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      SPAdes: A New Genome Assembly Algorithm and Its Applications to Single-Cell Sequencing

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          Abstract

          The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.

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          Most cited references 27

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          Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq.

          Our understanding of the development and maintenance of tissues has been greatly aided by large-scale gene expression analysis. However, tissues are invariably complex, and expression analysis of a tissue confounds the true expression patterns of its constituent cell types. Here we describe a novel strategy to access such complex samples. Single-cell RNA-seq expression profiles were generated, and clustered to form a two-dimensional cell map onto which expression data were projected. The resulting cell map integrates three levels of organization: the whole population of cells, the functionally distinct subpopulations it contains, and the single cells themselves-all without need for known markers to classify cell types. The feasibility of the strategy was demonstrated by analyzing the transcriptomes of 85 single cells of two distinct types. We believe this strategy will enable the unbiased discovery and analysis of naturally occurring cell types during development, adult physiology, and disease.
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            Rapid amplification of plasmid and phage DNA using Phi 29 DNA polymerase and multiply-primed rolling circle amplification.

            We describe a simple method of using rolling circle amplification to amplify vector DNA such as M13 or plasmid DNA from single colonies or plaques. Using random primers and phi29 DNA polymerase, circular DNA templates can be amplified 10,000-fold in a few hours. This procedure removes the need for lengthy growth periods and traditional DNA isolation methods. Reaction products can be used directly for DNA sequencing after phosphatase treatment to inactivate unincorporated nucleotides. Amplified products can also be used for in vitro cloning, library construction, and other molecular biology applications.
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              Quake: quality-aware detection and correction of sequencing errors

              We introduce Quake, a program to detect and correct errors in DNA sequencing reads. Using a maximum likelihood approach incorporating quality values and nucleotide specific miscall rates, Quake achieves the highest accuracy on realistically simulated reads. We further demonstrate substantial improvements in de novo assembly and SNP detection after using Quake. Quake can be used for any size project, including more than one billion human reads, and is freely available as open source software from http://www.cbcb.umd.edu/software/quake.
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                Author and article information

                Journal
                Journal of Computational Biology
                Journal of Computational Biology
                Mary Ann Liebert Inc
                1066-5277
                1557-8666
                May 2012
                May 2012
                : 19
                : 5
                : 455-477
                Affiliations
                [1 ]Algorithmic Biology Laboratory, St. Petersburg Academic University, Russian Academy of Sciences, St. Petersburg, Russia.
                [2 ]These authors contributed equally to this work.
                [3 ]Steklov Institute of Mathematics, St. Petersburg, Russia.
                [4 ]Department of Computer Science and Engineering, University of California, San Diego, La Jolla, California.
                [5 ]Department of Mathematics, University of California, San Diego, La Jolla, California.
                [6 ]Department of Computer Science and Engineering, University of South Carolina, Columbia, South Carolina.
                Article
                10.1089/cmb.2012.0021
                3342519
                22506599
                © 2012
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