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1. Enabling Reproducible Microbiome Science through Decentralized Provenance Tracking in QIIME 2

Author

Naimey, Ahmad Turan, Keefe, Christopher, Bolyen, Evan, Dillon, Matthew R, and J Gregory Caporaso

Abstract

We demonstrate the ways in which automatic, integrated, decentralized provenance tracking in QIIME 2, a leading microbiome bioinformatics platform, enables reproducible microbiome science. We use sample data from a recent study of arid soil microbiomes (Significant Impacts of Increasing Aridity on the Arid Soil Microbiome; Neilson et al, 2017[2]), to illustrate specific analyses that QIIME 2 supports, and to frame our discussion of the QIIME 2 platform. QIIME 2 actions yield as outputs Artifacts integrating the requested data or visualization with comprehensive data provenance that describes the computational history of that data or visualization, including all methods and parameters involved in its creation. This approach gives users, reviewers, and readers powerful tools for understanding, reproducing, and extending studies. The benefits this approach provides to both the researcher and the scientific community are significant, and provide a useful model for research software developers across disciplines.

Published
2019
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2. QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science

Author

Daniel Petras, Mehrbod Estaki, Kyle C. Weber, Yilong Zhang, Emily Vogtmann, Kestrel Gorlick, Eric J. Alm, Benjamin J. Callahan, Evan Bolyen, Alexey V. Melnik, Yang Bai, Gabriel A. Al-Ghalith, Louis-Félix Nothias, Elmar Pruesse, Massoud Maher, Zhenjiang Zech Xu, Gavin M. Douglas, Dan Knights, Stefan Janssen, Christopher R. Keefe, Jonathan Warren, Yong-Xin Liu, Chase Hd Williamson, Manimozhiyan Arumugam, Ii Michael S Robeson, Justin J. J. van der Hooft, Kyle Bittinger, Alan K. Jarmusch, Antonio Gonzalez, Sydney C. Morgan, Paul Keim, Stephanie B. Orchanian, Jessica L. Metcalf, Bryan D Martin, Jordan E. Bisanz, Andrés Mauricio Caraballo-Rodríguez, Curtis Huttenhower, Peter J. Turnbaugh, Arron Shiffer, Nicola Segata, Samuel L. Peoples, Hannes Holste, Ruth E. Ley, Rashmi Sinha, Ahmad Turan Naimey, Talima Pearson, Jose A. Navas-Molina, Francesco Asnicar, Michael Shaffer, Daniel McDonald, Gavin A. Huttley, Kyo Bin Kang, Jorden Kreps, Luke R. Thompson, Daniel M. Durall, Benjamin D. Kaehler, Anupriya Tripathi, Max von Hippel, Catherine A. Lozupone, Joslynn S. Lee, John Chase, Nicholas A. Bokulich, Colin J. Brislawn, Pauline Trinh, C. Titus Brown, Harriet Alexander, Matthew R. Dillon, Clarisse Marotz, Ricardo Silva, Jamie Morton, Morgan G. I. Langille, Mary L. Preuss, Deanna L. Gibson, Pieter C. Dorrestein, J. Gregory Caporaso, Pedro J. Torres, Yunhu Wan, Christian F. Edwardson, William A. Walters, Claire Duvallet, Benjamin Hillmann, Erikka Loftfield, Mingxun Wang, Fernando Vargas, Patrick Rosenthal, Susan Holmes, Se Jin Song, Madeleine Ernst, Irina Koester, Sabah Ul-Hasan, Lasse Buur Rasmussen, Rob Knight, Christian C. Abnet, Lauren J. McIver, Austin D. Swafford, Emily K. Cope, Lingjing Jiang, Jiarong Guo, Tomasz Kosciolek, Scott T. Kelley, Julia M. Gauglitz, Yoshiki Vázquez-Baeza, Amy D. Willis, Adam R. Rivers, Jennifer Fouquier, Jesse R. Zaneveld, John R. Spear, Jai Ram Rideout, and Asker Daniel Brejnrod

Subjects
Automated data, Computer science, Scalability, Microbiome, Data science, Shotgun metagenomics, Visualization
Abstract

We present QIIME 2, an open-source microbiome data science platform accessible to users spanning the microbiome research ecosystem, from scientists and engineers to clinicians and policy makers. QIIME 2 provides new features that will drive the next generation of microbiome research. These include interactive spatial and temporal analysis and visualization tools, support for metabolomics and shotgun metagenomics analysis, and automated data provenance tracking to ensure reproducible, transparent microbiome data science.

Published
2018
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3. Author Correction: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2

Author

Jamie Morton, Ruth E. Ley, Samuel L. Peoples, Dan Knights, Yong-Xin Liu, Gavin M. Douglas, Colin J. Brislawn, Fernando Vargas, Yunhu Wan, Daniel McDonald, Sean M. Gibbons, Daniel Petras, Amy D. Willis, Stephanie B. Orchanian, Rashmi Sinha, J. Gregory Caporaso, Gabriel A. Al-Ghalith, Claire Duvallet, Adam R. Rivers, Kyo Bin Kang, Lasse Buur Rasmussen, Pieter C. Dorrestein, Benjamin J. Callahan, Michael Shaffer, Rob Knight, Patrick Rosenthal, Madeleine Ernst, Matthew R. Dillon, Christian F. Edwardson, Qiyun Zhu, Yang Bai, Alexey V. Melnik, Daniel M. Durall, Christian C. Abnet, Lauren J. McIver, Julia M. Gauglitz, Jiarong Guo, Joslynn S. Lee, Charles H. D. Williamson, Tomasz Kosciolek, Massoud Maher, Emily Vogtmann, Mary L. Preuss, Kyle Bittinger, Alan K. Jarmusch, Mingxun Wang, Jose A. Navas-Molina, Susan Holmes, Michael S. Robeson, Manimozhiyan Arumugam, C. Titus Brown, Zhenjiang Zech Xu, Harriet Alexander, Nicola Segata, John Chase, Francesco Asnicar, Se Jin Song, Yoshiki Vázquez-Baeza, Sydney C. Morgan, Austin D. Swafford, Justin J. J. van der Hooft, Irina Koester, Stefan Janssen, Ahmad Turan Naimey, Antonio Gonzalez, Jessica L. Metcalf, Gavin A. Huttley, Elmar Pruesse, Scott T. Kelley, Jonathan Warren, Talima Pearson, Anupriya Tripathi, Luke R. Thompson, Sabah Ul-Hasan, Peter J. Turnbaugh, Emily K. Cope, Christopher R. Keefe, Hannes Holste, Catherine A. Lozupone, Ricardo Silva, Pedro J. Torres, Max von Hippel, Eric J. Alm, Evan Bolyen, Jordan E. Bisanz, Lingjing Jiang, Louis-Félix Nothias, Andrés Mauricio Caraballo-Rodríguez, William A. Walters, Curtis Huttenhower, Clarisse Marotz, Kyle C. Weber, Yilong Zhang, Kestrel Gorlick, Paul Keim, Asker Daniel Brejnrod, Deanna L. Gibson, Morgan G. I. Langille, Jennifer Fouquier, Jesse R. Zaneveld, Benjamin Hillmann, Mehrbod Estaki, John R. Spear, Jai Ram Rideout, Jorden Kreps, Benjamin D. Kaehler, Pauline Trinh, Nicholas A. Bokulich, Erikka Loftfield, Bryan D Martin, Arron Shiffer, and Christian Diener

Subjects
0303 health sciences, Information retrieval, Bioinformatics, Computer science, Published Erratum, Biomedical Engineering, mothur, Bioengineering, Applied Microbiology and Biotechnology, Bioconductor, 03 medical and health sciences, 0302 clinical medicine, Bioinformatica, Life Science, Molecular Medicine, 030217 neurology & neurosurgery, 030304 developmental biology, Biotechnology
Abstract

In the version of this article initially published, some reference citations were incorrect. The three references to Jupyter Notebooks should have cited Kluyver et al. instead of Gonzalez et al. The reference to Qiita should have cited Gonzalez et al. instead of Schloss et al. The reference to mothur should have cited Schloss et al. instead of McMurdie & Holmes. The reference to phyloseq should have cited McMurdie & Holmes instead of Huber et al. The reference to Bioconductor should have cited Huber et al. instead of Franzosa et al. And the reference to the biobakery suite should have cited Franzosa et al. instead of Kluyver et al. The errors have been corrected in the HTML and PDF versions of the article.

Published
2019
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4. Comparison of histologic stains in the diagnosis of Pneumocystis carinii

Author

G L, Naimey and R B, Wuerker

Subjects
AIDS-Related Opportunistic Infections, Staining and Labeling, Pneumocystis, Pneumonia, Pneumocystis, Histological Techniques, Humans, Coloring Agents
Abstract

To evaluate commonly used histologic stains used to detect Pneumocystis, including Diff-Quik (DQ), Grocott's Gomori methenamine silver (GMS), Papanicolaou, and hematoxylin and eosin (HE).The study compared the ability of these stains to detect alveolar casts.Although there was a slightly higher average number of casts detected with DQ, the difference between this stain and GMS was not significant. Both Papanicolaou stain and HE showed significantly lower averages. Some differences in HE may be related to differences in preparation.Similar results were obtained with DQ and GMS, but DQ offered the advantage of ease of preparation, rapidity and cost-effectiveness.

Published
1995

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