Supporting data for "Contamination detection and microbiome exploration with GRIMER"

Contamination detection is an 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 are challenging tasks, especially in low-biomass samples or in studies lacking proper controls by design. Interactive visualizations and analysis platforms are crucial to better guide this step, helping to identify and detect noisy patterns that can potentially be a source of contamination. Additionally, external evidences, aggregation of methods and data and common contaminants reported in the literature can be used to discover and mitigate contamination. <br>We propose GRIMER, a tool that perform automated analysis and generates a portable and interactive dashboard integrating annotation, taxonomy and metadata. It unifies several sources of evidence towards contamination detection. GRIMER is independent of quantification methods and directly analyses contingency tables to create an interactive and offline report. Reports can be created in seconds and are accessible for non-specialists, 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 genus and 627 species reported in 22 published articles. <br>GRIMER enables visual data exploration, analysis and contamination detection in microbiome studies. The tool and data presented are open-source and available at: https://gitlab.com/dacs-hpi/grimer.