Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2
- E. Bolyen, J. Rideout, J. Caporaso
- MedicineNature Biotechnology
- 24 July 2019
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.
Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences
- M. Langille, Jesse R. Zaneveld, C. Huttenhower
- BiologyNature Biotechnology
- 25 August 2013
The results demonstrate that phylogeny and function are sufficiently linked that this 'predictive metagenomic' approach should provide useful insights into the thousands of uncultivated microbial communities for which only marker gene surveys are currently available.
PyNAST: a flexible tool for aligning sequences to a template alignment
- J. Caporaso, K. Bittinger, F. Bushman, T. DeSantis, G. Andersen, R. Knight
- Computer ScienceBioinform.
- 13 November 2009
Motivation: The Nearest Alignment Space Termination (NAST) tool is commonly used in sequence-based microbial ecology community analysis, but due to the limited portability of the original…
Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing
- N. Bokulich, Sathish Subramanian, J. Caporaso
- BiologyNature Methods
- 21 November 2012
It is demonstrated that high-quality read length and abundance are the primary factors differentiating correct from erroneous reads produced by Illumina GAIIx, HiSeq and MiSeq instruments.
Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms
- J. Caporaso, C. Lauber, R. Knight
- BiologyThe ISME Journal
- 8 March 2012
It is shown that the protocol developed for these instruments successfully recaptures known biological results, and additionally that biological conclusions are consistent across sequencing platforms (the HiSeq2000 versus the MiSeq) and across the sequenced regions of amplicons.
Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample
- J. Caporaso, C. Lauber, R. Knight
- BiologyProceedings of the National Academy of Sciences…
- 3 June 2010
This work sequences a diverse array of 25 environmental samples and three known “mock communities” at a depth averaging 3.1 million reads per sample to demonstrate excellent consistency in taxonomic recovery and recapture diversity patterns that were previously reported on the basis of metaanalysis of many studies from the literature.
Soil bacterial and fungal communities across a pH gradient in an arable soil
Soils collected across a long-term liming experiment were used to investigate the direct influence of pH on the abundance and composition of the two major soil microbial taxa, fungi and bacteria, and both the relative abundance and diversity of bacteria were positively related to pH.
Using QIIME to Analyze 16S rRNA Gene Sequences from Microbial Communities
- Justin Kuczynski, J. Stombaugh, William A. Walters, Antonio Gonzalez, J. Caporaso, R. Knight
- BiologyCurrent Protocols in Bioinformatics
- 1 December 2011
The following protocols describe how to install QIIME on a single computer and use it to analyze microbial 16S sequence data from nine distinct microbial communities.
Species-level functional profiling of metagenomes and metatranscriptomes
- E. Franzosa, L. McIver, C. Huttenhower
- BiologyNature Methods
- 21 September 2018
HUMAnN2 is developed, a tiered search strategy that enables fast, accurate, and species-resolved functional profiling of host-associated and environmental communities and introduces ‘contributional diversity’ to explain patterns of ecological assembly across different microbial community types.
Cross-biome metagenomic analyses of soil microbial communities and their functional attributes
- N. Fierer, J. Leff, J. Caporaso
- Biology, MedicineProceedings of the National Academy of Sciences…
- 10 December 2012
As the most comprehensive survey of soil taxonomic, phylogenetic, and functional diversity to date, this study demonstrates that metagenomic approaches can be used to build a predictive understanding of how microbial diversity and function vary across terrestrial biomes.
...
...