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Keefe CR, Dillon MR, Gehret E, Herman C, Jewell M, Wood CV, Bolyen E, Caporaso JG. Facilitating bioinformatics reproducibility with QIIME 2 Provenance Replay. PLoS Comput Biol 2023; 19:e1011676. [PMID: 38011287 PMCID: PMC10703398 DOI: 10.1371/journal.pcbi.1011676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 12/07/2023] [Accepted: 11/10/2023] [Indexed: 11/29/2023] Open
Abstract
Study reproducibility is essential to corroborate, build on, and learn from the results of scientific research but is notoriously challenging in bioinformatics, which often involves large data sets and complex analytic workflows involving many different tools. Additionally, many biologists are not trained in how to effectively record their bioinformatics analysis steps to ensure reproducibility, so critical information is often missing. Software tools used in bioinformatics can automate provenance tracking of the results they generate, removing most barriers to bioinformatics reproducibility. Here we present an implementation of that idea, Provenance Replay, a tool for generating new executable code from results generated with the QIIME 2 bioinformatics platform, and discuss considerations for bioinformatics developers who wish to implement similar functionality in their software.
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Affiliation(s)
- Christopher R. Keefe
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, United States of America
| | - Matthew R. Dillon
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, United States of America
| | - Elizabeth Gehret
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, United States of America
| | - Chloe Herman
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, United States of America
- School of Informatics, Computing and Cyber Systems, Northern Arizona University, Flagstaff, Arizona, United States of America
| | - Mary Jewell
- Department of Epidemiology, University of Washington, Seattle, Washington, United States of America
| | - Colin V. Wood
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, United States of America
| | - Evan Bolyen
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, United States of America
| | - J. Gregory Caporaso
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, United States of America
- School of Informatics, Computing and Cyber Systems, Northern Arizona University, Flagstaff, Arizona, United States of America
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2
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Borsom EM, Conn K, Keefe CR, Herman C, Orsini GM, Hirsch AH, Palma Avila M, Testo G, Jaramillo SA, Bolyen E, Lee K, Caporaso JG, Cope EK. Predicting Neurodegenerative Disease Using Prepathology Gut Microbiota Composition: a Longitudinal Study in Mice Modeling Alzheimer's Disease Pathologies. Microbiol Spectr 2023; 11:e0345822. [PMID: 36877047 PMCID: PMC10101110 DOI: 10.1128/spectrum.03458-22] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 01/12/2023] [Indexed: 03/07/2023] Open
Abstract
The gut microbiota-brain axis is suspected to contribute to the development of Alzheimer's disease (AD), a neurodegenerative disease characterized by amyloid-β plaque deposition, neurofibrillary tangles, and neuroinflammation. To evaluate the role of the gut microbiota-brain axis in AD, we characterized the gut microbiota of female 3xTg-AD mice modeling amyloidosis and tauopathy and wild-type (WT) genetic controls. Fecal samples were collected fortnightly from 4 to 52 weeks, and the V4 region of the 16S rRNA gene was amplified and sequenced on an Illumina MiSeq. RNA was extracted from the colon and hippocampus, converted to cDNA, and used to measure immune gene expression using reverse transcriptase quantitative PCR (RT-qPCR). Diversity metrics were calculated using QIIME2, and a random forest classifier was applied to predict bacterial features that are important in predicting mouse genotype. Gene expression of glial fibrillary acidic protein (GFAP; indicating astrocytosis) was elevated in the colon at 24 weeks. Markers of Th1 inflammation (il6) and microgliosis (mrc1) were elevated in the hippocampus. Gut microbiota were compositionally distinct early in life between 3xTg-AD mice and WT mice (permutational multivariate analysis of variance [PERMANOVA], 8 weeks, P = 0.001, 24 weeks, P = 0.039, and 52 weeks, P = 0.058). Mouse genotypes were correctly predicted 90 to 100% of the time using fecal microbiome composition. Finally, we show that the relative abundance of Bacteroides species increased over time in 3xTg-AD mice. Taken together, we demonstrate that changes in bacterial gut microbiota composition at prepathology time points are predictive of the development of AD pathologies. IMPORTANCE Recent studies have demonstrated alterations in the gut microbiota composition in mice modeling Alzheimer's disease (AD) pathologies; however, these studies have only included up to 4 time points. Our study is the first of its kind to characterize the gut microbiota of a transgenic AD mouse model, fortnightly, from 4 weeks of age to 52 weeks of age, to quantify the temporal dynamics in the microbial composition that correlate with the development of disease pathologies and host immune gene expression. In this study, we observed temporal changes in the relative abundances of specific microbial taxa, including the genus Bacteroides, that may play a central role in disease progression and the severity of pathologies. The ability to use features of the microbiota to discriminate between mice modeling AD and wild-type mice at prepathology time points indicates a potential role of the gut microbiota as a risk or protective factor in AD.
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Affiliation(s)
- Emily M. Borsom
- Center for Applied Microbiome Sciences, the Pathogen and Microbiome Institute, Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona, USA
| | - Kathryn Conn
- Center for Applied Microbiome Sciences, the Pathogen and Microbiome Institute, Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona, USA
| | - Christopher R. Keefe
- Center for Applied Microbiome Sciences, the Pathogen and Microbiome Institute, Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona, USA
| | - Chloe Herman
- Center for Applied Microbiome Sciences, the Pathogen and Microbiome Institute, Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona, USA
| | - Gabrielle M. Orsini
- Center for Applied Microbiome Sciences, the Pathogen and Microbiome Institute, Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona, USA
| | - Allyson H. Hirsch
- Center for Applied Microbiome Sciences, the Pathogen and Microbiome Institute, Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona, USA
| | - Melanie Palma Avila
- Center for Applied Microbiome Sciences, the Pathogen and Microbiome Institute, Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona, USA
| | - George Testo
- Center for Applied Microbiome Sciences, the Pathogen and Microbiome Institute, Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona, USA
| | - Sierra A. Jaramillo
- Center for Applied Microbiome Sciences, the Pathogen and Microbiome Institute, Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona, USA
| | - Evan Bolyen
- Center for Applied Microbiome Sciences, the Pathogen and Microbiome Institute, Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona, USA
| | - Keehoon Lee
- Center for Applied Microbiome Sciences, the Pathogen and Microbiome Institute, Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona, USA
| | - J. Gregory Caporaso
- Center for Applied Microbiome Sciences, the Pathogen and Microbiome Institute, Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona, USA
| | - Emily K. Cope
- Center for Applied Microbiome Sciences, the Pathogen and Microbiome Institute, Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona, USA
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3
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Estaki M, Jiang L, Bokulich NA, McDonald D, González A, Kosciolek T, Martino C, Zhu Q, Birmingham A, Vázquez-Baeza Y, Dillon MR, Bolyen E, Caporaso JG, Knight R. QIIME 2 Enables Comprehensive End-to-End Analysis of Diverse Microbiome Data and Comparative Studies with Publicly Available Data. ACTA ACUST UNITED AC 2021; 70:e100. [PMID: 32343490 PMCID: PMC9285460 DOI: 10.1002/cpbi.100] [Citation(s) in RCA: 165] [Impact Index Per Article: 55.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
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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Affiliation(s)
- Mehrbod Estaki
- Department of Pediatrics, University of California San Diego, La Jolla, California
| | - Lingjing Jiang
- Division of Biostatistics, University of California San Diego, La Jolla, California
| | - Nicholas A Bokulich
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, Arizona.,Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona
| | - Daniel McDonald
- Department of Pediatrics, University of California San Diego, La Jolla, California
| | - Antonio González
- Department of Pediatrics, University of California San Diego, La Jolla, California
| | - Tomasz Kosciolek
- Department of Pediatrics, University of California San Diego, La Jolla, California.,Małopolska Centre of Biotechnology, Jagiellonian University, Kraków, Poland
| | - Cameron Martino
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, California.,Center for Microbiome Innovation, University of California San Diego, La Jolla, California
| | - Qiyun Zhu
- Department of Pediatrics, University of California San Diego, La Jolla, California
| | - Amanda Birmingham
- Center for Computational Biology and Bioinformatics, University of California San Diego, La Jolla, California
| | - Yoshiki Vázquez-Baeza
- Center for Microbiome Innovation, University of California San Diego, La Jolla, California.,Jacobs School of Engineering, University of California San Diego, La Jolla, California
| | - Matthew R Dillon
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, Arizona
| | - Evan Bolyen
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, Arizona
| | - J Gregory Caporaso
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, Arizona.,Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona
| | - Rob Knight
- Department of Pediatrics, University of California San Diego, La Jolla, California.,Center for Microbiome Innovation, University of California San Diego, La Jolla, California.,Department of Computer Science and Engineering, University of California San Diego, La Jolla, California.,Department of Bioengineering, University of California San Diego, La Jolla, California
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4
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Ladner JT, Larsen BB, Bowers JR, Hepp CM, Bolyen E, Folkerts M, Sheridan K, Pfeiffer A, Yaglom H, Lemmer D, Sahl JW, Kaelin EA, Maqsood R, Bokulich NA, Quirk G, Watts TD, Komatsu KK, Waddell V, Lim ES, Caporaso JG, Engelthaler DM, Worobey M, Keim P. An Early Pandemic Analysis of SARS-CoV-2 Population Structure and Dynamics in Arizona. mBio 2020; 11:e02107-20. [PMID: 32887735 PMCID: PMC7474171 DOI: 10.1128/mbio.02107-20] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 08/13/2020] [Indexed: 02/06/2023] Open
Abstract
In December of 2019, a novel coronavirus, SARS-CoV-2, emerged in the city of Wuhan, China, causing severe morbidity and mortality. Since then, the virus has swept across the globe, causing millions of confirmed infections and hundreds of thousands of deaths. To better understand the nature of the pandemic and the introduction and spread of the virus in Arizona, we sequenced viral genomes from clinical samples tested at the TGen North Clinical Laboratory, the Arizona Department of Health Services, and those collected as part of community surveillance projects at Arizona State University and the University of Arizona. Phylogenetic analysis of 84 genomes from across Arizona revealed a minimum of 11 distinct introductions inferred to have occurred during February and March. We show that >80% of our sequences descend from strains that were initially circulating widely in Europe but have since dominated the outbreak in the United States. In addition, we show that the first reported case of community transmission in Arizona descended from the Washington state outbreak that was discovered in late February. Notably, none of the observed transmission clusters are epidemiologically linked to the original travel-related case in the state, suggesting successful early isolation and quarantine. Finally, we use molecular clock analyses to demonstrate a lack of identifiable, widespread cryptic transmission in Arizona prior to the middle of February 2020.IMPORTANCE As the COVID-19 pandemic swept across the United States, there was great differential impact on local and regional communities. One of the earliest and hardest hit regions was in New York, while at the same time Arizona (for example) had low incidence. That situation has changed dramatically, with Arizona now having the highest rate of disease increase in the country. Understanding the roots of the pandemic during the initial months is essential as the pandemic continues and reaches new heights. Genomic analysis and phylogenetic modeling of SARS-COV-2 in Arizona can help to reconstruct population composition and predict the earliest undetected introductions. This foundational work represents the basis for future analysis and understanding as the pandemic continues.
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Affiliation(s)
- Jason T Ladner
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, USA
| | - Brendan B Larsen
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, Arizona, USA
| | - Jolene R Bowers
- Pathogen and Microbiome Division, Translational Genomics Research Institute, Flagstaff, Arizona, USA
| | - Crystal M Hepp
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, USA
- School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, Arizona, USA
| | - Evan Bolyen
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, USA
| | - Megan Folkerts
- Pathogen and Microbiome Division, Translational Genomics Research Institute, Flagstaff, Arizona, USA
| | - Krystal Sheridan
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, USA
| | - Ashlyn Pfeiffer
- Pathogen and Microbiome Division, Translational Genomics Research Institute, Flagstaff, Arizona, USA
| | - Hayley Yaglom
- Pathogen and Microbiome Division, Translational Genomics Research Institute, Flagstaff, Arizona, USA
| | - Darrin Lemmer
- Pathogen and Microbiome Division, Translational Genomics Research Institute, Flagstaff, Arizona, USA
| | - Jason W Sahl
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, USA
| | - Emily A Kaelin
- School of Life Sciences, Arizona State University, Tempe, Arizona, USA
- Center for Fundamental and Applied Microbiomics, Biodesign Institute, Tempe, Arizona, USA
| | - Rabia Maqsood
- Center for Fundamental and Applied Microbiomics, Biodesign Institute, Tempe, Arizona, USA
| | - Nicholas A Bokulich
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, USA
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, USA
- Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona, USA
| | - Grace Quirk
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, Arizona, USA
| | - Thomas D Watts
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, Arizona, USA
| | | | - Victor Waddell
- Bureau of Laboratory Services, Arizona Department of Health Services, Phoenix, Arizona, USA
| | - Efrem S Lim
- School of Life Sciences, Arizona State University, Tempe, Arizona, USA
- Center for Fundamental and Applied Microbiomics, Biodesign Institute, Tempe, Arizona, USA
| | - J Gregory Caporaso
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, USA
- Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona, USA
| | - David M Engelthaler
- Pathogen and Microbiome Division, Translational Genomics Research Institute, Flagstaff, Arizona, USA
| | - Michael Worobey
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, Arizona, USA
| | - Paul Keim
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, USA
- Pathogen and Microbiome Division, Translational Genomics Research Institute, Flagstaff, Arizona, USA
- Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona, USA
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5
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Bolyen E, Dillon MR, Bokulich NA, Ladner JT, Larsen BB, Hepp CM, Lemmer D, Sahl JW, Sanchez A, Holdgraf C, Sewell C, Choudhury AG, Stachurski J, McKay M, Simard A, Engelthaler DM, Worobey M, Keim P, Caporaso JG. Reproducibly sampling SARS-CoV-2 genomes across time, geography, and viral diversity. F1000Res 2020; 9:657. [PMID: 33500774 PMCID: PMC7814287 DOI: 10.12688/f1000research.24751.1] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/20/2020] [Indexed: 08/03/2023] Open
Abstract
The COVID-19 pandemic has led to a rapid accumulation of SARS-CoV-2 genomes, enabling genomic epidemiology on local and global scales. Collections of genomes from resources such as GISAID must be subsampled to enable computationally feasible phylogenetic and other analyses. We present genome-sampler, a software package that supports sampling collections of viral genomes across multiple axes including time of genome isolation, location of genome isolation, and viral diversity. The software is modular in design so that these or future sampling approaches can be applied independently and combined (or replaced with a random sampling approach) to facilitate custom workflows and benchmarking. genome-sampler is written as a QIIME 2 plugin, ensuring that its application is fully reproducible through QIIME 2's unique retrospective data provenance tracking system. genome-sampler can be installed in a conda environment on macOS or Linux systems. A complete default pipeline is available through a Snakemake workflow, so subsampling can be achieved using a single command. genome-sampler is open source, free for all to use, and available at https://caporasolab.us/genome-sampler. We hope that this will facilitate SARS-CoV-2 research and support evaluation of viral genome sampling approaches for genomic epidemiology.
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Affiliation(s)
- Evan Bolyen
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
- School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA
| | - Matthew R. Dillon
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Nicholas A. Bokulich
- Laboratory of Food Systems Biotechnology, Institute of Food, Nutrition and Health, ETH Zurich, Switzerland
| | - Jason T. Ladner
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Brendan B. Larsen
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA
| | - Crystal M. Hepp
- School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Darrin Lemmer
- Pathogen and Microbiome Division, Translational Genomics Research Institute, Flagstaff, AZ, USA
| | - Jason W. Sahl
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
- Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA
| | - Andrew Sanchez
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Chris Holdgraf
- Department of Statistics, University of California at Berkeley, Berkeley, CA, USA
| | - Chris Sewell
- Theory and Simulation of Materials, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Aakash G. Choudhury
- Research School of Economics, Australian National University, ACT, Australia
| | - John Stachurski
- Research School of Economics, Australian National University, ACT, Australia
| | - Matthew McKay
- Research School of Economics, Australian National University, ACT, Australia
| | - Anthony Simard
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - David M. Engelthaler
- Pathogen and Microbiome Division, Translational Genomics Research Institute, Flagstaff, AZ, USA
| | - Michael Worobey
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA
| | - Paul Keim
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
- Pathogen and Microbiome Division, Translational Genomics Research Institute, Flagstaff, AZ, USA
- Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA
| | - J. Gregory Caporaso
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
- Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA
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6
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Bolyen E, Dillon MR, Bokulich NA, Ladner JT, Larsen BB, Hepp CM, Lemmer D, Sahl JW, Sanchez A, Holdgraf C, Sewell C, Choudhury AG, Stachurski J, McKay M, Simard A, Engelthaler DM, Worobey M, Keim P, Caporaso JG. Reproducibly sampling SARS-CoV-2 genomes across time, geography, and viral diversity. F1000Res 2020; 9:657. [PMID: 33500774 PMCID: PMC7814287 DOI: 10.12688/f1000research.24751.2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/20/2020] [Indexed: 11/20/2022] Open
Abstract
The COVID-19 pandemic has led to a rapid accumulation of SARS-CoV-2 genomes, enabling genomic epidemiology on local and global scales. Collections of genomes from resources such as GISAID must be subsampled to enable computationally feasible phylogenetic and other analyses. We present genome-sampler, a software package that supports sampling collections of viral genomes across multiple axes including time of genome isolation, location of genome isolation, and viral diversity. The software is modular in design so that these or future sampling approaches can be applied independently and combined (or replaced with a random sampling approach) to facilitate custom workflows and benchmarking. genome-sampler is written as a QIIME 2 plugin, ensuring that its application is fully reproducible through QIIME 2’s unique retrospective data provenance tracking system. genome-sampler can be installed in a conda environment on macOS or Linux systems. A complete default pipeline is available through a Snakemake workflow, so subsampling can be achieved using a single command. genome-sampler is open source, free for all to use, and available at
https://caporasolab.us/genome-sampler. We hope that this will facilitate SARS-CoV-2 research and support evaluation of viral genome sampling approaches for genomic epidemiology.
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Affiliation(s)
- Evan Bolyen
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA.,School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA
| | - Matthew R Dillon
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Nicholas A Bokulich
- Laboratory of Food Systems Biotechnology, Institute of Food, Nutrition and Health, ETH Zurich, Switzerland
| | - Jason T Ladner
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Brendan B Larsen
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA
| | - Crystal M Hepp
- School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA.,Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Darrin Lemmer
- Pathogen and Microbiome Division, Translational Genomics Research Institute, Flagstaff, AZ, USA
| | - Jason W Sahl
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA.,Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA
| | - Andrew Sanchez
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Chris Holdgraf
- Department of Statistics, University of California at Berkeley, Berkeley, CA, USA
| | - Chris Sewell
- Theory and Simulation of Materials, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Aakash G Choudhury
- Research School of Economics, Australian National University, ACT, Australia
| | - John Stachurski
- Research School of Economics, Australian National University, ACT, Australia
| | - Matthew McKay
- Research School of Economics, Australian National University, ACT, Australia
| | - Anthony Simard
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - David M Engelthaler
- Pathogen and Microbiome Division, Translational Genomics Research Institute, Flagstaff, AZ, USA
| | - Michael Worobey
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA
| | - Paul Keim
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA.,Pathogen and Microbiome Division, Translational Genomics Research Institute, Flagstaff, AZ, USA.,Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA
| | - J Gregory Caporaso
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA.,Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA
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7
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Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, Alexander H, Alm EJ, Arumugam M, Asnicar F, Bai Y, Bisanz JE, Bittinger K, Brejnrod A, Brislawn CJ, Brown CT, Callahan BJ, Caraballo-Rodríguez AM, Chase J, Cope EK, Da Silva R, Diener C, Dorrestein PC, Douglas GM, Durall DM, Duvallet C, Edwardson CF, Ernst M, Estaki M, Fouquier J, Gauglitz JM, Gibbons SM, Gibson DL, Gonzalez A, Gorlick K, Guo J, Hillmann B, Holmes S, Holste H, Huttenhower C, Huttley GA, Janssen S, Jarmusch AK, Jiang L, Kaehler BD, Kang KB, Keefe CR, Keim P, Kelley ST, Knights D, Koester I, Kosciolek T, Kreps J, Langille MGI, Lee J, Ley R, Liu YX, Loftfield E, Lozupone C, Maher M, Marotz C, Martin BD, McDonald D, McIver LJ, Melnik AV, Metcalf JL, Morgan SC, Morton JT, Naimey AT, Navas-Molina JA, Nothias LF, Orchanian SB, Pearson T, Peoples SL, Petras D, Preuss ML, Pruesse E, Rasmussen LB, Rivers A, Robeson MS, Rosenthal P, Segata N, Shaffer M, Shiffer A, Sinha R, Song SJ, Spear JR, Swafford AD, Thompson LR, Torres PJ, Trinh P, Tripathi A, Turnbaugh PJ, Ul-Hasan S, van der Hooft JJJ, Vargas F, Vázquez-Baeza Y, Vogtmann E, von Hippel M, Walters W, Wan Y, Wang M, Warren J, Weber KC, Williamson CHD, Willis AD, Xu ZZ, Zaneveld JR, Zhang Y, Zhu Q, Knight R, Caporaso JG. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol 2019; 37:852-857. [PMID: 31341288 DOI: 10.1038/s41587-019-0209-9] [Citation(s) in RCA: 8073] [Impact Index Per Article: 1614.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Evan Bolyen
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Jai Ram Rideout
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Matthew R Dillon
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Nicholas A Bokulich
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Christian C Abnet
- Metabolic Epidemiology Branch, National Cancer Institute, Rockville, MD, USA
| | - Gabriel A Al-Ghalith
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA
| | - Harriet Alexander
- Biology Department, Woods Hole Oceanographic Institution, Woods Hole, MA, USA.,Department of Population Health and Reproduction, University of California, Davis, Davis, CA, USA
| | - Eric J Alm
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.,Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Manimozhiyan Arumugam
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Yang Bai
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China.,Centre of Excellence for Plant and Microbial Sciences (CEPAMS), Institute of Genetics and Developmental Biology, Chinese Academy of Sciences & John Innes Centre, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Jordan E Bisanz
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA
| | - Kyle Bittinger
- Division of Gastroenterology and Nutrition, Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Hepatology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Asker Brejnrod
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Colin J Brislawn
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - C Titus Brown
- Department of Population Health and Reproduction, University of California, Davis, Davis, CA, USA
| | - Benjamin J Callahan
- Department of Population Health & Pathobiology, North Carolina State University, Raleigh, NC, USA.,Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
| | - Andrés Mauricio Caraballo-Rodríguez
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | - John Chase
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Emily K Cope
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA.,Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA
| | - Ricardo Da Silva
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | | | - Pieter C Dorrestein
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | - Gavin M Douglas
- Department of Microbiology and Immunology, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Daniel M Durall
- Irving K. Barber School of Arts and Sciences, University of British Columbia, Kelowna, British Columbia, Canada
| | - Claire Duvallet
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Christian F Edwardson
- A. Watson Armour III Center for Animal Health and Welfare, Aquarium Microbiome Project, John G. Shedd Aquarium, Chicago, IL, USA
| | - Madeleine Ernst
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA.,Department of Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Mehrbod Estaki
- Department of Biology, University of British Columbia Okanagan, Okanagan, British Columbia, Canada
| | - Jennifer Fouquier
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.,Department of Medicine, Division of Biomedical Informatics and Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Julia M Gauglitz
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | - Sean M Gibbons
- Institute for Systems Biology, Seattle, WA, USA.,eScience Institute, University of Washington, Seattle, WA, USA
| | - Deanna L Gibson
- Irving K. Barber School of Arts and Sciences, Department of Biology, University of British Columbia, Kelowna, British Columbia, Canada.,Department of Medicine, University of British Columbia, Kelowna, British Columbia, Canada
| | - Antonio Gonzalez
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Kestrel Gorlick
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Jiarong Guo
- Center for Microbial Ecology, Michigan State University, East Lansing, MI, USA
| | - Benjamin Hillmann
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA
| | - Susan Holmes
- Statistics Department, Stanford University, Palo Alto, CA, USA
| | - Hannes Holste
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.,Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
| | - Curtis Huttenhower
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Gavin A Huttley
- Research School of Biology, The Australian National University, Canberra, Australian Capital Territory, Australia
| | - Stefan Janssen
- Department of Pediatric Oncology, Hematology and Clinical Immunology, Heinrich-Heine University Dusseldorf, Dusseldorf, Germany
| | - Alan K Jarmusch
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | - Lingjing Jiang
- Department of Family Medicine and Public Health, University of California San Diego, La Jolla, CA, USA
| | - Benjamin D Kaehler
- Research School of Biology, The Australian National University, Canberra, Australian Capital Territory, Australia.,School of Science, University of New South Wales, Canberra, Australian Capital Territory, Australia
| | - Kyo Bin Kang
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA.,College of Pharmacy, Sookmyung Women's University, Seoul, Republic of Korea
| | - Christopher R Keefe
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Paul Keim
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Scott T Kelley
- Department of Biology, San Diego State University, San Diego, CA, USA
| | - Dan Knights
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA.,Biotechnology Institute, University of Minnesota, Saint Paul, MN, USA
| | - Irina Koester
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA.,Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
| | - Tomasz Kosciolek
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Jorden Kreps
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Morgan G I Langille
- Department of Pharmacology, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Joslynn Lee
- Science Education, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Ruth Ley
- Department of Microbiome Science, Max Planck Institute for Developmental Biology, Tübingen, Germany.,Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY, USA
| | - Yong-Xin Liu
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China.,Centre of Excellence for Plant and Microbial Sciences (CEPAMS), Institute of Genetics and Developmental Biology, Chinese Academy of Sciences & John Innes Centre, Beijing, China
| | - Erikka Loftfield
- Metabolic Epidemiology Branch, National Cancer Institute, Rockville, MD, USA
| | - Catherine Lozupone
- Department of Medicine, Division of Biomedical Informatics and Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Massoud Maher
- Department of Computer Science & Engineering, University of California San Diego, La Jolla, CA, USA
| | - Clarisse Marotz
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Bryan D Martin
- Department of Statistics, University of Washington, Seattle, WA, USA
| | - Daniel McDonald
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Lauren J McIver
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Alexey V Melnik
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | - Jessica L Metcalf
- Department of Animal Science, Colorado State University, Fort Collins, CO, USA
| | - Sydney C Morgan
- Irving K. Barber School of Arts and Sciences, Unit 2 (Biology), University of British Columbia, Kelowna, British Columbia, Canada
| | - Jamie T Morton
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.,Department of Computer Science & Engineering, University of California San Diego, La Jolla, CA, USA
| | - Ahmad Turan Naimey
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Jose A Navas-Molina
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.,Department of Computer Science & Engineering, University of California San Diego, La Jolla, CA, USA.,Google LLC, Mountain View, CA, USA
| | - Louis Felix Nothias
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | - Stephanie B Orchanian
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA
| | - Talima Pearson
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Samuel L Peoples
- School of Information Studies, Syracuse University, Syracuse, NY, USA.,School of STEM, University of Washington Bothell, Bothell, WA, USA
| | - Daniel Petras
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | - Mary Lai Preuss
- Department of Biological Sciences, Webster University, St. Louis, MO, USA
| | - Elmar Pruesse
- Department of Medicine, Division of Biomedical Informatics and Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Lasse Buur Rasmussen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Adam Rivers
- Agricultural Research Service, Genomics and Bioinformatics Research Unit, United States Department of Agriculture, Gainesville, FL, USA
| | - Michael S Robeson
- College of Medicine, Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Patrick Rosenthal
- Department of Biological Sciences, Webster University, St. Louis, MO, USA
| | - Nicola Segata
- Centre for Integrative Biology, University of Trento, Trento, Italy
| | - Michael Shaffer
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.,Department of Medicine, Division of Biomedical Informatics and Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Arron Shiffer
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Rashmi Sinha
- Metabolic Epidemiology Branch, National Cancer Institute, Rockville, MD, USA
| | - Se Jin Song
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - John R Spear
- Department of Civil and Environmental Engineering, Colorado School of Mines, Golden, CO, USA
| | - Austin D Swafford
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA
| | - Luke R Thompson
- Department of Biological Sciences and Northern Gulf Institute, University of Southern Mississippi, Hattiesburg, MS, USA.,Ocean Chemistry and Ecosystems Division, Atlantic Oceanographic and Meteorological Laboratory, National Oceanic and Atmospheric Administration, La Jolla, CA, USA
| | - Pedro J Torres
- Department of Biology, San Diego State University, San Diego, CA, USA
| | - Pauline Trinh
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA
| | - Anupriya Tripathi
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA.,Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.,Division of Biological Sciences, University of California San Diego, San Diego, CA, USA
| | - Peter J Turnbaugh
- Department of Microbiology and Immunology, University of California San Francisco, San Francisco, CA, USA
| | - Sabah Ul-Hasan
- Quantitative and Systems Biology Graduate Program, University of California Merced, Merced, CA, USA
| | | | - Fernando Vargas
- Division of Biological Sciences, University of California San Diego, San Diego, CA, USA
| | | | - Emily Vogtmann
- Metabolic Epidemiology Branch, National Cancer Institute, Rockville, MD, USA
| | - Max von Hippel
- Department of Mathematics, University of Arizona, Tucson, AZ, USA
| | - William Walters
- Department of Microbiome Science, Max Planck Institute for Developmental Biology, Tübingen, Germany
| | - Yunhu Wan
- Metabolic Epidemiology Branch, National Cancer Institute, Rockville, MD, USA
| | - Mingxun Wang
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | - Jonathan Warren
- National Laboratory Service, Environment Agency, Starcross, UK
| | - Kyle C Weber
- Agricultural Research Service, Genomics and Bioinformatics Research Unit, United States Department of Agriculture, Gainesville, FL, USA.,College of Agriculture and Life Sciences, University of Florida, Gainesville, FL, USA
| | | | - Amy D Willis
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Zhenjiang Zech Xu
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Jesse R Zaneveld
- School of STEM, Division of Biological Sciences, University of Washington Bothell, Bothell, WA, USA
| | | | - Qiyun Zhu
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Rob Knight
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.,Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA.,Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
| | - J Gregory Caporaso
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA. .,Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA.
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8
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Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, Alexander H, Alm EJ, Arumugam M, Asnicar F, Bai Y, Bisanz JE, Bittinger K, Brejnrod A, Brislawn CJ, Brown CT, Callahan BJ, Caraballo-Rodríguez AM, Chase J, Cope EK, Da Silva R, Diener C, Dorrestein PC, Douglas GM, Durall DM, Duvallet C, Edwardson CF, Ernst M, Estaki M, Fouquier J, Gauglitz JM, Gibbons SM, Gibson DL, Gonzalez A, Gorlick K, Guo J, Hillmann B, Holmes S, Holste H, Huttenhower C, Huttley GA, Janssen S, Jarmusch AK, Jiang L, Kaehler BD, Kang KB, Keefe CR, Keim P, Kelley ST, Knights D, Koester I, Kosciolek T, Kreps J, Langille MGI, Lee J, Ley R, Liu YX, Loftfield E, Lozupone C, Maher M, Marotz C, Martin BD, McDonald D, McIver LJ, Melnik AV, Metcalf JL, Morgan SC, Morton JT, Naimey AT, Navas-Molina JA, Nothias LF, Orchanian SB, Pearson T, Peoples SL, Petras D, Preuss ML, Pruesse E, Rasmussen LB, Rivers A, Robeson MS, Rosenthal P, Segata N, Shaffer M, Shiffer A, Sinha R, Song SJ, Spear JR, Swafford AD, Thompson LR, Torres PJ, Trinh P, Tripathi A, Turnbaugh PJ, Ul-Hasan S, van der Hooft JJJ, Vargas F, Vázquez-Baeza Y, Vogtmann E, von Hippel M, Walters W, Wan Y, Wang M, Warren J, Weber KC, Williamson CHD, Willis AD, Xu ZZ, Zaneveld JR, Zhang Y, Zhu Q, Knight R, Caporaso JG. Author Correction: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol 2019; 37:1091. [PMID: 31399723 DOI: 10.1038/s41587-019-0252-6] [Citation(s) in RCA: 281] [Impact Index Per Article: 56.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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Affiliation(s)
- Evan Bolyen
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Jai Ram Rideout
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Matthew R Dillon
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Nicholas A Bokulich
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Christian C Abnet
- Metabolic Epidemiology Branch, National Cancer Institute, Rockville, MD, USA
| | - Gabriel A Al-Ghalith
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA
| | - Harriet Alexander
- Biology Department, Woods Hole Oceanographic Institution, Woods Hole, MA, USA.,Department of Population Health and Reproduction, University of California, Davis, Davis, CA, USA
| | - Eric J Alm
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.,Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Manimozhiyan Arumugam
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Yang Bai
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China.,Centre of Excellence for Plant and Microbial Sciences (CEPAMS), Institute of Genetics and Developmental Biology, Chinese Academy of Sciences & John Innes Centre, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Jordan E Bisanz
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA
| | - Kyle Bittinger
- Division of Gastroenterology and Nutrition, Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Hepatology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Asker Brejnrod
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Colin J Brislawn
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - C Titus Brown
- Department of Population Health and Reproduction, University of California, Davis, Davis, CA, USA
| | - Benjamin J Callahan
- Department of Population Health & Pathobiology, North Carolina State University, Raleigh, NC, USA.,Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
| | - Andrés Mauricio Caraballo-Rodríguez
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | - John Chase
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Emily K Cope
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA.,Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA
| | - Ricardo Da Silva
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | | | - Pieter C Dorrestein
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | - Gavin M Douglas
- Department of Microbiology and Immunology, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Daniel M Durall
- Irving K. Barber School of Arts and Sciences, University of British Columbia, Kelowna, British Columbia, Canada
| | - Claire Duvallet
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Christian F Edwardson
- A. Watson Armour III Center for Animal Health and Welfare, Aquarium Microbiome Project, John G. Shedd Aquarium, Chicago, IL, USA
| | - Madeleine Ernst
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA.,Department of Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Mehrbod Estaki
- Department of Biology, University of British Columbia Okanagan, Okanagan, British Columbia, Canada
| | - Jennifer Fouquier
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.,Department of Medicine, Division of Biomedical Informatics and Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Julia M Gauglitz
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | - Sean M Gibbons
- Institute for Systems Biology, Seattle, WA, USA.,eScience Institute, University of Washington, Seattle, WA, USA
| | - Deanna L Gibson
- Irving K. Barber School of Arts and Sciences, Department of Biology, University of British Columbia, Kelowna, British Columbia, Canada.,Department of Medicine, University of British Columbia, Kelowna, British Columbia, Canada
| | - Antonio Gonzalez
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Kestrel Gorlick
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Jiarong Guo
- Center for Microbial Ecology, Michigan State University, East Lansing, MI, USA
| | - Benjamin Hillmann
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA
| | - Susan Holmes
- Statistics Department, Stanford University, Palo Alto, CA, USA
| | - Hannes Holste
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.,Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
| | - Curtis Huttenhower
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Gavin A Huttley
- Research School of Biology, The Australian National University, Canberra, Australian Capital Territory, Australia
| | - Stefan Janssen
- Department of Pediatric Oncology, Hematology and Clinical Immunology, Heinrich-Heine University Dusseldorf, Dusseldorf, Germany
| | - Alan K Jarmusch
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | - Lingjing Jiang
- Department of Family Medicine and Public Health, University of California San Diego, La Jolla, CA, USA
| | - Benjamin D Kaehler
- Research School of Biology, The Australian National University, Canberra, Australian Capital Territory, Australia.,School of Science, University of New South Wales, Canberra, Australian Capital Territory, Australia
| | - Kyo Bin Kang
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA.,College of Pharmacy, Sookmyung Women's University, Seoul, Republic of Korea
| | - Christopher R Keefe
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Paul Keim
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Scott T Kelley
- Department of Biology, San Diego State University, San Diego, CA, USA
| | - Dan Knights
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA.,Biotechnology Institute, University of Minnesota, Saint Paul, MN, USA
| | - Irina Koester
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA.,Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
| | - Tomasz Kosciolek
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Jorden Kreps
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Morgan G I Langille
- Department of Pharmacology, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Joslynn Lee
- Science Education, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Ruth Ley
- Department of Microbiome Science, Max Planck Institute for Developmental Biology, Tübingen, Germany.,Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY, USA
| | - Yong-Xin Liu
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China.,Centre of Excellence for Plant and Microbial Sciences (CEPAMS), Institute of Genetics and Developmental Biology, Chinese Academy of Sciences & John Innes Centre, Beijing, China
| | - Erikka Loftfield
- Metabolic Epidemiology Branch, National Cancer Institute, Rockville, MD, USA
| | - Catherine Lozupone
- Department of Medicine, Division of Biomedical Informatics and Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Massoud Maher
- Department of Computer Science & Engineering, University of California San Diego, La Jolla, CA, USA
| | - Clarisse Marotz
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Bryan D Martin
- Department of Statistics, University of Washington, Seattle, WA, USA
| | - Daniel McDonald
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Lauren J McIver
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Alexey V Melnik
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | - Jessica L Metcalf
- Department of Animal Science, Colorado State University, Fort Collins, CO, USA
| | - Sydney C Morgan
- Irving K. Barber School of Arts and Sciences, Unit 2 (Biology), University of British Columbia, Kelowna, British Columbia, Canada
| | - Jamie T Morton
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.,Department of Computer Science & Engineering, University of California San Diego, La Jolla, CA, USA
| | - Ahmad Turan Naimey
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Jose A Navas-Molina
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.,Department of Computer Science & Engineering, University of California San Diego, La Jolla, CA, USA.,Google LLC, Mountain View, CA, USA
| | - Louis Felix Nothias
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | - Stephanie B Orchanian
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA
| | - Talima Pearson
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Samuel L Peoples
- School of Information Studies, Syracuse University, Syracuse, NY, USA.,School of STEM, University of Washington Bothell, Bothell, WA, USA
| | - Daniel Petras
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | - Mary Lai Preuss
- Department of Biological Sciences, Webster University, St. Louis, MO, USA
| | - Elmar Pruesse
- Department of Medicine, Division of Biomedical Informatics and Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Lasse Buur Rasmussen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Adam Rivers
- Agricultural Research Service, Genomics and Bioinformatics Research Unit, United States Department of Agriculture, Gainesville, FL, USA
| | - Michael S Robeson
- College of Medicine, Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Patrick Rosenthal
- Department of Biological Sciences, Webster University, St. Louis, MO, USA
| | - Nicola Segata
- Centre for Integrative Biology, University of Trento, Trento, Italy
| | - Michael Shaffer
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.,Department of Medicine, Division of Biomedical Informatics and Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Arron Shiffer
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Rashmi Sinha
- Metabolic Epidemiology Branch, National Cancer Institute, Rockville, MD, USA
| | - Se Jin Song
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - John R Spear
- Department of Civil and Environmental Engineering, Colorado School of Mines, Golden, CO, USA
| | - Austin D Swafford
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA
| | - Luke R Thompson
- Department of Biological Sciences and Northern Gulf Institute, University of Southern Mississippi, Hattiesburg, MS, USA.,Ocean Chemistry and Ecosystems Division, Atlantic Oceanographic and Meteorological Laboratory, National Oceanic and Atmospheric Administration, La Jolla, CA, USA
| | - Pedro J Torres
- Department of Biology, San Diego State University, San Diego, CA, USA
| | - Pauline Trinh
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA
| | - Anupriya Tripathi
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA.,Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.,Division of Biological Sciences, University of California San Diego, San Diego, CA, USA
| | - Peter J Turnbaugh
- Department of Microbiology and Immunology, University of California San Francisco, San Francisco, CA, USA
| | - Sabah Ul-Hasan
- Quantitative and Systems Biology Graduate Program, University of California Merced, Merced, CA, USA
| | | | - Fernando Vargas
- Division of Biological Sciences, University of California San Diego, San Diego, CA, USA
| | | | - Emily Vogtmann
- Metabolic Epidemiology Branch, National Cancer Institute, Rockville, MD, USA
| | - Max von Hippel
- Department of Mathematics, University of Arizona, Tucson, AZ, USA
| | - William Walters
- Department of Microbiome Science, Max Planck Institute for Developmental Biology, Tübingen, Germany
| | - Yunhu Wan
- Metabolic Epidemiology Branch, National Cancer Institute, Rockville, MD, USA
| | - Mingxun Wang
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | - Jonathan Warren
- National Laboratory Service, Environment Agency, Starcross, UK
| | - Kyle C Weber
- Agricultural Research Service, Genomics and Bioinformatics Research Unit, United States Department of Agriculture, Gainesville, FL, USA.,College of Agriculture and Life Sciences, University of Florida, Gainesville, FL, USA
| | | | - Amy D Willis
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Zhenjiang Zech Xu
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Jesse R Zaneveld
- School of STEM, Division of Biological Sciences, University of Washington Bothell, Bothell, WA, USA
| | | | - Qiyun Zhu
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Rob Knight
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.,Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA.,Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
| | - J Gregory Caporaso
- Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA. .,Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA.
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9
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Stone NE, Nunnally AE, Jimenez V, Cope EK, Sahl JW, Sheridan K, Hornstra HM, Vinocur J, Settles EW, Headley KC, Williamson CHD, Rideout JR, Bolyen E, Caporaso JG, Terriquez J, Monroy FP, Busch JD, Keim P, Wagner DM. Domestic canines do not display evidence of gut microbial dysbiosis in the presence of Clostridioides (Clostridium) difficile, despite cellular susceptibility to its toxins. Anaerobe 2019; 58:53-72. [PMID: 30946985 DOI: 10.1016/j.anaerobe.2019.03.017] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Revised: 03/11/2019] [Accepted: 03/25/2019] [Indexed: 12/14/2022]
Abstract
Clostridioides difficile infection (CDI) is an emerging public health threat and C. difficile is the most common cause of antimicrobial-associated diarrhea worldwide and the leading cause of hospital-associated infections in the US, yet the burden of community-acquired infections (CAI) is poorly understood. Characterizing C. difficile isolated from canines is important for understanding the role that canines may play in CAI. In addition, several studies have suggested that canines carry toxigenic C. difficile asymptomatically, which may imply that there are mechanisms responsible for resistance to CDI in canines that could be exploited to help combat human CDI. To assess the virulence potential of canine-derived C. difficile, we tested whether toxins TcdA and TcdB (hereafter toxins) derived from a canine isolate were capable of causing tight junction disruptions to colonic epithelial cells. Additionally, we addressed whether major differences exist between human and canine cells regarding C. difficile pathogenicity by exposing them to identical toxins. We then examined the canine gut microbiome associated with C. difficile carriage using 16S rRNA gene sequencing and searched for deviations from homeostasis as an indicator of CDI. Finally, we queried 16S rRNA gene sequences for bacterial taxa that may be associated with resistance to CDI in canines. Clostridioides difficile isolated from a canine produced toxins that reduced tight junction integrity in both human and canine cells in vitro. However, canine guts were not dysbiotic in the presence of C. difficile. These findings support asymptomatic carriage in canines and, furthermore, suggest that there are features of the gut microbiome and/or a canine-specific immune response that may protect canines against CDI. We identified two biologically relevant bacteria that may aid in CDI resistance in canines: 1) Clostridium hiranonis, which synthesizes secondary bile acids that have been shown to provide resistance to CDI in mice; and 2) Sphingobacterium faecium, which produces sphingophospholipids that may be associated with regulating homeostasis in the canine gut. Our findings suggest that canines may be cryptic reservoirs for C. difficile and, furthermore, that mechanisms of CDI resistance in the canine gut could provide insights into targeted therapeutics for human CDI.
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Affiliation(s)
- Nathan E Stone
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, 86011, USA.
| | - Amalee E Nunnally
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, 86011, USA.
| | - Victor Jimenez
- Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, 86011, USA.
| | - Emily K Cope
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, 86011, USA.
| | - Jason W Sahl
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, 86011, USA; Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, 86011, USA.
| | - Krystal Sheridan
- Translational Genomics Research Institute, Flagstaff, AZ, 86001, USA.
| | - Heidie M Hornstra
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, 86011, USA.
| | - Jacob Vinocur
- Northern Arizona Healthcare, Flagstaff Medical Center, Flagstaff, AZ, 86001, USA.
| | - Erik W Settles
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, 86011, USA; Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, 86011, USA.
| | - Kyle C Headley
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, 86011, USA.
| | - Charles H D Williamson
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, 86011, USA.
| | - Jai Ram Rideout
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, 86011, USA.
| | - Evan Bolyen
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, 86011, USA.
| | - J Gregory Caporaso
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, 86011, USA.
| | - Joel Terriquez
- Northern Arizona Healthcare, Flagstaff Medical Center, Flagstaff, AZ, 86001, USA.
| | - Fernando P Monroy
- Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, 86011, USA.
| | - Joseph D Busch
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, 86011, USA.
| | - Paul Keim
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, 86011, USA; Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, 86011, USA; Translational Genomics Research Institute, Flagstaff, AZ, 86001, USA
| | - David M Wagner
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, 86011, USA; Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, 86011, USA.
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10
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Bokulich NA, Dillon MR, Zhang Y, Rideout JR, Bolyen E, Li H, Albert PS, Caporaso JG. q2-longitudinal: Longitudinal and Paired-Sample Analyses of Microbiome Data. mSystems 2018; 3:e00219-18. [PMID: 30505944 PMCID: PMC6247016 DOI: 10.1128/msystems.00219-18] [Citation(s) in RCA: 159] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Accepted: 11/06/2018] [Indexed: 01/28/2023] Open
Abstract
Studies of host-associated and environmental microbiomes often incorporate longitudinal sampling or paired samples in their experimental design. Longitudinal sampling provides valuable information about temporal trends and subject/population heterogeneity, offering advantages over cross-sectional and pre-post study designs. To support the needs of microbiome researchers performing longitudinal studies, we developed q2-longitudinal, a software plugin for the QIIME 2 microbiome analysis platform (https://qiime2.org). The q2-longitudinal plugin incorporates multiple methods for analysis of longitudinal and paired-sample data, including interactive plotting, linear mixed-effects models, paired differences and distances, microbial interdependence testing, first differencing, longitudinal feature selection, and volatility analyses. The q2-longitudinal package (https://github.com/qiime2/q2-longitudinal) is open-source software released under a 3-clause Berkeley Software Distribution (BSD) license and is freely available, including for commercial use. IMPORTANCE Longitudinal sampling provides valuable information about temporal trends and subject/population heterogeneity. We describe q2-longitudinal, a software plugin for longitudinal analysis of microbiome data sets in QIIME 2. The availability of longitudinal statistics and visualizations in the QIIME 2 framework will make the analysis of longitudinal data more accessible to microbiome researchers.
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Affiliation(s)
- Nicholas A. Bokulich
- The Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, USA
| | - Matthew R. Dillon
- The Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, USA
| | | | - Jai Ram Rideout
- The Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, USA
| | - Evan Bolyen
- The Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, USA
| | - Huilin Li
- Departments of Population Health (Biostatistics) and Environmental Medicine, NYU Langone Medical Center, New York, New York, USA
| | - Paul S. Albert
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA
| | - J. Gregory Caporaso
- The Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, USA
- Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona, USA
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11
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Bolyen E, Rideout JR, Chase J, Pitman TA, Shiffer A, Mercurio W, Dillon MR, Caporaso JG. An Introduction to Applied Bioinformatics: a free, open, and interactive text. ACTA ACUST UNITED AC 2018; 1. [PMID: 30687845 PMCID: PMC6343836 DOI: 10.21105/jose.00027] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Evan Bolyen
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Jai Ram Rideout
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - John Chase
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - T Anders Pitman
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Arron Shiffer
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA.,Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA
| | - Willow Mercurio
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Matthew R Dillon
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - J Gregory Caporaso
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA.,Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA
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12
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Gonzalez A, Navas-Molina JA, Kosciolek T, McDonald D, Vázquez-Baeza Y, Ackermann G, DeReus J, Janssen S, Swafford AD, Orchanian SB, Sanders JG, Shorenstein J, Holste H, Petrus S, Robbins-Pianka A, Brislawn CJ, Wang M, Rideout JR, Bolyen E, Dillon M, Caporaso JG, Dorrestein PC, Knight R. Qiita: rapid, web-enabled microbiome meta-analysis. Nat Methods 2018; 15:796-798. [PMID: 30275573 PMCID: PMC6235622 DOI: 10.1038/s41592-018-0141-9] [Citation(s) in RCA: 338] [Impact Index Per Article: 56.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2018] [Accepted: 08/10/2018] [Indexed: 01/08/2023]
Abstract
Multi-omic insights into microbiome function and composition typically advance one study at a time. However, to understand relationships across studies, they must be aggregated into meta-analyses. This makes it possible to generate new hypotheses by finding features that are reproducible across biospecimens and data layers. Qiita dramatically accelerates such integration tasks in a web-based microbiome comparison platform, which we demonstrate with Human Microbiome Project and iHMP data.
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Affiliation(s)
- Antonio Gonzalez
- Department of Pediatrics, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Jose A Navas-Molina
- Department of Pediatrics, School of Medicine, University of California, San Diego, La Jolla, CA, USA.,Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA.,Google LLC, Mountain View, CA, USA
| | - Tomasz Kosciolek
- Department of Pediatrics, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Daniel McDonald
- Department of Pediatrics, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Yoshiki Vázquez-Baeza
- Department of Pediatrics, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Gail Ackermann
- Department of Pediatrics, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Jeff DeReus
- Department of Pediatrics, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Stefan Janssen
- Department of Pediatrics, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Austin D Swafford
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA
| | - Stephanie B Orchanian
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA
| | - Jon G Sanders
- Department of Pediatrics, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Joshua Shorenstein
- Department of Pediatrics, School of Medicine, University of California, San Diego, La Jolla, CA, USA.,Inscripta, Inc., Boulder, CO, USA
| | - Hannes Holste
- Department of Pediatrics, School of Medicine, University of California, San Diego, La Jolla, CA, USA.,Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA
| | - Semar Petrus
- Department of Biology, University of California, San Diego, La Jolla, CA, USA
| | - Adam Robbins-Pianka
- Department of Computer Science, University of Colorado, Boulder, Boulder, CO, USA
| | - Colin J Brislawn
- Earth & Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Mingxun Wang
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Jai Ram Rideout
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Evan Bolyen
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Matthew Dillon
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - J Gregory Caporaso
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA.,Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA
| | - Pieter C Dorrestein
- Department of Pediatrics, School of Medicine, University of California, San Diego, La Jolla, CA, USA.,Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA.,Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Rob Knight
- Department of Pediatrics, School of Medicine, University of California, San Diego, La Jolla, CA, USA. .,Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA. .,Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA.
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13
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Bokulich NA, Kaehler BD, Rideout JR, Dillon M, Bolyen E, Knight R, Huttley GA, Gregory Caporaso J. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2's q2-feature-classifier plugin. Microbiome 2018; 6:90. [PMID: 29773078 PMCID: PMC5956843 DOI: 10.1186/s40168-018-0470-z] [Citation(s) in RCA: 2260] [Impact Index Per Article: 376.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
BACKGROUND Taxonomic classification of marker-gene sequences is an important step in microbiome analysis. RESULTS We present q2-feature-classifier ( https://github.com/qiime2/q2-feature-classifier ), a QIIME 2 plugin containing several novel machine-learning and alignment-based methods for taxonomy classification. We evaluated and optimized several commonly used classification methods implemented in QIIME 1 (RDP, BLAST, UCLUST, and SortMeRNA) and several new methods implemented in QIIME 2 (a scikit-learn naive Bayes machine-learning classifier, and alignment-based taxonomy consensus methods based on VSEARCH, and BLAST+) for classification of bacterial 16S rRNA and fungal ITS marker-gene amplicon sequence data. The naive-Bayes, BLAST+-based, and VSEARCH-based classifiers implemented in QIIME 2 meet or exceed the species-level accuracy of other commonly used methods designed for classification of marker gene sequences that were evaluated in this work. These evaluations, based on 19 mock communities and error-free sequence simulations, including classification of simulated "novel" marker-gene sequences, are available in our extensible benchmarking framework, tax-credit ( https://github.com/caporaso-lab/tax-credit-data ). CONCLUSIONS Our results illustrate the importance of parameter tuning for optimizing classifier performance, and we make recommendations regarding parameter choices for these classifiers under a range of standard operating conditions. q2-feature-classifier and tax-credit are both free, open-source, BSD-licensed packages available on GitHub.
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Affiliation(s)
- Nicholas A Bokulich
- The Pathogen and Microbiome Institute, Northern Arizona University, PO Box 4073, Flagstaff, AZ, 86011-4073, USA.
| | - Benjamin D Kaehler
- Research School of Biology, Australian National University, 46 Sullivans Creek Road, Acton ACT, 2601, Australia.
| | - Jai Ram Rideout
- The Pathogen and Microbiome Institute, Northern Arizona University, PO Box 4073, Flagstaff, AZ, 86011-4073, USA
| | - Matthew Dillon
- The Pathogen and Microbiome Institute, Northern Arizona University, PO Box 4073, Flagstaff, AZ, 86011-4073, USA
| | - Evan Bolyen
- The Pathogen and Microbiome Institute, Northern Arizona University, PO Box 4073, Flagstaff, AZ, 86011-4073, USA
| | - Rob Knight
- Departments of Pediatrics and Computer Science and Engineering, and Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA
| | - Gavin A Huttley
- Research School of Biology, Australian National University, 46 Sullivans Creek Road, Acton ACT, 2601, Australia.
| | - J Gregory Caporaso
- The Pathogen and Microbiome Institute, Northern Arizona University, PO Box 4073, Flagstaff, AZ, 86011-4073, USA.
- Department of Biological Sciences, Northern Arizona University, 1298 S Knoles Drive, Building 56, 3rd Floor, Flagstaff, AZ, USA.
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14
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Bokulich NA, Dillon MR, Bolyen E, Kaehler BD, Huttley GA, Caporaso JG. q2-sample-classifier: machine-learning tools for microbiome classification and regression. J Open Res Softw 2018; 3:934. [PMID: 31552137 PMCID: PMC6759219 DOI: 10.21105/joss.00934] [Citation(s) in RCA: 82] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
q2-sample-classifier is a plugin for the QIIME 2 microbiome bioinformatics platform that facilitates access, reproducibility, and interpretation of supervised learning (SL) methods for a broad audience of non-bioinformatics specialists.
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Affiliation(s)
- Nicholas A Bokulich
- The Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Matthew R Dillon
- The Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Evan Bolyen
- The Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
| | - Benjamin D Kaehler
- Research School of Biology, Australian National University, Canberra, Australia
| | - Gavin A Huttley
- Research School of Biology, Australian National University, Canberra, Australia
| | - J Gregory Caporaso
- The Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
- Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA
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15
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Rideout JR, Chase JH, Bolyen E, Ackermann G, González A, Knight R, Caporaso JG. Keemei: cloud-based validation of tabular bioinformatics file formats in Google Sheets. Gigascience 2016; 5:27. [PMID: 27296526 PMCID: PMC4906574 DOI: 10.1186/s13742-016-0133-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2016] [Accepted: 06/01/2016] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Bioinformatics software often requires human-generated tabular text files as input and has specific requirements for how those data are formatted. Users frequently manage these data in spreadsheet programs, which is convenient for researchers who are compiling the requisite information because the spreadsheet programs can easily be used on different platforms including laptops and tablets, and because they provide a familiar interface. It is increasingly common for many different researchers to be involved in compiling these data, including study coordinators, clinicians, lab technicians and bioinformaticians. As a result, many research groups are shifting toward using cloud-based spreadsheet programs, such as Google Sheets, which support the concurrent editing of a single spreadsheet by different users working on different platforms. Most of the researchers who enter data are not familiar with the formatting requirements of the bioinformatics programs that will be used, so validating and correcting file formats is often a bottleneck prior to beginning bioinformatics analysis. MAIN TEXT We present Keemei, a Google Sheets Add-on, for validating tabular files used in bioinformatics analyses. Keemei is available free of charge from Google's Chrome Web Store. Keemei can be installed and run on any web browser supported by Google Sheets. Keemei currently supports the validation of two widely used tabular bioinformatics formats, the Quantitative Insights into Microbial Ecology (QIIME) sample metadata mapping file format and the Spatially Referenced Genetic Data (SRGD) format, but is designed to easily support the addition of others. CONCLUSIONS Keemei will save researchers time and frustration by providing a convenient interface for tabular bioinformatics file format validation. By allowing everyone involved with data entry for a project to easily validate their data, it will reduce the validation and formatting bottlenecks that are commonly encountered when human-generated data files are first used with a bioinformatics system. Simplifying the validation of essential tabular data files, such as sample metadata, will reduce common errors and thereby improve the quality and reliability of research outcomes.
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Affiliation(s)
- Jai Ram Rideout
- Center for Microbial Genetics and Genomics, Northern Arizona University, Flagstaff, AZ, 86011, USA
| | - John H Chase
- Center for Microbial Genetics and Genomics, Northern Arizona University, Flagstaff, AZ, 86011, USA
| | - Evan Bolyen
- Center for Microbial Genetics and Genomics, Northern Arizona University, Flagstaff, AZ, 86011, USA
| | - Gail Ackermann
- Department of Pediatrics, University of California San Diego, San Diego, CA, 92093, USA
| | - Antonio González
- Department of Pediatrics, University of California San Diego, San Diego, CA, 92093, USA
| | - Rob Knight
- Department of Pediatrics, University of California San Diego, San Diego, CA, 92093, USA.,Department of Computer Science and Engineering, University of California San Diego, San Diego, CA, 92093, USA
| | - J Gregory Caporaso
- Center for Microbial Genetics and Genomics, Northern Arizona University, Flagstaff, AZ, 86011, USA. .,Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, 86011, USA.
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Fouquier J, Rideout JR, Bolyen E, Chase J, Shiffer A, McDonald D, Knight R, Caporaso JG, Kelley ST. Ghost-tree: creating hybrid-gene phylogenetic trees for diversity analyses. Microbiome 2016; 4:11. [PMID: 26905735 PMCID: PMC4765138 DOI: 10.1186/s40168-016-0153-6] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2015] [Accepted: 02/05/2016] [Indexed: 05/11/2023]
Abstract
BACKGROUND Fungi play critical roles in many ecosystems, cause serious diseases in plants and animals, and pose significant threats to human health and structural integrity problems in built environments. While most fungal diversity remains unknown, the development of PCR primers for the internal transcribed spacer (ITS) combined with next-generation sequencing has substantially improved our ability to profile fungal microbial diversity. Although the high sequence variability in the ITS region facilitates more accurate species identification, it also makes multiple sequence alignment and phylogenetic analysis unreliable across evolutionarily distant fungi because the sequences are hard to align accurately. To address this issue, we created ghost-tree, a bioinformatics tool that integrates sequence data from two genetic markers into a single phylogenetic tree that can be used for diversity analyses. Our approach starts with a "foundation" phylogeny based on one genetic marker whose sequences can be aligned across organisms spanning divergent taxonomic groups (e.g., fungal families). Then, "extension" phylogenies are built for more closely related organisms (e.g., fungal species or strains) using a second more rapidly evolving genetic marker. These smaller phylogenies are then grafted onto the foundation tree by mapping taxonomic names such that each corresponding foundation-tree tip would branch into its new "extension tree" child. RESULTS We applied ghost-tree to graft fungal extension phylogenies derived from ITS sequences onto a foundation phylogeny derived from fungal 18S sequences. Our analysis of simulated and real fungal ITS data sets found that phylogenetic distances between fungal communities computed using ghost-tree phylogenies explained significantly more variance than non-phylogenetic distances. The phylogenetic metrics also improved our ability to distinguish small differences (effect sizes) between microbial communities, though results were similar to non-phylogenetic methods for larger effect sizes. CONCLUSIONS The Silva/UNITE-based ghost tree presented here can be easily integrated into existing fungal analysis pipelines to enhance the resolution of fungal community differences and improve understanding of these communities in built environments. The ghost-tree software package can also be used to develop phylogenetic trees for other marker gene sets that afford different taxonomic resolution, or for bridging genome trees with amplicon trees. AVAILABILITY ghost-tree is pip-installable. All source code, documentation, and test code are available under the BSD license at https://github.com/JTFouquier/ghost-tree .
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Affiliation(s)
- Jennifer Fouquier
- Graduate Program in Bioinformatics and Medical Informatics, San Diego State University, San Diego, CA, USA.
| | - Jai Ram Rideout
- Center for Microbial Genetics and Genomics, Northern Arizona University, Flagstaff, AZ, USA.
| | - Evan Bolyen
- Center for Microbial Genetics and Genomics, Northern Arizona University, Flagstaff, AZ, USA.
| | - John Chase
- Center for Microbial Genetics and Genomics, Northern Arizona University, Flagstaff, AZ, USA.
| | - Arron Shiffer
- Center for Microbial Genetics and Genomics, Northern Arizona University, Flagstaff, AZ, USA.
- Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA.
| | | | - Rob Knight
- Department of Pediatrics, and Department of Computer Science and Engineering, University of California San Diego, San Diego, CA, USA.
| | - J Gregory Caporaso
- Center for Microbial Genetics and Genomics, Northern Arizona University, Flagstaff, AZ, USA.
- Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA.
| | - Scott T Kelley
- Graduate Program in Bioinformatics and Medical Informatics, San Diego State University, San Diego, CA, USA.
- Department of Biology, San Diego State University, San Diego, CA, USA.
- San Diego State University, 5500 Campanile Drive, San Diego, CA, 92182-4614, USA.
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Bokulich NA, Rideout JR, Kopylova E, Bolyen E, Patnode J, Ellett Z, Mcdonald D, Wolfe B, Maurice CF, Dutton RJ, Turnbaugh PJ, Knight R, Caporaso JG. A standardized, extensible framework for optimizing classification improves marker-gene taxonomic assignments.. [PMID: 0 DOI: 10.7287/peerj.preprints.934v2] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Background: Taxonomic classification of marker-gene (i.e., amplicon) sequences represents an important step for molecular identification of microorganisms.
Results: We present three advances in our ability to assign and interpret taxonomic classifications of short marker gene sequences: two new methods for taxonomy assignment, which reduce runtime up to two-fold and achieve high-precision genus-level assignments; an evaluation of classification methods that highlights differences in performance with different marker genes and at different levels of taxonomic resolution; and an extensible framework for evaluating and optimizing new classification methods, which we hope will serve as a model for standardized and reproducible bioinformatics methods evaluations.
Conclusions: Our new methods are accessible in QIIME 1.9.0, and our evaluation framework will support ongoing optimization of classification methods to complement rapidly evolving short-amplicon sequencing and bioinformatics technologies. Static versions of all of the analysis notebooks generated with this framework, which contain all code and analysis results, can be viewed at http://bit.ly/srta-012 .
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Affiliation(s)
- Nicholas A Bokulich
- Department of Medicine, New York University Langone Medical Center, New York, NY, USA
| | - Jai Ram Rideout
- Center for Microbial Genetics and Genomics, Northern Arizona University, Flagstaff, AZ, 86011
| | - Evguenia Kopylova
- Department of Pediatrics, University of California, San Diego, San Diego, CA, USA
| | - Evan Bolyen
- Center for Microbial Genetics and Genomics, Northern Arizona University, Flagstaff, AZ, 86011
| | - Jessica Patnode
- Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, United States
| | - Zach Ellett
- Department of Computer Science, Northern Arizona University, Flagstaff, AZ, USA
| | - Daniel McDonald
- Department of Computer Science, University of Colorado at Boulder, Boulder, CO, USA
- BioFrontiers Institute, University of Colorado at Boulder, Boulder, CO, United States
| | - Benjamin Wolfe
- FAS Center for Systems Biology, Harvard University, Cambridge, MA, USA
| | - Corinne F Maurice
- Department of Microbiology and Immunology, Microbiome and Disease Tolerance Centre, McGill University, Montreal, QC, Canada
| | - Rachel J Dutton
- FAS Center for Systems Biology, Harvard University, Cambridge, MA, USA
| | - Peter J Turnbaugh
- Department of Microbiology and Immunology, University of California San Francisco, San Francisco, CA, USA
| | - Rob Knight
- Department of Pediatrics, University of California, San Diego, San Diego, CA, USA
| | - J Gregory Caporaso
- Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, United States
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