Contamination detection and microbiome exploration with GRIMER
- Background: Contamination detection is a important step that should be carefully considered in early stages when designing and performing microbiome studies to avoid biased outcomes. Detecting and removing true contaminants is challenging, especially in low-biomass samples or in studies lacking proper controls. Interactive visualizations and analysis platforms are crucial to better guide this step, to help to identify and detect noisy patterns that could potentially be contamination. Additionally, external evidence, like aggregation of several contamination detection methods and the use of common contaminants reported in the literature, could help to discover and mitigate contamination. Results: We propose GRIMER, a tool that performs automated analyses and generates a portable and interactive dashboard integrating annotation, taxonomy, and metadata. It unifies several sources of evidence to help detect contamination. GRIMER is independent of quantification methods and directly analyzes contingency tables to create anBackground: Contamination detection is a important step that should be carefully considered in early stages when designing and performing microbiome studies to avoid biased outcomes. Detecting and removing true contaminants is challenging, especially in low-biomass samples or in studies lacking proper controls. Interactive visualizations and analysis platforms are crucial to better guide this step, to help to identify and detect noisy patterns that could potentially be contamination. Additionally, external evidence, like aggregation of several contamination detection methods and the use of common contaminants reported in the literature, could help to discover and mitigate contamination. Results: We propose GRIMER, a tool that performs automated analyses and generates a portable and interactive dashboard integrating annotation, taxonomy, and metadata. It unifies several sources of evidence to help detect contamination. GRIMER is independent of quantification methods and directly analyzes contingency tables to create an interactive and offline report. Reports can be created in seconds and are accessible for nonspecialists, providing an intuitive set of charts to explore data distribution among observations and samples and its connections with external sources. Further, we compiled and used an extensive list of possible external contaminant taxa and common contaminants with 210 genera and 627 species reported in 22 published articles. Conclusion: GRIMER enables visual data exploration and analysis, supporting contamination detection in microbiome studies. The tool and data presented are open source and available at https://gitlab.com/dacs-hpi/grimer.…
Author details: | Vitor C. PiroORCiD, Bernhard Y. RenardORCiDGND |
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DOI: | https://doi.org/10.1093/gigascience/giad017 |
ISSN: | 2047-217X |
Pubmed ID: | https://pubmed.ncbi.nlm.nih.gov/36994872 |
Title of parent work (English): | GigaScience |
Publisher: | Oxford Univ. Press |
Place of publishing: | Oxford |
Publication type: | Article |
Language: | English |
Date of first publication: | 2023/03/30 |
Publication year: | 2023 |
Release date: | 2024/06/20 |
Tag: | Contamination; Microbiome; Taxonomy; Visualization |
Volume: | 12 |
Article number: | giad017 |
Number of pages: | 13 |
Funding institution: | Deutsche Forschungsgemeinschaft (DFG, German Research Foundation); [458163427]; German Ministry for Education and Research; (Bundesministerium fur Bildung und Forschung - BMBF) [01KI1905D];; OpenAccess Publication Fund of Freie Universitat Berlin |
Organizational units: | Digital Engineering Fakultät / Hasso-Plattner-Institut für Digital Engineering GmbH |
DDC classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 000 Informatik, Informationswissenschaft, allgemeine Werke |
Peer review: | Referiert |
Publishing method: | Open Access / Gold Open-Access |
License (German): | CC-BY - Namensnennung 4.0 International |