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

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

doi:10.7287/peerj.preprints.27295v1

Cited by 245 publications

(129 citation statements)

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“…All loci were amplified using the following thermal cycle profile: 94°C for 2 min (one cycle); 94°C for 15 s, primer-specific annealing temperature (Table S1) and 72°C for 30 s (31 cycles); and 72°C for 30 min on a Mastercycler gradient thermal cycler (Eppendorf). Paired-end sample data with quality scores were imported and then reads were joined using vsearch join-pairs and quality filtered using quality-filter q-score-joined within qiime2 (Bolyen et al, 2018;Caporaso et al, 2010). Chimeras were removed and sequences were denoised using deblur with a p-trim-length parameter of 230, which truncates the read at this position.…”

Section: Symbiodinium Microsatellite Analysismentioning

confidence: 99%

What drives phenotypic divergence among coral clonemates of Acropora palmata?

et al. 2019

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Evolutionary rescue of populations depends on their ability to produce phenotypic variation that is heritable and adaptive. DNA mutations are the best understood mechanisms to create phenotypic variation, but other, less well‐studied mechanisms exist. Marine benthic foundation species provide opportunities to study these mechanisms because many are dominated by isogenic stands produced through asexual reproduction. For example, Caribbean acroporid corals are long lived and reproduce asexually via breakage of branches. Fragmentation is often the dominant mode of local population maintenance. Thus, large genets with many ramets (colonies) are common. Here, we observed phenotypic variation in stress responses within genets following the coral bleaching events in 2014 and 2015 caused by high water temperatures. This was not due to genetic variation in their symbiotic dinoflagellates (Symbiodinium “fitti”) because each genet of this coral species typically harbours a single strain of S. “fitti”. Characterization of the microbiome via 16S tag sequencing correlated the abundance of only two microbiome members (Tepidiphilus, Endozoicomonas) with a bleaching response. Epigenetic changes were significantly correlated with the host's genetic background, the location of the sampled polyps within the colonies (e.g., branch vs. base of colony), and differences in the colonies’ condition during the bleaching event. We conclude that long‐term microenvironmental differences led to changes in the way the ramets methylated their genomes, contributing to the differential bleaching response. However, most of the variation in differential bleaching response among clonemates of Acropora palmata remains unexplained. This research provides novel data and hypotheses to help understand intragenet variability in stress phenotypes of sessile marine species.

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Section: Symbiodinium Microsatellite Analysismentioning

confidence: 99%

What drives phenotypic divergence among coral clonemates of Acropora palmata?

et al. 2019

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“…16S rRNA genes in extracted DNA were sequenced at Argonne National Laboratory using bacterial/archaeal primer set 515F/806R that targets the V4 region. Resulting reads were checked for chimeras (USEARCH 61 algorithm) and subsequently clustered into exact sequence variant (ESV) classifications at 100% similarities using the DADA2 algorithm in the QIIME2 pipeline (Qiime2-2018.4; (Bolyen et al, 2018) and SILVA 16S rRNA gene database. Subsequent analyses were performed using the R vegan, GUniFrac, and picante packages to determine linkages between geochemical variables and temporal microbial changes.…”

Section: S Rrna Gene Analysesmentioning

confidence: 99%

Hyporheic Zone Microbiome Assembly Is Linked to Dynamic Water Mixing Patterns in Snowmelt‐Dominated Headwater Catchments

Saup

Bryant

et al. 2019

JGR Biogeosciences

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Terrestrial and aquatic elemental cycles are tightly linked in upland fluvial networks. Biotic and abiotic mineral weathering, microbially mediated degradation of organic matter, and anthropogenic influences all result in the movement of solutes (e.g., carbon, metals, and nutrients) through these catchments, with implications for downstream water quality. Within the river channel, the region of hyporheic mixing represents a hot spot of microbial activity, exerting significant control over solute cycling. To investigate how snowmelt-driven seasonal changes in river discharge affect microbial community assembly and carbon biogeochemistry, depth-resolved pore water samples were recovered from multiple locations around a representative meander on the East River near Crested Butte, CO, USA. Vertical temperature sensor arrays were also installed in the streambed to enable seepage flux estimates. Snowmelt-driven high river discharge led to an expanding zone of vertical hyporheic mixing and introduced dissolved oxygen into the streambed that stimulated aerobic microbial respiration. These physicochemical processes contributed to microbial communities undergoing homogenizing selection, in contrast to other ecosystems where lower permeability may limit the extent of mixing. Conversely, lower river discharge conditions led to a greater influence of upwelling groundwater within the streambed and a decrease in microbial respiration rates. Associated with these processes, microbial communities throughout the streambed exhibited increasing dissimilarity between each other, suggesting that the earlier onset of snowmelt and longer periods of base flow may lead to changes in the composition (and associated function) of streambed microbiomes, with consequent implications for the processing and export of solutes from upland catchments. Plain Language SummarySeasonal changes in river discharge in high-altitude watersheds affect patterns of surface and groundwater mixing in the hyporheic zone (the region in the riverbed where these two water sources interact) that impacts how carbon compounds and dissolved metals are transported.One key constraint with respect to hyporheic mixing concerns the rate of water flow, a process driving the change in oxygen concentration in the riverbed. Oxygen concentration controls rates of carbon degradation and metal inputs from surrounding rocks, which, in turn, affect downstream water quality. In this study, we examined a stream in an alpine watershed, the East River, in the Upper Colorado River Basin, sampling a representative meander at discrete depths throughout the year for microbiological and geochemical analyses. We also installed data loggers to continuously collect temperature information to understand the extent of hyporheic mixing. We found that the movement of dissolved materials was strongly correlated with seasonal changes in flow. Under maximum flow, increased oxygen concentration stimulates microbial degradation of carbon compounds, and conversely, during minimum flow, decreased oxygen c...

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“…The pooled library was sequenced on the Illumina MiSeq sequencing platform with a MiSeq Reagent Kit v2 and paired-end 150 cycles. All data were processed and analyzed using the QIIME2 software suite [8] and Deblur [9]. Counterintuitively, single PCR reactions yielded significantly more reads than triplicate PCR reactions (mean ± SEM: 10,821 ± 298 versus 10,029 ± 262, respectively, paired T-test p = 0.0003), and fewer dropouts (Figure 1A).…”

mentioning

confidence: 99%

Triplicate PCR Reactions for 16S rRNA Gene Amplicon Sequencing are Unnecessary

et al. 2019

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Conventional wisdom holds that PCR amplification for sequencing should employ pooled replicate reactions to reduce bias due to jackpot effects and chimera formation. However, modern amplicon data analysis employs methods that may be less sensitive to such artifacts. Here we directly compare results from single versus triplicate reactions for 16S amplicon sequencing and find no significant impact of adopting a less labor-intensive single-reaction protocol.

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

Cited by 245 publications

References 0 publications

What drives phenotypic divergence among coral clonemates of Acropora palmata?

What drives phenotypic divergence among coral clonemates of Acropora palmata?

Hyporheic Zone Microbiome Assembly Is Linked to Dynamic Water Mixing Patterns in Snowmelt‐Dominated Headwater Catchments

Triplicate PCR Reactions for 16S rRNA Gene Amplicon Sequencing are Unnecessary

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