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      Genomic epidemiology of superspreading events in Austria reveals mutational dynamics and transmission properties of SARS-CoV-2

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      Science Translational Medicine
      American Association for the Advancement of Science

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          Abstract

          Epidemiological and genomic analyses uncover viral mutational dynamics and transmission bottleneck size during the early COVID-19 pandemic in Austria.

          Tracking and tracing SARS-CoV-2 mutations

          Austria was an early hotspot of SARS-CoV-2 transmission due to winter tourism. By integrating viral genomic and phylogenetic analyses with time-resolved contact tracing data, Popa et al. examined the fine-scale dynamics of viral spread within and from Austria in the spring of 2020. Epidemiologically defined phylogenetic clusters and viral mutational profiles provided evidence of the ongoing fixation of two viral alleles within transmission chains and enabled estimation of the SARS-CoV-2 bottleneck size. This study provides an epidemiologically contextualized, high-resolution picture of SARS-CoV-2 mutational dynamics in an early international transmission hub.

          Abstract

          Superspreading events shaped the coronavirus disease 2019 (COVID-19) pandemic, and their rapid identification and containment are essential for disease control. Here, we provide a national-scale analysis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) superspreading during the first wave of infections in Austria, a country that played a major role in initial virus transmissions in Europe. Capitalizing on Austria’s well-developed epidemiological surveillance system, we identified major SARS-CoV-2 clusters during the first wave of infections and performed deep whole-genome sequencing of more than 500 virus samples. Phylogenetic-epidemiological analysis enabled the reconstruction of superspreading events and charts a map of tourism-related viral spread originating from Austria in spring 2020. Moreover, we exploited epidemiologically well-defined clusters to quantify SARS-CoV-2 mutational dynamics, including the observation of low-frequency mutations that progressed to fixation within the infection chain. Time-resolved virus sequencing unveiled viral mutation dynamics within individuals with COVID-19, and epidemiologically validated infector-infectee pairs enabled us to determine an average transmission bottleneck size of 10 3 SARS-CoV-2 particles. In conclusion, this study illustrates the power of combining epidemiological analysis with deep viral genome sequencing to unravel the spread of SARS-CoV-2 and to gain fundamental insights into mutational dynamics and transmission properties.

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus–Infected Pneumonia in Wuhan, China

            In December 2019, novel coronavirus (2019-nCoV)-infected pneumonia (NCIP) occurred in Wuhan, China. The number of cases has increased rapidly but information on the clinical characteristics of affected patients is limited.
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              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

                Journal
                Sci Transl Med
                Sci Transl Med
                STM
                scitranslmed
                Science Translational Medicine
                American Association for the Advancement of Science
                1946-6234
                1946-6242
                09 December 2020
                23 November 2020
                : 12
                : 573
                : eabe2555
                Affiliations
                [1 ]CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria.
                [2 ]Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.
                [3 ]Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
                [4 ]Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA.
                [5 ]Austrian Agency for Health and Food Safety (AGES), 1220 Vienna, Austria.
                [6 ]Center for Virology, Medical University of Vienna, 1090 Vienna, Austria.
                [7 ]Bioinformatics and Biostatistics Platform, Department of Biomedical Sciences, University of Veterinary Medicine, 1210 Vienna, Austria.
                [8 ]Institute for Research in Biomedicine (IRB), 08028 Barcelona, Spain.
                [9 ]Institute of Virology, Medical University Innsbruck, 6020 Innsbruck, Austria.
                [10 ]Department of Medicine IV, Kaiser Franz Josef Hospital, 1100 Vienna, Austria.
                [11 ]Department of Internal Medicine II, Medical University of Innsbruck, 6020 Innsbruck, Austria.
                [12 ]Department of Theoretical Chemistry, University of Vienna, 1090 Vienna, Austria.
                [13 ]Research Group Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna, 1090 Vienna, Austria.
                [14 ]Center for Physiology and Pharmacology, Medical University of Vienna, 1090 Vienna, Austria.
                [15 ]Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain.
                [16 ]Broad Institute of MIT and Harvard, Cambridge, MA, USA.
                [17 ]Ludwig Center at Harvard, Boston, MA, USA.
                [18 ]Center for Cancer Evolution, Dana-Farber Cancer Institute, Boston, MA, USA.
                [19 ]Department of Laboratory Medicine, Medical University of Vienna, 1090 Vienna, Austria.
                Author notes
                [*]

                These authors contributed equally to this work.

                [†]

                These authors contributed equally to this work.

                []Corresponding author. Email: abergthaler@ 123456cemm.oeaw.ac.at
                Author information
                http://orcid.org/0000-0001-7156-272X
                http://orcid.org/0000-0003-4502-1094
                http://orcid.org/0000-0003-2813-8979
                http://orcid.org/0000-0002-0044-8396
                http://orcid.org/0000-0003-3478-5304
                http://orcid.org/0000-0002-9115-8756
                http://orcid.org/0000-0002-2026-0163
                http://orcid.org/0000-0003-2592-1722
                http://orcid.org/0000-0003-2975-8969
                http://orcid.org/0000-0002-5137-9976
                http://orcid.org/0000-0001-7804-1134
                http://orcid.org/0000-0002-1956-1882
                http://orcid.org/0000-0001-8011-5825
                http://orcid.org/0000-0001-7571-0566
                http://orcid.org/0000-0002-4089-4104
                http://orcid.org/0000-0001-8906-9562
                http://orcid.org/0000-0002-9870-2339
                http://orcid.org/0000-0002-6299-3226
                http://orcid.org/0000-0001-9103-7041
                http://orcid.org/0000-0003-0709-2158
                http://orcid.org/0000-0003-0925-5205
                http://orcid.org/0000-0003-4869-8842
                http://orcid.org/0000-0001-6091-3088
                http://orcid.org/0000-0003-0597-1976
                Article
                abe2555
                10.1126/scitranslmed.abe2555
                7857414
                33229462
                401b1027-ef6e-44ed-a03a-48477405ab21
                Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).

                This is an open-access article distributed under the terms of the Creative Commons Attribution license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 12 August 2020
                : 16 November 2020
                Funding
                Funded by: doi http://dx.doi.org/10.13039/100016394, Ludwig Center at Harvard;
                Funded by: doi http://dx.doi.org/10.13039/501100001821, Vienna Science and Technology Fund;
                Award ID: COVID-19 Rapid Response
                Categories
                Research Article
                Research Articles
                STM r-articles
                Medicine
                Coronavirus
                Custom metadata
                Catherine Charneski
                Penchie Limbo

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