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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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Thompson LR, Williams GJ, Haroon MF, Shibl A, Larsen P, Shorenstein J, Knight R, Stingl U. Metagenomic covariation along densely sampled environmental gradients in the Red Sea. ISME J 2017; 11:138-151. [PMID: 27420030 PMCID: PMC5315489 DOI: 10.1038/ismej.2016.99] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2016] [Revised: 06/08/2016] [Accepted: 06/12/2016] [Indexed: 12/13/2022]
Abstract
Oceanic microbial diversity covaries with physicochemical parameters. Temperature, for example, explains approximately half of global variation in surface taxonomic abundance. It is unknown, however, whether covariation patterns hold over narrower parameter gradients and spatial scales, and extending to mesopelagic depths. We collected and sequenced 45 epipelagic and mesopelagic microbial metagenomes on a meridional transect through the eastern Red Sea. We asked which environmental parameters explain the most variation in relative abundances of taxonomic groups, gene ortholog groups, and pathways-at a spatial scale of <2000 km, along narrow but well-defined latitudinal and depth-dependent gradients. We also asked how microbes are adapted to gradients and extremes in irradiance, temperature, salinity, and nutrients, examining the responses of individual gene ortholog groups to these parameters. Functional and taxonomic metrics were equally well explained (75-79%) by environmental parameters. However, only functional and not taxonomic covariation patterns were conserved when comparing with an intruding water mass with different physicochemical properties. Temperature explained the most variation in each metric, followed by nitrate, chlorophyll, phosphate, and salinity. That nitrate explained more variation than phosphate suggested nitrogen limitation, consistent with low surface N:P ratios. Covariation of gene ortholog groups with environmental parameters revealed patterns of functional adaptation to the challenging Red Sea environment: high irradiance, temperature, salinity, and low nutrients. Nutrient-acquisition gene ortholog groups were anti-correlated with concentrations of their respective nutrient species, recapturing trends previously observed across much larger distances and environmental gradients. This dataset of metagenomic covariation along densely sampled environmental gradients includes online data exploration supplements, serving as a community resource for marine microbial ecology.
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Affiliation(s)
- Luke R Thompson
- Red Sea Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- Department of Pediatrics, University of California, San Diego, CA, USA
| | - Gareth J Williams
- Center for Marine Biodiversity and Conservation, Scripps Institution of Oceanography, La Jolla, CA, USA
- School of Ocean Sciences, Bangor University, Anglesey, UK
| | - Mohamed F Haroon
- Red Sea Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Ahmed Shibl
- Red Sea Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | | | | | - Rob Knight
- Department of Pediatrics, University of California, San Diego, CA, USA
- Department of Computer Science, University of California, San Diego, CA, USA
| | - Ulrich Stingl
- Red Sea Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
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Dallas A, Ilves H, Ge Q, Kumar P, Shorenstein J, Kazakov SA, Cuellar TL, McManus MT, Behlke MA, Johnston BH. Right- and left-loop short shRNAs have distinct and unusual mechanisms of gene silencing. Nucleic Acids Res 2012; 40:9255-71. [PMID: 22810205 PMCID: PMC3467060 DOI: 10.1093/nar/gks662] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Small hairpin RNAs (shRNAs) having duplex lengths of 25–29 bp are normally processed by Dicer into short interfering RNAs (siRNAs) before incorporation into the RNA-induced silencing complex (RISC). However, shRNAs of ≤19 bp [short shRNAs (sshRNAs)] are too short for Dicer to excise their loops, raising questions about their mechanism of action. sshRNAs are designated as L-type or R-type according to whether the loop is positioned 3′ or 5′ to the guide sequence, respectively. Using nucleotide modifications that inhibit RNA cleavage, we show that R- but not L-sshRNAs require loop cleavage for optimum activity. Passenger-arm slicing was found to be important for optimal functioning of L-sshRNAs but much less important for R-sshRNAs that have a cleavable loop. R-sshRNAs could be immunoprecipitated by antibodies to Argonaute-1 (Ago1); complexes with Ago1 contained both intact and loop-cleaved sshRNAs. In contrast, L-sshRNAs were immunoprecipitated with either Ago1 or Ago2 and were predominantly sliced in the passenger arm of the hairpin. However, ‘pre-sliced’ L-sshRNAs were inactive. We conclude that active L-sshRNAs depend on slicing of the passenger arm to facilitate opening of the duplex, whereas R-sshRNAs primarily act via loop cleavage to generate a 5′-phosphate at the 5′-end of the guide strand.
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Affiliation(s)
- Anne Dallas
- SomaGenics, Inc., 2161 Delaware Avenue, Santa Cruz, CA 95060, USA
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