BeerDeCoded: the open beer metagenome project.
Jonathan Aryeh SobelLuc HenryNicolas RotmanGianpaolo RandoPublished in: F1000Research (2017)
Next generation sequencing has radically changed research in the life sciences, in both academic and corporate laboratories. The potential impact is tremendous, yet a majority of citizens have little or no understanding of the technological and ethical aspects of this widespread adoption. We designed BeerDeCoded as a pretext to discuss the societal issues related to genomic and metagenomic data with fellow citizens, while advancing scientific knowledge of the most popular beverage of all. In the spirit of citizen science, sample collection and DNA extraction were carried out with the participation of non-scientists in the community laboratory of Hackuarium, a not-for-profit organisation that supports unconventional research and promotes the public understanding of science. The dataset presented herein contains the targeted metagenomic profile of 39 bottled beers from 5 countries, based on internal transcribed spacer (ITS) sequencing of fungal species. A preliminary analysis reveals the presence of a large diversity of wild yeast species in commercial brews. With this project, we demonstrate that coupling simple laboratory procedures that can be carried out in a non-professional environment with state-of-the-art sequencing technologies and targeted metagenomic analyses, can lead to the detection and identification of the microbial content in bottled beer.
Keyphrases
- antibiotic resistance genes
- healthcare
- quality improvement
- circulating tumor
- microbial community
- public health
- electronic health record
- single cell
- mental health
- cancer therapy
- copy number
- genetic diversity
- physical activity
- cell wall
- minimally invasive
- single molecule
- decision making
- cell free
- emergency department
- loop mediated isothermal amplification
- drug delivery
- bioinformatics analysis
- circulating tumor cells
- gene expression
- climate change
- machine learning
- artificial intelligence
- deep learning
- adverse drug