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      Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms

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          Abstract

          DNA sequencing continues to decrease in cost with the Illumina HiSeq2000 generating up to 600 Gb of paired-end 100 base reads in a ten-day run. Here we present a protocol for community amplicon sequencing on the HiSeq2000 and MiSeq Illumina platforms, and apply that protocol to sequence 24 microbial communities from host-associated and free-living environments. A critical question as more sequencing platforms become available is whether biological conclusions derived on one platform are consistent with what would be derived on a different platform. We show that the protocol developed for these instruments successfully recaptures known biological results, and additionally that biological conclusions are consistent across sequencing platforms (the HiSeq2000 versus the MiSeq) and across the sequenced regions of amplicons.

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          Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample.

          J. Gregory Caporaso, Christian L. Lauber, William A. Walters … (2011)
          The ongoing revolution in high-throughput sequencing continues to democratize the ability of small groups of investigators to map the microbial component of the biosphere. In particular, the coevolution of new sequencing platforms and new software tools allows data acquisition and analysis on an unprecedented scale. Here we report the next stage in this coevolutionary arms race, using the Illumina GAIIx platform to sequence a diverse array of 25 environmental samples and three known "mock communities" at a depth averaging 3.1 million reads per sample. We demonstrate excellent consistency in taxonomic recovery and recapture diversity patterns that were previously reported on the basis of metaanalysis of many studies from the literature (notably, the saline/nonsaline split in environmental samples and the split between host-associated and free-living communities). We also demonstrate that 2,000 Illumina single-end reads are sufficient to recapture the same relationships among samples that we observe with the full dataset. The results thus open up the possibility of conducting large-scale studies analyzing thousands of samples simultaneously to survey microbial communities at an unprecedented spatial and temporal resolution.
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            A renaissance for the pioneering 16S rRNA gene.

            Susannah Tringe, Philip Hugenholtz (2008)
            Culture-independent molecular surveys using the 16S rRNA gene have become a mainstay for characterizing microbial community structure over the past quarter century. More recently this approach has been overshadowed by metagenomics, which provides a global overview of a community's functional potential rather than just an inventory of its inhabitants. However, the pioneering 16S rRNA gene is making a comeback in its own right thanks to a number of methodological advancements including higher resolution (more sequences), analysis of multiple related samples (e.g. spatial and temporal series) and improved metadata, and use of metadata. The standard conclusion that microbial ecosystems are remarkably complex and diverse is now being replaced by detailed insights into microbial ecology and evolution based only on this one historically important marker gene.
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              Microbial community resemblance methods differ in their ability to detect biologically relevant patterns

              Justin Kuczynski, Zongzhi Liu, Catherine A. Lozupone … (2010)
              The development of high-throughput sequencing methods allows for the characterization of microbial communities in a wide range of environments on an unprecedented scale. However, insight into microbial community composition is limited by our ability to detect patterns in this flood of sequences. Here we compare the performance of 51 analysis techniques using real and simulated bacterial 16S rRNA pyrosequencing datasets containing either clustered samples or samples arrayed across environmental gradients. We find that many diversity patterns are evident with severely undersampled communities, and that methods vary widely in their ability to detect gradients and clusters. Chi-squared distances and Pearson correlation distances perform especially well for detecting gradients, while Gower and Canberra distances perform especially well for detecting clusters. These results also provide a basis for understanding tradeoffs between number of samples and depth of coverage, tradeoffs which are important to consider when designing studies to characterize microbial communities.
              Article 0 comments Cited 110 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): ISME J
                Journal ID (iso-abbrev): ISME J
                Title: The ISME Journal
                Publisher: Nature Publishing Group
                ISSN (Print): 1751-7362
                ISSN (Electronic): 1751-7370
                Publication date (Print): August 2012
                Publication date (Electronic): 08 March 2012
                Publication date PMC-release: 1 August 2012
                Volume: 6
                Issue: 8
                Pages: 1621-1624
                Affiliations
                [1 ]simpleDepartment of Computer Science, Northern Arizona University , Flagstaff, AZ, USA
                [2 ]simpleCooperative Institute for Research in Environmental Sciences, UCB 216, University of Colorado , Boulder, CO, USA
                [3 ]simpleDepartment of Molecular, Cellular and Developmental Biology, UCB 347, University of Colorado , Boulder, CO, USA
                [4 ]simpleColorado Initiative in Molecular Biotechnology, UCB 347, University of Colorado , Boulder, CO, USA
                [5 ]simpleDepartment of Ecology and Evolutionary Biology, UCB 334, University of Colorado , Boulder, Colorado, USA
                [6 ]simpleArgonne National Laboratory , Argonne, IL, USA
                [7 ]simpleIllumina Cambridge Ltd., Chesterford Research Park, Saffron Walden , Essex, UK
                [8 ]simpleDepartment of Ecology and Evolution, University of Chicago , Chicago, IL, USA
                [9 ]simpleDepartment of Chemistry and Biochemistry, UCB 215, University of Colorado , Boulder, CO, USA
                [10 ]simpleHoward Hughes Medical Institute , simpleUniversity of Colorado at Boulder , simpleUCB 215 , Boulder, CO, USA
                Author notes
                [* ]simpleHoward Hughes Medical Institute, University of Colorado at Boulder , UCB 215, Boulder, CO 80309, USA. E-mail: rob@ 123456spot.colorado.edu
                Article
                Publisher Item ID: ismej20128
                DOI: 10.1038/ismej.2012.8
                PMC ID: 3400413
                PubMed ID: 22402401
                SO-VID: d86ccbcf-f13c-49b1-9807-66c43dcef4a7
                Copyright © Copyright © 2012 International Society for Microbial Ecology
                License:

                This work is licensed under the Creative Commons Attribution-NonCommercial-Share Alike 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/

                History
                Date received : 12 September 2011
                Date revision received : 13 January 2012
                Date accepted : 19 January 2012
                Categories
                Subject: Short Communication

                ScienceOpen disciplines: Microbiology & Virology
                Keywords: barcoded sequencing,illumine,qiime
                Data availability:
                ScienceOpen disciplines: Microbiology & Virology
                Keywords: barcoded sequencing, illumine, qiime

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