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      Massively parallel sequencing of single cells by epicPCR links functional genes with phylogenetic markers

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          Abstract

          Many microbial communities are characterized by high genetic diversity. 16S ribosomal RNA sequencing can determine community members, and metagenomics can determine the functional diversity, but resolving the functional role of individual cells in high throughput remains an unsolved challenge. Here, we describe epicPCR (Emulsion, Paired Isolation and Concatenation PCR), a new technique that links functional genes and phylogenetic markers in uncultured single cells, providing a throughput of hundreds of thousands of cells with costs comparable to one genomic library preparation. We demonstrate the utility of our technique in a natural environment by profiling a sulfate-reducing community in a freshwater lake, revealing both known sulfate reducers and discovering new putative sulfate reducers. Our method is adaptable to any conserved genetic trait and translates genetic associations from diverse microbial samples into a sequencing library that answers targeted ecological questions. Potential applications include identifying functional community members, tracing horizontal gene transfer networks and mapping ecological interactions between microbial cells.

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          Most cited references 35

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          Basic local alignment search tool.

          A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.
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            QIIME allows analysis of high-throughput community sequencing data.

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              Search and clustering orders of magnitude faster than BLAST.

               Robert Edgar (2010)
              Biological sequence data is accumulating rapidly, motivating the development of improved high-throughput methods for sequence classification. UBLAST and USEARCH are new algorithms enabling sensitive local and global search of large sequence databases at exceptionally high speeds. They are often orders of magnitude faster than BLAST in practical applications, though sensitivity to distant protein relationships is lower. UCLUST is a new clustering method that exploits USEARCH to assign sequences to clusters. UCLUST offers several advantages over the widely used program CD-HIT, including higher speed, lower memory use, improved sensitivity, clustering at lower identities and classification of much larger datasets. Binaries are available at no charge for non-commercial use at http://www.drive5.com/usearch.
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                Author and article information

                Journal
                ISME J
                ISME J
                The ISME Journal
                Nature Publishing Group
                1751-7362
                1751-7370
                February 2016
                22 September 2015
                1 February 2016
                : 10
                : 2
                : 427-436
                Affiliations
                [1 ]Computational and Systems Biology, Massachusetts Institute of Technology , Cambridge, MA, USA
                [2 ]Department of Biological Engineering, Massachusetts Institute of Technology , Cambridge, MA, USA
                [3 ]Department of Food and Environmental Sciences, University of Helsinki , Helsinki, Finland
                [4 ]School of Engineering and Applied Sciences, Harvard University , Cambridge, MA, USA
                [5 ]Department of Physics, Harvard University , Cambridge, MA, USA
                [6 ]AbVitro Inc. , Boston, MA, USA
                [7 ]Department of Civil and Environmental Engineering, Massachusetts Institute of Technology , Cambridge, MA, USA
                [8 ]The Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology , Cambridge, MA, USA
                [9 ]The Broad Institute of MIT and Harvard , Cambridge, MA, USA
                Author notes
                [* ]Department of Biological Engineering, Massachusetts Institute of Technology , 77 Massachusetts Avenue, Cambridge, MA 02139, USA E-mail: ejalm@ 123456mit.edu
                [* ]Department of Food and Environmental Sciences, University of Helsinki , PO Box 56, Helsinki 00014, Finland. E-mail: mvtammin@ 123456mit.edu
                [10]

                These authors contributed equally to this work.

                Article
                ismej2015124
                10.1038/ismej.2015.124
                4737934
                26394010
                Copyright © 2016 International Society for Microbial Ecology

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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                Original Article

                Microbiology & Virology

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