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      Next-generation sequencing: insights to advance clinical investigations of the microbiome

      review-article
      1 , 2 , 3 , 4 , 5 , 1 , 6
      The Journal of Clinical Investigation
      American Society for Clinical Investigation

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          Abstract

          Next-generation sequencing (NGS) technology has advanced our understanding of the human microbiome by allowing for the discovery and characterization of unculturable microbes with prediction of their function. Key NGS methods include 16S rRNA gene sequencing, shotgun metagenomic sequencing, and RNA sequencing. The choice of which NGS methodology to pursue for a given purpose is often unclear for clinicians and researchers. In this Review, we describe the fundamentals of NGS, with a focus on 16S rRNA and shotgun metagenomic sequencing. We also discuss pros and cons of each methodology as well as important concepts in data variability, study design, and clinical metadata collection. We further present examples of how NGS studies of the human microbiome have advanced our understanding of human disease pathophysiology across diverse clinical contexts, including the development of diagnostics and therapeutics. Finally, we share insights as to how NGS might further be integrated into and advance microbiome research and clinical care in the coming years.

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          Most cited references138

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          A Novel Coronavirus from Patients with Pneumonia in China, 2019

          Summary In December 2019, a cluster of patients with pneumonia of unknown cause was linked to a seafood wholesale market in Wuhan, China. A previously unknown betacoronavirus was discovered through the use of unbiased sequencing in samples from patients with pneumonia. Human airway epithelial cells were used to isolate a novel coronavirus, named 2019-nCoV, which formed a clade within the subgenus sarbecovirus, Orthocoronavirinae subfamily. Different from both MERS-CoV and SARS-CoV, 2019-nCoV is the seventh member of the family of coronaviruses that infect humans. Enhanced surveillance and further investigation are ongoing. (Funded by the National Key Research and Development Program of China and the National Major Project for Control and Prevention of Infectious Disease in China.)
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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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              The SILVA ribosomal RNA gene database project: improved data processing and web-based tools

              SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.
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                Author and article information

                Contributors
                Journal
                J Clin Invest
                J Clin Invest
                J Clin Invest
                The Journal of Clinical Investigation
                American Society for Clinical Investigation
                0021-9738
                1558-8238
                1 April 2022
                1 April 2022
                1 April 2022
                1 April 2022
                : 132
                : 7
                : e154944
                Affiliations
                [1 ]Department of Medicine and
                [2 ]Department of Physiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
                [3 ]Department of Biomedical Engineering,
                [4 ]Department of Computer Science, and
                [5 ]Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland, USA.
                [6 ]Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
                Author notes
                Address correspondence to: Cynthia L. Sears, Johns Hopkins University School of Medicine, 1550 Orleans Street, CRB2 Building, Suite 1M.05, Baltimore, Maryland 21231, USA. Phone: 410.614.8378; Email: csears@ 123456jhmi.edu
                Author information
                http://orcid.org/0000-0001-6233-2922
                http://orcid.org/0000-0003-3621-2665
                http://orcid.org/0000-0002-8859-7432
                Article
                154944
                10.1172/JCI154944
                8970668
                35362479
                53eeacc3-5787-4648-90bf-3630062aa3db
                © 2022 Wensel et al.

                This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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