The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
QIIME 2 Enables Comprehensive End‐to‐End Analysis of Diverse Microbiome Data and Comparative Studies with Publicly Available Data
Open accessAbstract
QIIME 2 is a completely re‐engineered microbiome bioinformatics platform based on the popular QIIME platform, which it has replaced. QIIME 2 facilitates comprehensive and fully reproducible microbiome data science, improving accessibility to diverse users by adding multiple user interfaces. QIIME 2 can be combined with Qiita, an open‐source web‐based platform, to re‐use available data for meta‐analysis. The following basic protocol describes how to install QIIME 2 on a single computer and analyze microbiome sequence data, from processing of raw DNA sequence reads through generating publishable interactive figures. These interactive figures allow readers of a study to interact with data with the same ease as its authors, advancing microbiome science transparency and reproducibility. We also show how plug‐ins developed by the community to add analysis capabilities can be installed and used with QIIME 2, enhancing various aspects of microbiome analyses—e.g., improving taxonomic classification accuracy. Finally, we illustrate how users can perform meta‐analyses combining different datasets using readily available public data through Qiita. In this tutorial, we analyze a subset of the Early Childhood Antibiotics and the Microbiome (ECAM) study, which tracked the microbiome composition and development of 43 infants in the United States from birth to 2 years of age, identifying microbiome associations with antibiotic exposure, delivery mode, and diet. For more information about QIIME 2, see https://qiime2.org . To troubleshoot or ask questions about QIIME 2 and microbiome analysis, join the active community at https://forum.qiime2.org . © 2020 The Authors. Basic Protocol : Using QIIME 2 with microbiome data Support Protocol : Further microbiome analyses
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Cites
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
Cutadapt removes adapter sequences from high-throughput sequencing reads
Martin, M. (2011) · EMBnet.journal · doi:10.14806/ej.17.1.200 · Cited by 33,889
Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2
Bolyen, E., Rideout, J. R., Dillon, M. R., Bokulich, N. A., Abnet, C. C., Al-Ghalith, G. A., Alexander, H., Alm, E. J., Arumugam, M., Asnicar, F., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0209-9 · Cited by 23,742
Self-citationFastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments
Price, M. N., Dehal, P. S., & Arkin, A. P. (2010) · PLoS ONE · doi:10.1371/journal.pone.0009490 · Cited by 14,894
UniFrac: a New Phylogenetic Method for Comparing Microbial Communities
Lozupone, C., & Knight, R. (2005) · Applied and Environmental Microbiology · doi:10.1128/aem.71.12.8228-8235.2005 · Cited by 8,227
Self-citationMicrobiome Datasets Are Compositional: And This Is Not Optional
Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V., & Egozcue, J. J. (2017) · Frontiers in Microbiology · doi:10.3389/fmicb.2017.02224 · Cited by 3,044
MetaPhlAn2 for enhanced metagenomic taxonomic profiling
Truong, D. T., Franzosa, E. A., Tickle, T. L., Scholz, M., Weingart, G., Pasolli, E., Tett, A., Huttenhower, C., & Segata, N. (2015) · Nature Methods · doi:10.1038/nmeth.3589 · Cited by 2,185
Best practices for analysing microbiomes
Knight, R., Vrbanac, A., Taylor, B. C., Aksenov, A., Callewaert, C., Debelius, J., Gonzalez, A., Kosciolek, T., McCall, L.-I., McDonald, D., et al. (2018) · Nature Reviews Microbiology · doi:10.1038/s41579-018-0029-9 · Cited by 2,030
Self-citationAnalysis of composition of microbiomes: a novel method for studying microbial composition
Mandal, S., Van Treuren, W., White, R. A., Eggesbø, M., Knight, R., & Peddada, S. D. (2015) · Microbial Ecology in Health & Disease · doi:10.3402/mehd.v26.27663 · Cited by 1,942
Self-citationAntibiotics, birth mode, and diet shape microbiome maturation during early life
Bokulich, N. A., Chung, J., Battaglia, T., Henderson, N., Jay, M., Li, H., D. Lieber, A., Wu, F., Perez-Perez, G. I., Chen, Y., et al. (2016) · Science Translational Medicine · doi:10.1126/scitranslmed.aad7121 · Cited by 1,469
Self-citationFernandes, A. D., Reid, J. N., Macklaim, J. M., McMurrough, T. A., Edgell, D. R., & Gloor, G. B. (2014) · Microbiome · doi:10.1186/2049-2618-2-15 · Cited by 1,329
Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights
Pasolli, E., Truong, D. T., Malik, F., Waldron, L., & Segata, N. (2016) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004977 · Cited by 659
Evaluating the Information Content of Shallow Shotgun Metagenomics
Hillmann, B., Al-Ghalith, G. A., Shields-Cutler, R. R., Zhu, Q., Gohl, D. M., Beckman, K. B., Knight, R., & Knights, D. (2018) · mSystems · doi:10.1128/msystems.00069-18 · Cited by 474 · cited 3×
“In the example below, we demonstrate the use of q2-shogun , a wrapper for the SHOGUN pipeline (Hillmann et al., 2018 --i-reference-reads refseqs.qza \ --i-reference-taxonomy taxonomy.qza \ --i-database bt2-database.qza \ --o-taxa-table taxatable.qza In this example, SHOGUN is called to align query…”
BackgroundMethodInfluentialSelf-citationIt's all relative: analyzing microbiome data as compositions
Gloor, G. B., Wu, J. R., Pawlowsky-Glahn, V., & Egozcue, J. J. (2016) · Annals of Epidemiology · doi:10.1016/j.annepidem.2016.03.003 · Cited by 323
A broken promise: microbiome differential abundance methods do not control the false discovery rate
Hawinkel, S., Mattiello, F., Bijnens, L., & Thas, O. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbx104 · Cited by 199
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Hall, D. A., Voigt, R. M., Cantu-Jungles, T. M., Hamaker, B., Engen, P. A., Shaikh, M., Raeisi, S., Green, S. J., Naqib, A., Forsyth, C. B., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36497-x · Cited by 124
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Bacterial induction of B cell senescence promotes age-related changes in the gut microbiota
Kawamoto, S., Uemura, K., Hori, N., Takayasu, L., Konishi, Y., Katoh, K., Matsumoto, T., Suzuki, M., Sakai, Y., Matsudaira, T., et al. (2023) · Nature Cell Biology · doi:10.1038/s41556-023-01145-5 · Cited by 91
Deep Nasal Sinus Cavity Microbiota Dysbiosis in Parkinson’s Disease
Pal, G., Ramirez, V., Engen, P. A., Naqib, A., Forsyth, C. B., Green, S. J., Mahdavinia, M., Batra, P. S., Tajudeen, B. A., & Keshavarzian, A. (2021) · npj Parkinson s Disease · doi:10.1038/s41531-021-00254-y · Cited by 28
Improving microbial phylogeny with citizen science within a mass-market video game
Sarrazin-Gendron, R., Ghasemloo Gheidari, P., Butyaev, A., Keding, T., Cai, E., Zheng, J., Mutalova, R., Mounthanyvong, J., Zhu, Y., Nazarova, E., et al. (2025) · Nature Biotechnology · doi:10.1038/s41587-024-02175-6 · Cited by 9
Self-citation
Metadata
- Authors
- Estaki, M., Jiang, L., Bokulich, N. A., McDonald, D., González, A., Kosciolek, T., Martino, C., Zhu, Q., Birmingham, A., Vázquez‐Baeza, Y., Dillon, M. R., Bolyen, E., Caporaso, J. G., & Knight, R.
- Venue
- Current Protocols in Bioinformatics
- Publisher
- Wiley
- Published
- 2020-04-28
- Volume
- 70
- Issue
- 1
- Pages
- e100-e100
- ISSN
- 1934-3396 (ISSN-L)
- 1934-340X
- OpenAlex
- W3019168548
- Cited by
- 565
- References
- 15
- FWCI
- 13.90
- Type
- journal article
- Language
- en
- Open access
- hybrid
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