Rapid epidemic expansion of the SARS-CoV-2 Omicron variant in southern Africa

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Abstract

The SARS-CoV-2 epidemic in southern Africa has been characterized by three distinct waves. The first was associated with a mix of SARS-CoV-2 lineages, while the second and third waves were driven by the Beta (B.1.351) and Delta (B.1.617.2) variants, respectively ^{1–
3}. In November 2021, genomic surveillance teams in South Africa and Botswana detected a new SARS-CoV-2 variant associated with a rapid resurgence of infections in Gauteng province, South Africa. Within three days of the first genome being uploaded, it was designated a variant of concern (Omicron, B.1.1.529) by the World Health Organization and, within three weeks, had been identified in 87 countries. The Omicron variant is exceptional for carrying over 30 mutations in the spike glycoprotein, which are predicted to influence antibody neutralization and spike function ⁴. Here we describe the genomic profile and early transmission dynamics of Omicron, highlighting the rapid spread in regions with high levels of population immunity.

Abstract

The genomic profile and early transmission dynamics of the Omicron strain of SARS-CoV-2.

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RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies

Alexandros Stamatakis (2014)

Motivation: Phylogenies are increasingly used in all fields of medical and biological research. Moreover, because of the next-generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. RAxML (Randomized Axelerated Maximum Likelihood) is a popular program for phylogenetic analyses of large datasets under maximum likelihood. Since the last RAxML paper in 2006, it has been continuously maintained and extended to accommodate the increasingly growing input datasets and to serve the needs of the user community. Results: I present some of the most notable new features and extensions of RAxML, such as a substantial extension of substitution models and supported data types, the introduction of SSE3, AVX and AVX2 vector intrinsics, techniques for reducing the memory requirements of the code and a plethora of operations for conducting post-analyses on sets of trees. In addition, an up-to-date 50-page user manual covering all new RAxML options is available. Availability and implementation: The code is available under GNU GPL at https://github.com/stamatak/standard-RAxML. Contact: alexandros.stamatakis@h-its.org Supplementary information: Supplementary data are available at Bioinformatics online.

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Integrative Genomics Viewer

James Robinson, Helga Thorvaldsdóttir, Wendy Winckler … (2011)

To the Editor Rapid improvements in sequencing and array-based platforms are resulting in a flood of diverse genome-wide data, including data from exome and whole genome sequencing, epigenetic surveys, expression profiling of coding and non-coding RNAs, SNP and copy number profiling, and functional assays. Analysis of these large, diverse datasets holds the promise of a more comprehensive understanding of the genome and its relation to human disease. Experienced and knowledgeable human review is an essential component of this process, complementing computational approaches. This calls for efficient and intuitive visualization tools able to scale to very large datasets and to flexibly integrate multiple data types, including clinical data. However, the sheer volume and scope of data poses a significant challenge to the development of such tools. To address this challenge we developed the Integrative Genomics Viewer (IGV), a lightweight visualization tool that enables intuitive real-time exploration of diverse, large-scale genomic datasets on standard desktop computers. It supports flexible integration of a wide range of genomic data types including aligned sequence reads, mutations, copy number, RNAi screens, gene expression, methylation, and genomic annotations (Figure S1). The IGV makes use of efficient, multi-resolution file formats to enable real-time exploration of arbitrarily large datasets over all resolution scales, while consuming minimal resources on the client computer (see Supplementary Text). Navigation through a dataset is similar to Google Maps, allowing the user to zoom and pan seamlessly across the genome at any level of detail from whole-genome to base pair (Figure S2). Datasets can be loaded from local or remote sources, including cloud-based resources, enabling investigators to view their own genomic datasets alongside publicly available data from, for example, The Cancer Genome Atlas (TCGA) 1 , 1000 Genomes (www.1000genomes.org/), and ENCODE 2 (www.genome.gov/10005107) projects. In addition, IGV allows collaborators to load and share data locally or remotely over the Web. IGV supports concurrent visualization of diverse data types across hundreds, and up to thousands of samples, and correlation of these integrated datasets with clinical and phenotypic variables. A researcher can define arbitrary sample annotations and associate them with data tracks using a simple tab-delimited file format (see Supplementary Text). These might include, for example, sample identifier (used to link different types of data for the same patient or tissue sample), phenotype, outcome, cluster membership, or any other clinical or experimental label. Annotations are displayed as a heatmap but more importantly are used for grouping, sorting, filtering, and overlaying diverse data types to yield a comprehensive picture of the integrated dataset. This is illustrated in Figure 1, a view of copy number, expression, mutation, and clinical data from 202 glioblastoma samples from the TCGA project in a 3 kb region around the EGFR locus 1, 3 . The investigator first grouped samples by tumor subtype, then by data type (copy number and expression), and finally sorted them by median copy number over the EGFR locus. A shared sample identifier links the copy number and expression tracks, maintaining their relative sort order within the subtypes. Mutation data is overlaid on corresponding copy number and expression tracks, based on shared participant identifier annotations. Several trends in the data stand out, such as a strong correlation between copy number and expression and an overrepresentation of EGFR amplified samples in the Classical subtype. IGV’s scalable architecture makes it well suited for genome-wide exploration of next-generation sequencing (NGS) datasets, including both basic aligned read data as well as derived results, such as read coverage. NGS datasets can approach terabytes in size, so careful management of data is necessary to conserve compute resources and to prevent information overload. IGV varies the displayed level of detail according to resolution scale. At very wide views, such as the whole genome, IGV represents NGS data by a simple coverage plot. Coverage data is often useful for assessing overall quality and diagnosing technical issues in sequencing runs (Figure S3), as well as analysis of ChIP-Seq 4 and RNA-Seq 5 experiments (Figures S4 and S5). As the user zooms below the ~50 kb range, individual aligned reads become visible (Figure 2) and putative SNPs are highlighted as allele counts in the coverage plot. Alignment details for each read are available in popup windows (Figures S6 and S7). Zooming further, individual base mismatches become visible, highlighted by color and intensity according to base call and quality. At this level, the investigator may sort reads by base, quality, strand, sample and other attributes to assess the evidence of a variant. This type of visual inspection can be an efficient and powerful tool for variant call validation, eliminating many false positives and aiding in confirmation of true findings (Figures S6 and S7). Many sequencing protocols produce reads from both ends (“paired ends”) of genomic fragments of known size distribution. IGV uses this information to color-code paired ends if their insert sizes are larger than expected, fall on different chromosomes, or have unexpected pair orientations. Such pairs, when consistent across multiple reads, can be indicative of a genomic rearrangement. When coloring aberrant paired ends, each chromosome is assigned a unique color, so that intra- (same color) and inter- (different color) chromosomal events are readily distinguished (Figures 2 and S8). We note that misalignments, particularly in repeat regions, can also yield unexpected insert sizes, and can be diagnosed with the IGV (Figure S9). There are a number of stand-alone, desktop genome browsers available today 6 including Artemis 7 , EagleView 8 , MapView 9 , Tablet 10 , Savant 11 , Apollo 12 , and the Integrated Genome Browser 13 . Many of them have features that overlap with IGV, particularly for NGS sequence alignment and genome annotation viewing. The Integrated Genome Browser also supports viewing array-based data. See Supplementary Table 1 and Supplementary Text for more detail. IGV focuses on the emerging integrative nature of genomic studies, placing equal emphasis on array-based platforms, such as expression and copy-number arrays, next-generation sequencing, as well as clinical and other sample metadata. Indeed, an important and unique feature of IGV is the ability to view all these different data types together and to use the sample metadata to dynamically group, sort, and filter datasets (Figure 1 above). Another important characteristic of IGV is fast data loading and real-time pan and zoom – at all scales of genome resolution and all dataset sizes, including datasets comprising hundreds of samples. Finally, we have placed great emphasis on the ease of installation and use of IGV, with the goal of making both the viewing and sharing of their data accessible to non-informatics end users. IGV is open source software and freely available at http://www.broadinstitute.org/igv/, including full documentation on use of the software. Supplementary Material 1

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FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments

Morgan N. Price, Paramvir S Dehal, Adam Arkin (2010)

Background We recently described FastTree, a tool for inferring phylogenies for alignments with up to hundreds of thousands of sequences. Here, we describe improvements to FastTree that improve its accuracy without sacrificing scalability. Methodology/Principal Findings Where FastTree 1 used nearest-neighbor interchanges (NNIs) and the minimum-evolution criterion to improve the tree, FastTree 2 adds minimum-evolution subtree-pruning-regrafting (SPRs) and maximum-likelihood NNIs. FastTree 2 uses heuristics to restrict the search for better trees and estimates a rate of evolution for each site (the “CAT” approximation). Nevertheless, for both simulated and genuine alignments, FastTree 2 is slightly more accurate than a standard implementation of maximum-likelihood NNIs (PhyML 3 with default settings). Although FastTree 2 is not quite as accurate as methods that use maximum-likelihood SPRs, most of the splits that disagree are poorly supported, and for large alignments, FastTree 2 is 100–1,000 times faster. FastTree 2 inferred a topology and likelihood-based local support values for 237,882 distinct 16S ribosomal RNAs on a desktop computer in 22 hours and 5.8 gigabytes of memory. Conclusions/Significance FastTree 2 allows the inference of maximum-likelihood phylogenies for huge alignments. FastTree 2 is freely available at http://www.microbesonline.org/fasttree.

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Author and article information

Contributors

Tulio de Oliveira:

ORCID: http://orcid.org/0000-0002-3027-5254

tulio@sun.ac.za

Journal

Journal ID (nlm-ta): Nature

Journal ID (iso-abbrev): Nature

Title: Nature

Publisher: Nature Publishing Group UK (London )

ISSN (Print): 0028-0836

ISSN (Electronic): 1476-4687

Publication date (Electronic): 7 January 2022

Publication date PMC-release: 7 January 2022

Publication date (Print): 2022

Volume: 603

Issue: 7902

Pages: 679-686

Affiliations

[1 ]GRID grid.511132.5, ISNI 0000 0004 0500 3622, Lancet Laboratories, ; Johannesburg, South Africa

[2 ]GRID grid.462829.3, Botswana Harvard AIDS Institute Partnership, , Botswana Harvard HIV Reference Laboratory, ; Gaborone, Botswana

[3 ]GRID grid.38142.3c, ISNI 000000041936754X, Harvard T.H. Chan School of Public Health, ; Boston, MA USA

[4 ]Botswana Presidential COVID-19 Taskforce, Gaborone, Botswana

[5 ]GRID grid.416657.7, ISNI 0000 0004 0630 4574, National Institute for Communicable Diseases (NICD) of the National Health Laboratory Service (NHLS), ; Johannesburg, South Africa

[6 ]GRID grid.16463.36, ISNI 0000 0001 0723 4123, KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R. Mandela School of Medicine, , University of KwaZulu-Natal, ; Durban, South Africa

[7 ]GRID grid.11951.3d, ISNI 0000 0004 1937 1135, South African Medical Research Council Antibody Immunity Research Unit, School of Pathology, Faculty of Health Sciences, , University of the Witwatersrand, ; Johannesburg, South Africa

[8 ]GRID grid.5734.5, ISNI 0000 0001 0726 5157, Institute of Social and Preventive Medicine, , University of Bern, ; Bern, Switzerland

[9 ]GRID grid.416657.7, ISNI 0000 0004 0630 4574, Division of Virology, , National Health Laboratory Service, ; Bloemfontein, South Africa

[10 ]GRID grid.412219.d, ISNI 0000 0001 2284 638X, Division of Virology, , University of the Free State, ; Bloemfontein, South Africa

[11 ]GRID grid.29857.31, ISNI 0000 0001 2097 4281, Center for Infectious Disease Dynamics, Department of Biology, , Pennsylvania State University, ; University Park, PA USA

[12 ]Diagnofirm Medical Laboratories, Gaborone, Botswana

[13 ]GRID grid.4305.2, ISNI 0000 0004 1936 7988, Institute of Evolutionary Biology, , University of Edinburgh, ; Edinburgh, UK

[14 ]GRID grid.49697.35, ISNI 0000 0001 2107 2298, Zoonotic Arbo and Respiratory Virus Program, Centre for Viral Zoonoses, Department of Medical Virology, , University of Pretoria, ; Pretoria, South Africa

[15 ]GRID grid.508029.6, Emweb, ; Herent, Belgium

[16 ]GRID grid.7836.a, ISNI 0000 0004 1937 1151, Division of Medical Virology, Faculty of Health Sciences, , University of Cape Town, ; Cape Town, South Africa

[17 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, Department of Zoology, , University of Oxford, ; Oxford, UK

[18 ]GRID grid.5801.c, ISNI 0000 0001 2156 2780, Department of Biosystems Science and Engineering, , ETH Zurich, ; Zurich, Switzerland

[19 ]GRID grid.11956.3a, ISNI 0000 0001 2214 904X, Division of Medical Virology, Faculty of Medicine and Health Sciences, , Stellenbosch University, Tygerberg, ; Cape Town, South Africa

[20 ]GRID grid.418068.3, ISNI 0000 0001 0723 0931, Laboratorio de Flavivirus, , Fundacao Oswaldo Cruz, ; Rio de Janeiro, Brazil

[21 ]GRID grid.8430.f, ISNI 0000 0001 2181 4888, Laboratório de Genética Celular e Molecular, , Universidade Federal de Minas Gerais, ; Belo Horizonte, Brazil

[22 ]Division of Virology, NHLS Groote Schuur Laboratory, Cape Town, South Africa

[23 ]GRID grid.497864.0, Wellcome Centre for Infectious Diseases Research in Africa (CIDRI-Africa), ; Cape Town, South Africa

[24 ]GRID grid.7836.a, ISNI 0000 0004 1937 1151, Division of Computational Biology, Faculty of Health Sciences, , University of Cape Town, ; Cape Town, South Africa

[25 ]GRID grid.264727.2, ISNI 0000 0001 2248 3398, Institute for Genomics and Evolutionary Medicine, Department of Biology, , Temple University, ; Philadelphia, PA USA

[26 ]GRID grid.415807.f, Health Services Management, , Ministry of Health and Wellness, ; Gaborone, Botswana

[27 ]NHLS Port Elizabeth Laboratory, Port Elizabeth, South Africa

[28 ]GRID grid.412870.8, ISNI 0000 0001 0447 7939, Faculty of Health Sciences, , Walter Sisulu University, ; Mthatha, South Africa

[29 ]GRID grid.415807.f, Public Health Department, Integrated Disease Surveillance and Response, , Ministry of Health and Wellness, ; Gaborone, Botswana

[30 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Immunology and Infectious Diseases, , Harvard T.H. Chan School of Public Health, Boston, ; MA, USA

[31 ]GRID grid.417371.7, ISNI 0000 0004 0635 423X, NHLS Tygerberg Laboratory, , Tygerberg Hospital, ; Cape Town, South Africa

[32 ]GRID grid.414707.1, ISNI 0000 0001 0364 9292, Department of Virology, , Charlotte Maxeke Johannesburg Academic Hospital, ; Johannesburg, South Africa

[33 ]GRID grid.463139.a, Botswana-Baylor Children’s Clinical Centre of Excellence, ; Gaborone, Botswana

[34 ]GRID grid.39382.33, ISNI 0000 0001 2160 926X, Baylor College of Medicine, ; Houston, TX USA

[35 ]GRID grid.49697.35, ISNI 0000 0001 2107 2298, Department of Medical Virology, , University of Pretoria, ; Pretoria, South Africa

[36 ]GRID grid.415807.f, National Health Laboratory, Health Services Management, , Ministry of Health and Wellness, ; Gaborone, Botswana

[37 ]GRID grid.416657.7, ISNI 0000 0004 0630 4574, National Health Laboratory Service (NHLS), ; Johannesburg, South Africa

[38 ]GRID grid.428428.0, ISNI 0000 0004 5938 4248, Centre for the AIDS Programme of Research in South Africa (CAPRISA), ; Durban, South Africa

[39 ]GRID grid.11956.3a, ISNI 0000 0001 2214 904X, Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, , Stellenbosch University, ; Stellenbosch, South Africa

[40 ]GRID grid.7621.2, ISNI 0000 0004 0635 5486, Department of Medicine, Faculty of Medicine, , University of Botswana, ; Gaborone, Botswana

[41 ]GRID grid.7621.2, ISNI 0000 0004 0635 5486, Department of Medical Laboratory Sciences, School of Allied Health Professions, Faculty of Health Sciences, , University of Botswana, ; Gaborone, Botswana

[42 ]GRID grid.16463.36, ISNI 0000 0001 0723 4123, Discipline of Virology, School of Laboratory Medicine and Medical Sciences and National Health Laboratory Service (NHLS), , University of KwaZulu-Natal, ; Durban, South Africa

[43 ]GRID grid.412219.d, ISNI 0000 0001 2284 638X, Next Generation Sequencing Unit, Division of Virology, Faculty of Health Sciences, , University of the Free State, ; Bloemfontein, South Africa

[44 ]GRID grid.11951.3d, ISNI 0000 0004 1937 1135, Department of Molecular Medicine and Haematology, , University of the Witwatersrand, ; Johannesburg, South Africa

[45 ]GRID grid.11951.3d, ISNI 0000 0004 1937 1135, School of Pathology, Faculty of Health Sciences, , University of the Witwatersrand, ; Johannesburg, South Africa

[46 ]PathCare Vermaak, Pretoria, South Africa

[47 ]GRID grid.7836.a, ISNI 0000 0004 1937 1151, Institute of Infectious Disease and Molecular Medicine, , University of Cape Town, ; Cape Town, South Africa

[48 ]GRID grid.11951.3d, ISNI 0000 0004 1937 1135, Department of Molecular Pathology, School of Pathology, Faculty of Health Sciences, , University of the Witwatersrand, ; Johannesburg, South Africa

[49 ]GRID grid.34477.33, ISNI 0000000122986657, Department of Global Health, , University of Washington, ; Seattle, WA USA

Author information

Sikhulile Moyo http://orcid.org/0000-0003-3821-4592

Daniel G. Amoako http://orcid.org/0000-0003-3551-3458

Christian L. Althaus http://orcid.org/0000-0002-5230-6760

Maciej F. Boni http://orcid.org/0000-0002-0830-9630

Koen Deforche http://orcid.org/0000-0003-3898-9494

Louis du Plessis http://orcid.org/0000-0003-0352-6289

Susan Engelbrecht http://orcid.org/0000-0002-1318-5994

Josie Everatt http://orcid.org/0000-0002-0373-2093

Marta Giovanetti http://orcid.org/0000-0002-5849-7326

Nei-Yuan Hsiao http://orcid.org/0000-0003-4926-6216

Arash Iranzadeh http://orcid.org/0000-0003-4413-2569

Arshad Ismail http://orcid.org/0000-0003-4672-5915

Sergei L. Kosakovsky Pond http://orcid.org/0000-0003-4817-4029

Moritz U. G. Kraemer http://orcid.org/0000-0001-8838-7147

Richard J. Lessells http://orcid.org/0000-0003-0926-710X

Boitshoko Mahlangu http://orcid.org/0000-0002-2129-527X

Upasana Ramphal http://orcid.org/0000-0002-2441-3868

Steven Weaver http://orcid.org/0000-0002-6931-7191

Wolfgang Preiser http://orcid.org/0000-0002-0254-7910

Carolyn Williamson http://orcid.org/0000-0003-0125-1226

Oliver G. Pybus http://orcid.org/0000-0002-8797-2667

Jinal Bhiman http://orcid.org/0000-0001-6354-4003

Andrew Rambaut http://orcid.org/0000-0003-4337-3707

Tulio de Oliveira http://orcid.org/0000-0002-3027-5254

Article

Publisher ID: 4411

DOI: 10.1038/s41586-022-04411-y

PMC ID: 8942855

PubMed ID: 35042229

SO-VID: a6ed1a49-cef6-4954-a9e4-7d458e895306

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 18 December 2021

Date accepted : 7 January 2022

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ScienceOpen disciplines: Uncategorized

Keywords: molecular evolution,epidemiology,sars-cov-2

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ScienceOpen disciplines: Uncategorized

Keywords: molecular evolution, epidemiology, sars-cov-2

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Rapid epidemic expansion of the SARS-CoV-2 Omicron variant in southern Africa

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Novel Coronavirus Disease COVID-19

Most cited references 58

RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies

Integrative Genomics Viewer

FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments

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Cited by 692

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