Qiyun Zhu

TL;DR: QIIME 2 development was primarily funded by NSF Awards 1565100 to J.G.C. and R.K.P. and partial support was also provided by the following: grants NIH U54CA143925 and U54MD012388.

TL;DR: A meta-analysis of microbial community samples collected by hundreds of researchers for the Earth Microbiome Project is presented, creating both a reference database giving global context to DNA sequence data and a framework for incorporating data from future studies, fostering increasingly complete characterization of Earth’s microbial diversity.

TL;DR: This Review focuses on recent findings that suggest that operational taxonomic unit-based analyses should be replaced with new methods that are based on exact sequence variants, methods for integrating metagenomic and metabolomic data, and issues surrounding compositional data analysis.

TL;DR: The utility of the living data resource and cross-cohort comparison is demonstrated to confirm existing associations between the microbiome and psychiatric illness and to reveal the extent of microbiome change within one individual during surgery, providing a paradigm for open microbiome research and education.

TL;DR: Microbial nucleic acids are detected in samples of tissues and blood from more than 10,000 patients with cancer, and machine learning is used to show that these can be used to discriminate between and among different types of cancer, suggesting a new microbiome-based diagnostic approach.