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Revista Cubana de Información en Ciencias de la Salud (ACIMED)

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2021, Number 2

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Revista Cubana de Información en Ciencias de la Salud (ACIMED) 2021; 32 (2)

Nextstrain: a tool to analyze the molecular epidemiology of SARSCoV- 2

Iglesias-Osores S, Alcántara-Mimbela M, Arce-Gil Z, Córdova-Rojas LM, López-López E, Rafael-Heredia A
Full text How to cite this article

Language: Spanish
References: 38
Page: 1-22
PDF size: 653.78 Kb.


Key words:

Phylogenetics, SARS-CoV-2, COVID-19, Nextstrain, epidemiology.

ABSTRACT

Worldwide concern about the novel coronavirus (2019-nCoV) as a global threat to public health is the reason for the exponential growth of phylogenetic analyses. The purpose of this review was to describe the mode of operation and advantages of the tool Nextstrain, as well as the sequencing of the SARS-CoV-2 virus worldwide. The interface of the Nextstrain page was used to show its functions and data visualization modes. These were downloaded from the website GISAID to show the number of SARS-CoV-2 sequencing processes performed so far. Nextstrain is an open code project created by bioinformatics biologists to make good use of the scientific and public health potential of data about genomes of pathogens. Nextstrain consists in a set of tools operating with unprocessed sequences (in FASTA format). Nextstrain performs a sequence alignment of the input data into a multiple sequence alignment based on fast Fourier transform. Its use is based on two software applications: Augur and Auspice. Nextstrain is an efficient tool by which lay people may obtain epidemiological data in a simple manner. It may be used in the public health sector, since it shows real time data about epidemics and their geographic distribution. It may also be used to follow-up outbreaks, as is the case with COVID-19.


REFERENCES

  1. Benvenuto D, Giovanetti M, Ciccozzi A, Spoto S, Angeletti S, Ciccozzi M.The 2019-new coronavirus epidemic: Evidence for virus evolution. J Med Virol[Internet]. 2020 [acceso: 25/07/2020];92(4):455-9. Disponible en:https://onlinelibrary.wiley.com/doi/full/10.1002/jmv.25688

  2. Fleming PL, Wortley PM, Karon JM, DeCock KM, Janssen RS. Tracking theHIV epidemic: Current issues, future challenges [Internet]. Am J Publ Health.2000 [acceso: 25/07/2020];90(7):1037-41. Disponible en:https://www.pmc/articles/PMC1446284/?report=abstract

  3. Pastor-Satorras R, Castellano C, Van Mieghem P, Vespignani A. Epidemicprocesses in complex networks. Rev Mod Phys [Internet]. 2015 [acceso:25/07/2020];87(3):925. Disponible en:https://journals.aps.org/rmp/abstract/10.1103/RevModPhys.87.925

  4. Keeling MJ, Eames KT. Networks and epidemic models. J Roy Soc Interface[Internet]. 2005 [acceso: 25/07/2020];2(4):295-307. Disponible en:https://royalsocietypublishing.org/doi/10.1098/rsif.2005.0051

  5. Brockmann D. Digital epidemiology. Bundesgesundh Gesundheits Gesundh.2020;63(2):166-75.

  6. Ladner JT, Grubaugh ND, Pybus OG, Andersen KG. Precision epidemiologyfor infectious disease control. Nat Med. 2019;25(2):206-11.

  7. von Bubnoff A. Next-Generation Sequencing: The Race Is On. Cell Press.2008;132:721-3.

  8. Sagulenko P, Puller V, Neher RA. TreeTime: Maximum-likelihoodphylodynamic analysis. Virus Evol. 2018;4(1):1.

  9. Suchard MA, Lemey P, Baele G, Ayres DL, Drummond AJ, Rambaut A.Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10.Virus Evol [Internet]. 2018 [acceso: 25/07/2020];4(1). Disponible en:https://pubmed.ncbi.nlm.nih.gov/29942656/

  10. Hadfield J, Megill C, Bell SM, Huddleston J, Potter B, Callender C, et al.Nextstrain: real-time tracking of pathogen evolution. Bioinformatics[Internet]. 2018 [acceso: 10/04/2020];34(23):4121-3. Disponible en:https://academic.oup.com/bioinformatics/article/34/23/4121/5001388

  11. Hadfield J, Brito AF, Swetnam DM, Vogels CBF, Tokarz RE, Andersen KG,et al. Twenty years of West Nile virus spread and evolution in the Americasvisualized by Nextstrain [Internet]. PLoS Pathogens: Public Library of Science;2019 [acceso: 25/07/2020]. p. e1008042. Disponible en:https://doi.org/10.1371/journal.ppat.1008042

  12. Pearson WR. Finding Protein and Nucleotide Similarities with FASTA. CurrProtoc Bioinform [Internet]. 2016 [acceso: 26/07/2020];53(1):391-925.Disponible en:https://onlinelibrary.wiley.com/doi/abs/10.1002/0471250953.bi0309s53

  13. Katoh K, Standley DM. MAFFT Multiple Sequence Alignment Software:Improvements in Performance and Usability. Mol Biol Evol [Internet].2013;30(4):772-80. DOI: https://doi.org/10.1093/molbev/mst010

  14. Lanave C, Preparata G, Sacone C, Serio G. A new method for calculatingevolutionary substitution rates. J Mol Evol [Internet]. 1984 [acceso:24/07/2020];20(1):86-93. Disponible en:https://link.springer.com/article/10.1007/BF02101990

  15. To TH, Jung M, Lycett S, Gascuel O. Fast Dating Using Least-SquaresCriteria and Algorithms. Syst Biol [Internet]. 2015;65(1):82-97. DOI:https://doi.org/10.1093/sysbio/syv068

  16. Junqueira DM, Wilkinson E, Vallari A, Deng X, Achari A, Yu G, et al. Newgenomes from the Congo Basin Expand History of CRF01_AE Origin and Dissemination. AIDS Res Hum Retrovir [Internet]. 2020 [acceso:24/07/2020];36(7):574-82. Disponible en:https://www.liebertpub.com/doi/10.1089/aid.2020.0031

  17. Billion A, Ghai R, Chakraborty T, Hain T. Augur - a computational pipelinefor whole genome microbial surface protein prediction and classification.Bioinformatics [Internet]. 2006;22(22):2819-20. DOI:https://doi.org/10.1093/bioinformatics/btl466

  18. Katoh K, Misawa K, Kuma K, Miyata T. MAFFT: a novel method for rapidmultiple sequence alignment based on fast Fourier transform. Nucleic AcidsRes [Internet]. 2002 [acceso: 25/07/2020];30(14):3059-66. Disponible en:https://pubmed.ncbi.nlm.nih.gov/12136088

  19. Nguyen LT, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: a fast andeffective stochastic algorithm for estimating maximum-likelihood phylogenies.Mol Biol Evol. 2015;32(1):268-74.

  20. Perkel J. Democratic databases: Science on GitHub. Nature [Internet].2016 [acceso: 25/07/2020];538(7623):127-8. Disponible en:http://www.nature.com/news/democratic-databases-science-on-github-1.20719

  21. Elbe S, Buckland-Merrett G. Data, disease and diplomacy: GISAID’sinnovative contribution to global health. Glob Challenges. 2017;1(1):33-46.

  22. Seberg O, Petersen G. Assembling the Tree of Life [Internet]. OxfordUniversity Press; 2006 [acceso: 26/07/2020]. p. 33-46. Disponible en:https://books.google.com.pe/books/about/Assembling_the_Tree_of_Life.html?id=6lXTP0YU6_kC&redir_esc=y

  23. Han AX, Parker E, Scholer F, Maurer-Stroh S, Russell CA. PhylogeneticClustering by Linear Integer Programming (PhyCLIP). Mol Biol Evol [Internet].2019;36(7):1580-95. DOI: https://doi.org/10.1093/molbev/msz053

  24. Tang X, Wu C, Li X, Song Y, Yao X, Wu X, et al. On the origin andcontinuing evolution of SARS-CoV-2. Natl Sci Rev [Internet]. 2020;7(6):1012-23. DOI: https://doi.org/10.1093/nsr/nwaa036

  25. Rambaut A, Holmes EC, O’Toole Á, Hill V, McCrone JT, Ruis C, et al. Adynamic nomenclature proposal for SARS-CoV-2 lineages to assist genomic epidemiology. Nat Microbiol [Internet]. 2020 [acceso: 25/07/2020];1-5.Disponible en:http://www.nature.com/articles/s41564-020-0770-5

  26. Hodcroft EB, Hadfield J, Neher RA, Bedford T. Year-letter genetic cladenaming for SARS-CoV-2 on nextstrain.org [Internet]. Nextstrain. 2020 [acceso:25/07/2020]. Disponible en: https://nextstrain.org/blog/2020-06-02-SARSCoV2-clade-naming

  27. Rife BD, Mavian C, Chen X, Ciccozzi M, Salemi M, Min J, et al.Phylodynamic applications in 21st century global infectious disease research.Glob Heal Res Policy. 2017;2(1):1-10.

  28. Monteil V, Kwon H, Prado P, Hagelkrüys A, Wimmer RA, Stahl M, et al.Inhibition of SARS-CoV-2 Infections in Engineered Human Tissues UsingClinical-Grade Soluble Human ACE2. Cell. 2020;181(4):905-13.

  29. Lee JM, Huddleston J, Doud MB, Hooper KA, Wu NC, Bedford T, et al.Deep mutational scanning of hemagglutinin helps predict evolutionary fates ofhuman H3N2 influenza variants. Proc Natl Acad Sci USA [Internet]. 2018[acceso: 26/07/2020];115(35):E8276-85. Disponible en:https://www.pnas.org/content/115/35/E8276

  30. Yamayoshi S, Kawaoka Y. Current and future influenza vaccines. Nat Med[Internet]. 2019 [acceso: 12/04/2020];25(2):212-20. Disponible en:http://www.nature.com/articles/s41591-018-0340-z

  31. Dolan PT, Whitfield ZJ, Andino R. Mechanisms and Concepts in RNA VirusPopulation Dynamics and Evolution. Annu Rev Virol [Internet]. 2018 [acceso:26/07/2020];5(1):69-92. Disponible en:https://www.annualreviews.org/doi/abs/10.1146/annurev-virology-101416-041718

  32. van de Vossenberg BTLH, Visser M, Bruinsma M, Koenraadt HMS,Westenberg M, Botermans M. Real-time tracking of Tomato brown rugose fruitvirus (ToBRFV) outbreaks in the Netherlands using Nextstrain. bioRxiv[Internet]. 2020 [acceso: 26/07/2020];06(02):129395. Disponible en:http://biorxiv.org/content/early/2020/06/02/2020.06.02.129395.abstract

  33. Vega-Fernández J, Iglesias-Osores S, Tullume-Vergara P. Use of abioinformatic tool for the molecular epidemiology of SARS-CoV-2. Univ MédPinar [Internet]. 2020 [acceso: 14/04/2020];16(3):3-5. Disponible en:http://revgaleno.sld.cu/index.php/ump/article/view/530

  34. Wang JT, Lin YY, Chang SY, Yeh SH, Hu BH, Chen PJ, et al. The role ofphylogenetic analysis in clarifying the infection source of a COVID-19 patient.J Infect. 2020;81(1):147-78.

  35. Fauver JR, Petrone ME, Hodcroft EB, Shioda K, Ehrlich HY, Watts AG, etal. Coast-to-Coast Spread of SARS-CoV-2 during the Early Epidemic in theUnited States. Cell. 2020;181(5):990-6.

  36. Singer JB, Thomson EC, McLauchlan J, Hughes J, Gifford RJ. GLUE: Aflexible software system for virus sequence data. BMC Bioinformatics[Internet]. 2018 [acceso: 26/07/2020];19(1):1-18. Disponible en:https://link.springer.com/articles/10.1186/s12859-018-2459-9

  37. Neher RA, Bedford T. Nextflu: real-time tracking of seasonal influenzavirus evolution in humans. Bioinformatics [Internet]. 2015;31(21):3546-8. DOI:https://doi.org/10.1093/bioinformatics/btv381

  38. Iglesias-Osores S, Iglesias-Osores S, Tullume-Vergara PO, Acosta-Quiroz J,Saavedra-Camacho JL, Rafael-Heredia A. Epidemiología genómica del virusSARS-CoV-2 con una plataforma bioinformática. Univ Méd Pinar [Internet].2020 [acceso: 25/07/2020];16(3):e555. Disponible en:http://revgaleno.sld.cu/index.php/ump/article/view/555




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Revista Cubana de Información en Ciencias de la Salud (ACIMED). 2021;32