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      Genotyping cognate Plasmodium falciparum in humans and mosquitoes to estimate onward transmission of asymptomatic infections

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          Abstract

          Malaria control may be enhanced by targeting reservoirs of Plasmodium falciparum transmission. One putative reservoir is asymptomatic malaria infections and the scale of their contribution to transmission in natural settings is not known. We assess the contribution of asymptomatic malaria to onward transmission using a 14-month longitudinal cohort of 239 participants in a high transmission site in Western Kenya. We identify P. falciparum in asymptomatically- and symptomatically-infected participants and naturally-fed mosquitoes from their households, genotype all parasites using deep sequencing of the parasite genes pfama1 and pfcsp, and use haplotypes to infer participant-to-mosquito transmission through a probabilistic model. In 1,242 infections (1,039 in people and 203 in mosquitoes), we observe 229 ( pfcsp) and 348 ( pfama1) unique parasite haplotypes. Using these to link human and mosquito infections, compared with symptomatic infections, asymptomatic infections more than double the odds of transmission to a mosquito among people with both infection types (Odds Ratio: 2.56; 95% Confidence Interval (CI): 1.36–4.81) and among all participants (OR 2.66; 95% CI: 2.05–3.47). Overall, 94.6% (95% CI: 93.1–95.8%) of mosquito infections likely resulted from asymptomatic infections. In high transmission areas, asymptomatic infections are the major contributor to mosquito infections and may be targeted as a component of transmission reduction.

          Abstract

          Asymptomatic malaria infections contribute to transmission. Here, Sumner et al. infer participant-to-mosquito transmission by sampling naturally-fed mosquitoes from households in Western Kenya and find that asymptomatic infections more than double the odds of transmission to a mosquito compared to symptomatic infections.

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          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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            DADA2: High resolution sample inference from Illumina amplicon data

            We present DADA2, a software package that models and corrects Illumina-sequenced amplicon errors. DADA2 infers sample sequences exactly, without coarse-graining into OTUs, and resolves differences of as little as one nucleotide. In several mock communities DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.
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              Cutadapt removes adapter sequences from high-throughput sequencing reads

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                Author and article information

                Contributors
                steve.taylor@duke.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                10 February 2021
                10 February 2021
                2021
                : 12
                : 909
                Affiliations
                [1 ]GRID grid.410711.2, ISNI 0000 0001 1034 1720, Department of Epidemiology, Gillings School of Global Public Health, , University of North Carolina, ; Chapel Hill, NC USA
                [2 ]GRID grid.26009.3d, ISNI 0000 0004 1936 7961, Division of Infectious Diseases, School of Medicine, , Duke University, ; Durham, NC USA
                [3 ]Academic Model Providing Access to Healthcare, Moi Teaching and Referral Hospital, Eldoret, Kenya
                [4 ]GRID grid.79730.3a, ISNI 0000 0001 0495 4256, School of Medicine, College of Health Sciences, , Moi University, ; Eldoret, Kenya
                [5 ]GRID grid.21107.35, ISNI 0000 0001 2171 9311, Department of Epidemiology, , Johns Hopkins Bloomberg School of Public Health, ; Baltimore, MD USA
                [6 ]GRID grid.79730.3a, ISNI 0000 0001 0495 4256, School of Public Health, College of Health Sciences, , Moi University, ; Eldoret, Kenya
                [7 ]GRID grid.26009.3d, ISNI 0000 0004 1936 7961, Duke Global Health Institute, , Duke University, ; Durham, NC USA
                Author information
                http://orcid.org/0000-0002-4479-897X
                http://orcid.org/0000-0001-6320-3575
                http://orcid.org/0000-0002-2783-0990
                Article
                21269
                10.1038/s41467-021-21269-2
                7875998
                33568678
                68ae2e56-8dc1-4171-888d-ab9f0e8781a9
                © The Author(s) 2021

                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
                : 7 August 2020
                : 15 January 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000060, U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (NIAID);
                Award ID: R21AI126024
                Award ID: R01AI146849
                Award Recipient :
                Funded by: U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (NIAID)
                Funded by: FundRef https://doi.org/10.13039/100000861, Burroughs Wellcome Fund (BWF);
                Award ID: none
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                parasite genetics,malaria,epidemiology
                Uncategorized
                parasite genetics, malaria, epidemiology

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