Literature DB >> 29321060

"Available upon request": not good enough for microbiome data!

Morgan G I Langille1, Jacques Ravel2, W Florian Fricke3,4.   

Abstract

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Year:  2018        PMID: 29321060      PMCID: PMC5761205          DOI: 10.1186/s40168-017-0394-z

Source DB:  PubMed          Journal:  Microbiome        ISSN: 2049-2618            Impact factor:   14.650


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Open data that is free and publicly available without restrictions is critical for progress in any scientific discipline and has been the cornerstone of sound and reproducible genomics research. Microbiome research is still a relatively young, thriving, active research field, with great biomedical potential. As a large data-driven research field, microbiome projects can include hundreds or even thousands of participants, samples, and associated background (“metadata”) parameters. Processing this data, identifying meaningful associations, and determining significance depends on complex, often non-standardized bioinformatics and biostatistics protocols. Reproducibility, transparency, and expandability of these protocols to review, evaluate, and build upon this work is crucial to fulfill on the promise of microbiome research and maintain credibility. At the absolute minimum, unrestricted access to the raw sequencing data and associated metadata is needed and has been recognized and implemented by the scientific community, some journals, and funding agencies. In practice, access to open protocols for data processing and analysis is also important to promote reproducibility and advances in the field but rarely provided. Unfortunately, there appears to be an increasing number of studies that are failing to satisfy even basic, community-accepted standards. Motivated by a number of recent negative experiences in our own research projects, as well as our interaction with authors aiming to publish in Microbiome, this editorial aims to shed light on common problems in the field and make recommendations to reinforce a culture of open data and protocols for microbiome research. Access to sequence data is required by most peer-reviewed journals. However, when we attempted to access published sequence and metadata from microbiome projects, we have often encountered missing, incomplete, inconsistent and/or incomprehensible sequence and metadata, and reluctance by authors, editors, and publishers to react to our complaints. Authors increasingly use new models for data distribution, which restrict or limit data access. Data is only made “available upon request” or access granted based on non-transparent, arbitrary, and costly application procedures. Reproducibility is further complicated by the limited availability of bioinformatic and biostatistic protocols, including software versions, program parameters, and code of software scripts. Although personal instances will vary, examples like the one highlighted in Table 1 are commonplace and largely unreported. We believe that the field would greatly benefit from an improved open data and open protocol culture. In the following, we outline a number of recommendations, which we have begun implementing at Microbiome:
Table 1

Personal experience

The following example was picked, because it represents a high-profile microbiome project with one of the most extensive collections of microbial sequence and health-related human background data to date [1]. As such, it could be a tremendous resource for extended research by the scientific community and has been of interest to on-going projects by the authors of this editorial.
Instead of simply obtaining the data through direct download from one of the existing publicly funded repositories, we were forced to undertake several time-consuming tasks. Here are the steps we took to obtain a particular dataset before eventually giving up:
1. Sent an email requesting the data and our intended use of the data.
 • Wait 1 month for response.
2. Obtained response indicating that we need to first fill out a three-page form including what data we want, the title of our project, a summary of the research proposal, our expertise in analyzing this data, and a recent publication record.
 • Wait 2 months for approval.
3. Were then sent a “Data Transfer Agreement” that needs to be signed by our institution.
 • Wait 2 weeks for reply from institution.
4. Were asked to provide a copy of ethical approval for our project, which we do not have and would not need if the data were publicly available.
 • Instead of waiting yet another month for ethics approval, we decide to abandon this dataset for our scientific plan.
Free unrestricted access to data and metadata, non-commercial bioinformatic software, options and code of published analysis should be given at the time of manuscript peer review and ongoing once published. Released data and protocols should encompass all parameters and analyses (including the code and scripts used) that are part of the publications and needed to fully reproduce its results. Journal peer review guidelines should be extended to include checking compliance with open data and protocol guidelines. Journal responsibilities should be extended and reinforced to control compliance and to react to non-compliance. Personal experience We are concerned that recent trends will continue and that they will set the precedent for data access restriction, greatly limiting scientific progress and reproducibility. We should note that some may try to contest open data access under the veil of privacy, but while data must be handled ethically, the public release of non-identifiable molecular data that has already led to publishable results must be the minimum moral/scientific standard to which researchers must be held. Further, funding agencies (public and private) should require their grantees to be fully compliant with open data access policies and endorse open data guidelines developed by the scientific community. We would encourage all microbiome researchers including authors, editors, and peer reviewers to stand up for open data access in order to ensure progress, credibility, and reproducibility in this rapidly developing research field.
  1 in total

1.  Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity.

Authors:  Alexandra Zhernakova; Alexander Kurilshikov; Marc Jan Bonder; Ettje F Tigchelaar; Melanie Schirmer; Tommi Vatanen; Zlatan Mujagic; Arnau Vich Vila; Gwen Falony; Sara Vieira-Silva; Jun Wang; Floris Imhann; Eelke Brandsma; Soesma A Jankipersadsing; Marie Joossens; Maria Carmen Cenit; Patrick Deelen; Morris A Swertz; Rinse K Weersma; Edith J M Feskens; Mihai G Netea; Dirk Gevers; Daisy Jonkers; Lude Franke; Yurii S Aulchenko; Curtis Huttenhower; Jeroen Raes; Marten H Hofker; Ramnik J Xavier; Cisca Wijmenga; Jingyuan Fu
Journal:  Science       Date:  2016-04-28       Impact factor: 47.728

  1 in total
  15 in total

Review 1.  Methods in Lung Microbiome Research.

Authors:  Sharon M Carney; Jose C Clemente; Michael J Cox; Robert P Dickson; Yvonne J Huang; Georgios D Kitsios; Kirsten M Kloepfer; Janice M Leung; Tricia D LeVan; Philip L Molyneaux; Bethany B Moore; David N O'Dwyer; Leopoldo N Segal; Stavros Garantziotis
Journal:  Am J Respir Cell Mol Biol       Date:  2020-03       Impact factor: 6.914

Review 2.  Ecosystem-specific microbiota and microbiome databases in the era of big data.

Authors:  Victor Lobanov; Angélique Gobet; Alyssa Joyce
Journal:  Environ Microbiome       Date:  2022-07-16

Review 3.  Multi-Omics Strategies for Investigating the Microbiome in Toxicology Research.

Authors:  Ethan W Morgan; Gary H Perdew; Andrew D Patterson
Journal:  Toxicol Sci       Date:  2022-05-26       Impact factor: 4.109

4.  Exploring Linkages between Taxonomic and Functional Profiles of the Human Microbiome.

Authors:  Morgan G I Langille
Journal:  mSystems       Date:  2018-03-27       Impact factor: 6.496

5.  Identifying and Overcoming Threats to Reproducibility, Replicability, Robustness, and Generalizability in Microbiome Research.

Authors:  Patrick D Schloss
Journal:  mBio       Date:  2018-06-05       Impact factor: 7.867

6.  Current progress and future opportunities in applications of bioinformatics for biodefense and pathogen detection: report from the Winter Mid-Atlantic Microbiome Meet-up, College Park, MD, January 10, 2018.

Authors:  Jacquelyn S Meisel; Daniel J Nasko; Brian Brubach; Victoria Cepeda-Espinoza; Jessica Chopyk; Héctor Corrada-Bravo; Marcus Fedarko; Jay Ghurye; Kiran Javkar; Nathan D Olson; Nidhi Shah; Sarah M Allard; Adam L Bazinet; Nicholas H Bergman; Alexis Brown; J Gregory Caporaso; Sean Conlan; Jocelyne DiRuggiero; Samuel P Forry; Nur A Hasan; Jason Kralj; Paul M Luethy; Donald K Milton; Brian D Ondov; Sarah Preheim; Shashikala Ratnayake; Stephanie M Rogers; M J Rosovitz; Eric G Sakowski; Nils Oliver Schliebs; Daniel D Sommer; Krista L Ternus; Gherman Uritskiy; Sean X Zhang; Mihai Pop; Todd J Treangen
Journal:  Microbiome       Date:  2018-11-05       Impact factor: 14.650

Review 7.  Systematic benchmarking of omics computational tools.

Authors:  Serghei Mangul; Lana S Martin; Brian L Hill; Angela Ka-Mei Lam; Margaret G Distler; Alex Zelikovsky; Eleazar Eskin; Jonathan Flint
Journal:  Nat Commun       Date:  2019-03-27       Impact factor: 14.919

8.  Issues and current standards of controls in microbiome research.

Authors:  Bastian V H Hornung; Romy D Zwittink; Ed J Kuijper
Journal:  FEMS Microbiol Ecol       Date:  2019-05-01       Impact factor: 4.194

Review 9.  Advances in Understanding the Human Urinary Microbiome and Its Potential Role in Urinary Tract Infection.

Authors:  Michael L Neugent; Neha V Hulyalkar; Vivian H Nguyen; Philippe E Zimmern; Nicole J De Nisco
Journal:  mBio       Date:  2020-04-28       Impact factor: 7.867

10.  Qiita: rapid, web-enabled microbiome meta-analysis.

Authors:  Antonio Gonzalez; Jose A Navas-Molina; Tomasz Kosciolek; Daniel McDonald; Yoshiki Vázquez-Baeza; Gail Ackermann; Jeff DeReus; Stefan Janssen; Austin D Swafford; Stephanie B Orchanian; Jon G Sanders; Joshua Shorenstein; Hannes Holste; Semar Petrus; Adam Robbins-Pianka; Colin J Brislawn; Mingxun Wang; Jai Ram Rideout; Evan Bolyen; Matthew Dillon; J Gregory Caporaso; Pieter C Dorrestein; Rob Knight
Journal:  Nat Methods       Date:  2018-10-01       Impact factor: 28.547

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