14:19.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Network [Available from:

  18. mapMECFS [Available from:

  19. Raijmakers RPH, Jansen AFM, Keijmel SP, Ter Horst R, Roerink ME, Novakovic B, et al. A possible role for mitochondrial-derived peptides humanin and MOTS-c in patients with Q fever fatigue syndrome and chronic fatigue syndrome. J Transl Med. 2019;17(1):157.

    Article  PubMed  PubMed Central  Google Scholar 

  20. CKAN.

  21. Docker

  22. AWS

  23. Amazon Relational Database Service (RDS)

  24. Amazon EC2 [Available from:

  25. Amazon Elastic Container Service

  26. Solr [Available from:

  27. Amazon CloudFront

  28. Amazon Route 53

  29. Giloteaux L, O’Neal A, Castro-Marrero J, Levine SM, Hanson MR. Cytokine profiling of extracellular vesicles isolated from plasma in myalgic encephalomyelitis/chronic fatigue syndrome: a pilot study. J Transl Med. 2020;18(1):387.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Montoya JG, Holmes TH, Anderson JN, Maecker HT, Rosenberg-Hasson Y, Valencia IJ, et al. Cytokine signature associated with disease severity in chronic fatigue syndrome patients. Proc Natl Acad Sci USA. 2017;114(34):E7150–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Kitami T, Fukuda S, Kato T, Yamaguti K, Nakatomi Y, Yamano E, et al. Deep phenotyping of myalgic encephalomyelitis/chronic fatigue syndrome in Japanese population. Sci Rep. 2020;10(1):19933.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Baraniuk JN, Kern G, Narayan V, Cheema A. Exercise modifies glutamate and other metabolic biomarkers in cerebrospinal fluid from Gulf War Illness and Myalgic encephalomyelitis / Chronic Fatigue Syndrome. PLoS ONE. 2021;16(1):e0244116.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Helliwell AM, Sweetman EC, Stockwell PA, Edgar CD, Chatterjee A, Tate WP. Changes in DNA methylation profiles of myalgic encephalomyelitis/chronic fatigue syndrome patients reflect systemic dysfunctions. Clin Epigenetics. 2020;12(1):167.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Nepotchatykh E, Elremaly W, Caraus I, Godbout C, Leveau C, Chalder L, et al. Profile of circulating microRNAs in myalgic encephalomyelitis and their relation to symptom severity, and disease pathophysiology. Sci Rep. 2020;10(1):19620.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Milivojevic M, Che X, Bateman L, Cheng A, Garcia BA, Hornig M, et al. Plasma proteomic profiling suggests an association between antigen driven clonal B cell expansion and ME/CFS. PLoS One. 2020;15(7):e0236148.

  36. Raijmakers RPH, Roerink ME, Jansen AFM, Keijmel SP, Gacesa R, Li Y, et al. Multi-omics examination of Q fever fatigue syndrome identifies similarities with chronic fatigue syndrome. J Transl Med. 2020;18(1):448.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Germain A, Levine SM, Hanson MR. In-depth analysis of the plasma proteome in ME/CFS exposes disrupted ephrin-eph and immune system signaling. Proteomes. 2021;9:1.

    Article  Google Scholar 

  38. Sweetman E, Kleffmann T, Edgar C, de Lange M, Vallings R, Tate W. A SWATH-MS analysis of myalgic encephalomyelitis/chronic fatigue syndrome peripheral blood mononuclear cell proteomes reveals mitochondrial dysfunction. J Transl Med. 2020;18(1):365.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. de Vega WC, Vernon SD, McGowan PO. DNA methylation modifications associated with chronic fatigue syndrome. PLoS ONE. 2014;9(8):e104757.

    Article  PubMed  PubMed Central  Google Scholar 

  40. de Vega WC, Herrera S, Vernon SD, McGowan PO. Epigenetic modifications and glucocorticoid sensitivity in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). BMC Med Genomics. 2017;10(1):11.

    Article  PubMed  PubMed Central  Google Scholar 

  41. de Vega W, Erdman L, Vernon SD, Goldenberg A, McGowan PO. Integration of DNA methylation & health scores identifies subtypes in myalgic encephalomyelitis/chronic fatigue syndrome. Epigenomics. 2018;10(5):539–57.

    Article  PubMed  Google Scholar 

  42. Trivedi MS, Oltra E, Sarria L, Rose N, Beljanski V, Fletcher MA, et al. Identification of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome-associated DNA methylation patterns. PLoS ONE. 2018;13(7):e0201066.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Herrera S, de Vega WC, Ashbrook D, Vernon SD, McGowan PO. Genome-epigenome interactions associated with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Epigenetics. 2018;13(12):1174–90.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Petty RD, McCarthy NE, Le Dieu R, Kerr JR. MicroRNAs hsa-miR-99b, hsa-miR-330, hsa-miR-126 and hsa-miR-30c: Potential Diagnostic Biomarkers in Natural Killer (NK) Cells of Patients with Chronic Fatigue Syndrome (CFS)/ Myalgic Encephalomyelitis (ME). PLoS ONE. 2016;11(3):e0150904.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Almenar-Perez E, Sarria L, Nathanson L, Oltra E. Assessing diagnostic value of microRNAs from peripheral blood mononuclear cells and extracellular vesicles in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Sci Rep. 2020;10(1):2064.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Bouquet J, Li T, Gardy JL, Kang X, Stevens S, Stevens J, et al. Whole blood human transcriptome and virome analysis of ME/CFS patients experiencing post-exertional malaise following cardiopulmonary exercise testing. PLoS ONE. 2019;14(3):e0212193.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Byrnes A, Jacks A, Dahlman-Wright K, Evengard B, Wright FA, Pedersen NL, et al. Gene expression in peripheral blood leukocytes in monozygotic twins discordant for chronic fatigue: no evidence of a biomarker. PLoS ONE. 2009;4(6):e5805.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Gow JW, Hagan S, Herzyk P, Cannon C, Behan PO, Chaudhuri A. A gene signature for post-infectious chronic fatigue syndrome. BMC Med Genomics. 2009;2:38.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Armstrong CW, McGregor NR, Lewis DP, Butt HL, Gooley PR. Metabolic profiling reveals anomalous energy metabolism and oxidative stress pathways in chronic fatigue syndrome patients. Metabolomics. 2015;11(6):1626–39.

    Article  CAS  Google Scholar 

  50. ckanext-searchterms.

  51. Maglott D, Ostell J, Pruitt KD, Tatusova T. Entrez Gene: gene-centered information at NCBI. Nucleic Acids Res. 2005;33(Database issue):D54–8.

  52. Heller SR, McNaught A, Pletnev I, Stein S, Tchekhovskoi D. InChI, the IUPAC international chemical identifier. J Cheminform. 2015;7:23.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Hastings J, de Matos P, Dekker A, Ennis M, Harsha B, Kale N, et al. The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013. Nucleic Acids Res. 2013;41(Database issue):D456-63.

    CAS  PubMed  Google Scholar 

  54. Wishart DS, Tzur D, Knox C, Eisner R, Guo AC, Young N, et al. HMDB: the Human Metabolome Database. Nucleic Acids Res. 2007;35(Database issue):D521-6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Kozomara A, Birgaoanu M, Griffiths-Jones S. miRBase: from microRNA sequences to function. Nucleic Acids Res. 2019;47(D1):D155–62.

    Article  CAS  PubMed  Google Scholar 

  56. ckanext-summarystats.

  57. Wilcoxon F. Individual comparisons by ranking methods. Biometrics Bulletin. 1945;1(6):80–3.

    Article  Google Scholar 

  58. Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, et al. The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data. 2016;3(1):160018.

    Article  PubMed  PubMed Central  Google Scholar 

  59. National Institutes of Health, Office of Data Science Strategy. NIH Strategic Plan for Data Science

  60. Subramanian I, Verma S, Kumar S, Jere A, Anamika K. Multi-omics data integration, interpretation, and its application. Bioinform Biol Insights. 2020;14:1177932219899051.

    Article  PubMed  PubMed Central  Google Scholar 

Download references


The authors would like to thank all members of the ME/CFS network including:

Andrew Breeden1,2, Joseph J. Breen3, JoNita Cox4, Maureen Hanson5, Keith LeGrow4, Ian Lipkin6, Taya McMillan7, Rebecca B. McNeil4, Alain Moreau8, Callie Riggs4, Derya Unutmaz9, Lawrence Whitley4, Vicky Whittemore1, Laura Elizabeth Wiener4

1National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA, 2Currently at the National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA, 3National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA, 4Biostatistics and Epidemiology Division, RTI International, Research Triangle Park, NC, USA, 5Center Director, Cornell NIH Collaborative ME/CFS Center, Cornell University, Ithaca, NY, USA, 6Center Director, Columbia Center for Solutions for ME/CFS, Columbia University, New York, NY, USA, 7Center for Communication Science, RTI International, Research Triangle Park, NC, USA, 8Center Director, Interdisciplinary Canadian Collaborative Myalgic Encephalomyelitis (ICanCME), ICanCME Research Network, Montréal, Quebec, Canada, 9Center Director, Jackson laboratory ME/CFS Collaborative Research Center, Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.


Research reported in this publication was supported by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under Award Number U24NS105535. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author information

Authors and Affiliations



RM and MC led the writing of this work. MC and AH are the Bioinformatics and development leads for mapMECFS. RM, MC, AH, AM, IT, AG, ML, MU, CT, RRE, and QB are technical staff working on the development of mapMECFS. LMB and MS are the MPIs on the ME/CFS DMCC and advised the development of mapMECFS. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Matthew Schu.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

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

Supplementary Information

Additional file 1: Figure S1 and Table S1 and S2.

Regarding mapMECFS’ authentication organizational structure, publicly curated datasets, and synonym tagging.

Additional file 2: Figure S2.

mapMECFS expected formats for (a) data (e.g., Cytokine data), (b) phenotype, (c) results files, and (d) the summary statistics file format.

Additional file 3: Figure S3.

Dataset page for mapMECFS shows the metadata included for the dataset on the left. Each of the uploaded files is shown by the title, description, and file type. Options are available to preview, download, and edit each file. mapMECFS generated files are shown directly below the uploaded dataset files.

Additional file 4: Figure S4

. Description: Results File Explorer example with a search for 4-hydroxyglutamate. The Results File Explorer shows three tables, “Data Files” and “Calculated Summary States,” “Results File,” and “Other Datasets.”

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 The Creative Commons Public Domain Dedication waiver ( 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

Mathur, R., Carnes, M.U., Harding, A. et al. mapMECFS: a portal to enhance data discovery across biological disciplines and collaborative sites. J Transl Med 19, 461 (2021).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: