@article{PiroRenard2023,
  author    = {Piro, Vitor C. and Renard, Bernhard Y.},
  title     = {Contamination detection and microbiome exploration with GRIMER},
  series = {GigaScience},
  volume    = {12},
  journal   = {GigaScience},
  publisher = {Oxford Univ. Press},
  address   = {Oxford},
  issn      = {2047-217X},
  doi       = {10.1093/gigascience/giad017},
  pages     = {13},
  year      = {2023},
  abstract  = {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 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.},
  language  = {en}
}
@article{SchutkowskiKoenigKlugeetal.2019,
  author    = {Schutkowski, Alexandra and K{\"o}nig, Bettina and Kluge, Holger and Hirche, Frank and Henze, Andrea and Schwerdtle, Tanja and Lorkowski, Stefan and Dawczynski, Christine and Gabel, Alexander and Grosse, Ivo and Stangl, Gabriele I.},
  title     = {Metabolic footprint and intestinal microbial changes in response to dietary proteins in a pig model},
  series = {The journal of nutritional biochemistry},
  volume    = {67},
  journal   = {The journal of nutritional biochemistry},
  publisher = {Elsevier},
  address   = {New York},
  issn      = {0955-2863},
  doi       = {10.1016/j.jnutbio.2019.02.004},
  pages     = {149 -- 160},
  year      = {2019},
  abstract  = {Epidemiological studies revealed that dietary proteins can contribute to the modulation of the cardiovascular disease risk. Still, direct effects of dietary proteins on serum metabolites and other health-modulating factors have not been fully explored. Here, we compared the effects of dietary lupin protein with the effects of beef protein and casein on the serum metabolite profile, cardiovascular risk markers and the fecal microbiome. Pigs were fed diets containing 15\% of the respective proteins for 4 weeks. A classification analysis of the serum metabolites revealed six biomarker sets of two metabolites each that discriminated between the intake of lupin protein, lean beef or casein. These biomarker sets included 1- and 3-methylhistidine, betaine, carnitine, homoarginine and methionine. The study revealed differences in the serum levels of the metabolites 1- and 3- methylhistidine, homoarginine, methionine and homocysteine, which are involved in the one-carbon cycle. However, these changes were not associated with differences in the methylation capacity or the histone methylation pattern. With the exception of serum homocysteine and homoarginine levels, other cardiovascular risk markers, such as the homeostatic model assessment index, trimethylamine-N-oxide and lipids, were not influenced by the dietary protein source. However, the composition of the fecal microorganisms was markedly changed by the dietary protein source. Lupin-protein-fed pigs exhibited more species from the phyla Bacteroidetes and Firmicutes than the other two groups. In conclusion, different dietary protein sources induce distinct serum metabolic fingerprints, have an impact on the cardiovascular risk and modulate the composition of the fecal microbiome. (C) 2019 Elsevier Inc. All rights reserved.},
  language  = {en}
}