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      Dengue virus genomic surveillance in the applying Wolbachia to eliminate dengue trial reveals genotypic efficacy and disruption of focal transmission

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          Abstract

          Release of Aedes aegypti mosquitoes infected with Wolbachia pipientis ( wMel strain) is a biocontrol approach against Ae. aegypti-transmitted arboviruses. The Applying Wolbachia to Eliminate Dengue (AWED) cluster-randomised trial was conducted in Yogyakarta, Indonesia in 2018–2020 and provided pivotal evidence for the efficacy of wMel- Ae. aegypti mosquito population replacement in significantly reducing the incidence of virologically-confirmed dengue (VCD) across all four dengue virus (DENV) serotypes. Here, we sequenced the DENV genomes from 318 dengue cases detected in the AWED trial, with the aim of characterising DENV genetic diversity, measuring genotype-specific intervention effects, and inferring DENV transmission dynamics in wMel-treated and untreated areas of Yogyakarta. Phylogenomic analysis of all DENV sequences revealed the co-circulation of five endemic DENV genotypes: DENV-1 genotype I (12.5%) and IV (4.7%), DENV-2 Cosmopolitan (47%), DENV-3 genotype I (8.5%), and DENV-4 genotype II (25.7%), and one recently imported genotype, DENV-4 genotype I (1.6%). The diversity of genotypes detected among AWED trial participants enabled estimation of the genotype-specific protective efficacies of wMel, which were similar (± 10%) to the point estimates of the respective serotype-specific efficacies reported previously. This indicates that wMel afforded protection to all of the six genotypes detected in Yogyakarta. We show that within this substantial overall viral diversity, there was a strong spatial and temporal structure to the DENV genomic relationships, consistent with highly focal DENV transmission around the home in wMel-untreated areas and a near-total disruption of transmission by wMel. These findings can inform long-term monitoring of DENV transmission dynamics in Wolbachia-treated areas including Yogyakarta.

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          Most cited references60

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          MultiQC: summarize analysis results for multiple tools and samples in a single report

          Motivation: Fast and accurate quality control is essential for studies involving next-generation sequencing data. Whilst numerous tools exist to quantify QC metrics, there is no common approach to flexibly integrate these across tools and large sample sets. Assessing analysis results across an entire project can be time consuming and error prone; batch effects and outlier samples can easily be missed in the early stages of analysis. Results: We present MultiQC, a tool to create a single report visualising output from multiple tools across many samples, enabling global trends and biases to be quickly identified. MultiQC can plot data from many common bioinformatics tools and is built to allow easy extension and customization. Availability and implementation: MultiQC is available with an GNU GPLv3 license on GitHub, the Python Package Index and Bioconda. Documentation and example reports are available at http://multiqc.info Contact: phil.ewels@scilifelab.se
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            Sustainable data analysis with Snakemake

            Data analysis often entails a multitude of heterogeneous steps, from the application of various command line tools to the usage of scripting languages like R or Python for the generation of plots and tables. It is widely recognized that data analyses should ideally be conducted in a reproducible way. Reproducibility enables technical validation and regeneration of results on the original or even new data. However, reproducibility alone is by no means sufficient to deliver an analysis that is of lasting impact (i.e., sustainable) for the field, or even just one research group. We postulate that it is equally important to ensure adaptability and transparency. The former describes the ability to modify the analysis to answer extended or slightly different research questions. The latter describes the ability to understand the analysis in order to judge whether it is not only technically, but methodologically valid. Here, we analyze the properties needed for a data analysis to become reproducible, adaptable, and transparent. We show how the popular workflow management system Snakemake can be used to guarantee this, and how it enables an ergonomic, combined, unified representation of all steps involved in data analysis, ranging from raw data processing, to quality control and fine-grained, interactive exploration and plotting of final results.
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              An amplicon-based sequencing framework for accurately measuring intrahost virus diversity using PrimalSeq and iVar

              How viruses evolve within hosts can dictate infection outcomes; however, reconstructing this process is challenging. We evaluate our multiplexed amplicon approach, PrimalSeq, to demonstrate how virus concentration, sequencing coverage, primer mismatches, and replicates influence the accuracy of measuring intrahost virus diversity. We develop an experimental protocol and computational tool, iVar, for using PrimalSeq to measure virus diversity using Illumina and compare the results to Oxford Nanopore sequencing. We demonstrate the utility of PrimalSeq by measuring Zika and West Nile virus diversity from varied sample types and show that the accumulation of genetic diversity is influenced by experimental and biological systems. Electronic supplementary material The online version of this article (10.1186/s13059-018-1618-7) contains supplementary material, which is available to authorized users.

                Author and article information

                Contributors
                Kathryn.Edenborough@gmail.com
                cameron.simmons@monash.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                14 November 2024
                14 November 2024
                2024
                : 14
                : 28004
                Affiliations
                [1 ]Department of Microbiology, Biomedicine Discovery Institute, Monash University, ( https://ror.org/02bfwt286) Clayton, VIC Australia
                [2 ]Centre for Tropical Medicine, Faculty of Medicine Public Health and Nursing, Universitas Gadjah Mada, ( https://ror.org/03ke6d638) Yogyakarta, Indonesia
                [3 ]GRID grid.47840.3f, ISNI 0000 0001 2181 7878, Division of Biostatistics, School of Public Health, , University of California, ; Berkeley, USA
                [4 ]Department of Child Health, Faculty of Medicine Public Health and Nursing, Universitas Gadjah Mada, ( https://ror.org/03ke6d638) Yogyakarta, Indonesia
                [5 ]Department of Epidemiology Biostatistics and Public Health, Faculty of Medicine Public Health and Nursing, Universitas Gadjah Mada, ( https://ror.org/03ke6d638) Yogyakarta, Indonesia
                [6 ]Eijkman Research Centre for Molecular Biology, National Research and Innovation Agency, ( https://ror.org/02hmjzt55) Cibinong, Bogor, 16911 Indonesia
                [7 ]World Mosquito Program, Monash University, ( https://ror.org/02bfwt286) Clayton, Melbourne, VIC Australia
                [8 ]School of Public Health and Preventive Medicine, Monash University, ( https://ror.org/02bfwt286) Prahran, Melbourne, VIC Australia
                Article
                78008
                10.1038/s41598-024-78008-y
                11564853
                39543157
                8d854629-a38d-438e-a887-66eb6bee2a98
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

                History
                : 12 March 2024
                : 28 October 2024
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000925, National Health and Medical Research Council;
                Award ID: 1173928
                Award ID: 1173928
                Award Recipient :
                Funded by: World Mosquito Program
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2024

                Uncategorized
                dengue virus,viral genetics,epidemiology,translational research,viral infection
                Uncategorized
                dengue virus, viral genetics, epidemiology, translational research, viral infection

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