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      High‐Throughput Metagenomics for Identification of Pathogens in the Clinical Settings

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          Abstract

          The application of sequencing technology is shifting from research to clinical laboratories owing to rapid technological developments and substantially reduced costs. However, although thousands of microorganisms are known to infect humans, identification of the etiological agents for many diseases remains challenging as only a small proportion of pathogens are identifiable by the current diagnostic methods. These challenges are compounded by the emergence of new pathogens. Hence, metagenomic next‐generation sequencing (mNGS), an agnostic, unbiased, and comprehensive method for detection, and taxonomic characterization of microorganisms, has become an attractive strategy. Although many studies, and cases reports, have confirmed the success of mNGS in improving the diagnosis, treatment, and tracking of infectious diseases, several hurdles must still be overcome. It is, therefore, imperative that practitioners and clinicians understand both the benefits and limitations of mNGS when applying it to clinical practice. Interestingly, the emerging third‐generation sequencing technologies may partially offset the disadvantages of mNGS. In this review, mainly: a) the history of sequencing technology; b) various NGS technologies, common platforms, and workflows for clinical applications; c) the application of NGS in pathogen identification; d) the global expert consensus on NGS‐related methods in clinical applications; and e) challenges associated with diagnostic metagenomics are described.

          Abstract

          Sequencing technology is becoming increasingly available in clinic. This review sheds lights on the most commonly used metagenomic next‐generation sequencing (mNGS). History and different platforms, current workflows, and applications of mNGS in pathogens identification, as well as challenges in the diagnostic metagenomics, are discussed. mNGS cannot substitute for traditional methods in the short term, but plays an irreplaceable role in microbiological detection.

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          Most cited references217

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          Is Open Access

          Trimmomatic: a flexible trimmer for Illumina sequence data

          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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            Fast gapped-read alignment with Bowtie 2.

            As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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              Is Open Access

              Fast and accurate short read alignment with Burrows–Wheeler transform

              Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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                Author and article information

                Contributors
                hu.bijie@zs-hospital.sh.cn
                Journal
                Small Methods
                Small Methods
                10.1002/(ISSN)2366-9608
                SMTD
                Small Methods
                John Wiley and Sons Inc. (Hoboken )
                2366-9608
                13 December 2020
                04 January 2021
                : 5
                : 1 ( doiID: 10.1002/smtd.v5.1 )
                : 2000792
                Affiliations
                [ 1 ] Department of Infectious Diseases Zhongshan Hospital Fudan University Shanghai 200032 China
                [ 2 ] Genoxor Medical Science and Technology Inc. Zhejiang 317317 China
                Author notes
                Author information
                https://orcid.org/0000-0002-2821-4292
                Article
                SMTD202000792
                10.1002/smtd.202000792
                7883231
                33614906
                0d1e6efd-9c0d-4816-a2c1-f84a9f46aeb7
                © 2020 Wiley‐VCH GmbH

                This article is being made freely available through PubMed Central as part of the COVID-19 public health emergency response. It can be used for unrestricted research re-use and analysis in any form or by any means with acknowledgement of the original source, for the duration of the public health emergency.

                History
                : 31 August 2020
                : 24 October 2020
                Page count
                Figures: 5, Tables: 3, Pages: 27, Words: 23721
                Categories
                Review
                Reviews
                Custom metadata
                2.0
                January 4, 2021
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.9.7 mode:remove_FC converted:15.02.2021

                clinical application,infectious disease,metagenomics,next‐generation sequencing

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