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      Real-time Metagenomic Analysis of Undiagnosed Fever Cases Unveils a Yellow Fever Outbreak in Edo State, Nigeria

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          Abstract

          Fifty patients with unexplained fever and poor outcomes presented at Irrua Specialist Teaching Hospital (ISTH) in Edo State, Nigeria, an area endemic for Lassa fever, between September 2018 - January 2019. After ruling out Lassa fever, plasma samples from these epidemiologically-linked cases were sent to the African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer’s University, Ede, Osun State, Nigeria, where we carried out metagenomic sequencing which implicated yellow fever virus (YFV) as the etiology of this outbreak. Twenty-nine of the 50 samples were confirmed positive for YFV by reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR), 14 of which resulted in genome assembly. Maximum likelihood phylogenetic analysis revealed that these YFV sequences formed a tightly clustered clade more closely related to sequences from Senegal than sequences from earlier Nigerian isolates, suggesting that the YFV clade responsible for this outbreak in Edo State does not descend directly from the Nigerian YFV outbreaks of the last century, but instead reflects a broader diversity and dynamics of YFV in West Africa. Here we demonstrate the power of metagenomic sequencing for identifying ongoing outbreaks and their etiologies and informing real-time public health responses, resulting in accurate and prompt disease management and control.

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          Many-core algorithms for statistical phylogenetics.

          Statistical phylogenetics is computationally intensive, resulting in considerable attention meted on techniques for parallelization. Codon-based models allow for independent rates of synonymous and replacement substitutions and have the potential to more adequately model the process of protein-coding sequence evolution with a resulting increase in phylogenetic accuracy. Unfortunately, due to the high number of codon states, computational burden has largely thwarted phylogenetic reconstruction under codon models, particularly at the genomic-scale. Here, we describe novel algorithms and methods for evaluating phylogenies under arbitrary molecular evolutionary models on graphics processing units (GPUs), making use of the large number of processing cores to efficiently parallelize calculations even for large state-size models. We implement the approach in an existing Bayesian framework and apply the algorithms to estimating the phylogeny of 62 complete mitochondrial genomes of carnivores under a 60-state codon model. We see a near 90-fold speed increase over an optimized CPU-based computation and a >140-fold increase over the currently available implementation, making this the first practical use of codon models for phylogenetic inference over whole mitochondrial or microorganism genomes. Source code provided in BEAGLE: Broad-platform Evolutionary Analysis General Likelihood Evaluator, a cross-platform/processor library for phylogenetic likelihood computation (http://beagle-lib.googlecode.com/). We employ a BEAGLE-implementation using the Bayesian phylogenetics framework BEAST (http://beast.bio.ed.ac.uk/).
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            The reemergence of yellow fever

            Since 2016, yellow fever has become a major public health concern
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              Unbiased Deep Sequencing of RNA Viruses from Clinical Samples

              Here we outline a next-generation RNA sequencing protocol that enables de novo assemblies and intra-host variant calls of viral genomes collected from clinical and biological sources. The method is unbiased and universal; it uses random primers for cDNA synthesis and requires no prior knowledge of the viral sequence content. Before library construction, selective RNase H-based digestion is used to deplete unwanted RNA — including poly(rA) carrier and ribosomal RNA — from the viral RNA sample. Selective depletion improves both the data quality and the number of unique reads in viral RNA sequencing libraries. Moreover, a transposase-based 'tagmentation' step is used in the protocol as it reduces overall library construction time. The protocol has enabled rapid deep sequencing of over 600 Lassa and Ebola virus samples-including collections from both blood and tissue isolates-and is broadly applicable to other microbial genomics studies.
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                Author and article information

                Contributors
                happic@run.edu.ng
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                21 February 2020
                21 February 2020
                2020
                : 10
                : 3180
                Affiliations
                [1 ]GRID grid.442553.1, African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer’s University, ; Ede, Osun State Nigeria
                [2 ]GRID grid.442553.1, Department of Biological Sciences, Faculty of Natural Sciences, Redeemer’s University, ; Ede, Osun State Nigeria
                [3 ]GRID grid.66859.34, Broad Institute of MIT and Harvard, ; Cambridge, Massachusetts USA
                [4 ]ISNI 0000 0000 9011 8547, GRID grid.239395.7, Beth Israel Deaconess Medical Center, Division of Infectious Diseases, ; Boston, Massachusetts USA
                [5 ]ISNI 000000041936754X, GRID grid.38142.3c, Center for Systems Biology, Department of Organismic and Evolutionary Biology, Harvard University, ; Cambridge, Massachusetts USA
                [6 ]Institute of Lassa Fever Research and Control, Irrua Specialist Teaching Hospital, Irrua, Edo State Nigeria
                [7 ]ISNI 000000041936754X, GRID grid.38142.3c, Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Harvard University, ; Boston, Massachusetts USA
                [8 ]ISNI 0000 0001 0036 4726, GRID grid.420210.5, Emerging Infectious Diseases Branch, Walter Reed Army Institute of Research, ; Silver Spring, MD USA
                [9 ]Nigeria Center for Disease Control, Abuja, Nigeria
                [10 ]ISNI 0000 0001 2167 1581, GRID grid.413575.1, Howard Hughes Medical Institute, ; Chevy Chase, Maryland USA
                Article
                59880
                10.1038/s41598-020-59880-w
                7035389
                32081931
                ff4b5619-73f7-4b67-938e-1633b6971a8d
                © The Author(s) 2020

                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
                : 15 November 2019
                : 3 February 2020
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                © The Author(s) 2020

                Uncategorized
                infectious-disease diagnostics,pathogens,phylogeny
                Uncategorized
                infectious-disease diagnostics, pathogens, phylogeny

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