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      Quantifying influenza virus diversity and transmission in humans

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          Abstract

          Influenza A virus is characterized by high genetic diversity. 13 However, most of what we know about influenza evolution has come from consensus sequences sampled at the epidemiological scale 4 that only represent the dominant virus lineage within each infected host. Less is known about the extent of intra-host virus diversity and what proportion is transmitted between individuals. 5 To characterize those virus variants that achieve sustainable transmission in new hosts, we examined intra-host virus genetic diversity within household donor/recipient pairs from the first wave of the 2009 H1N1 pandemic when seasonal H3N2 was co-circulating. While the same variants were found in multiple members of the community, the relative frequencies of variants fluctuated, with patterns of genetic variation more similar within than between households. We estimated the effective population size of influenza A virus across donor/recipient pairs to be approximately 100–200 contributing members, which enabled the transmission of multiple lineages including antigenic variants.

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          Most cited references19

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          Is Open Access

          Sequence-specific error profile of Illumina sequencers

          We identified the sequence-specific starting positions of consecutive miscalls in the mapping of reads obtained from the Illumina Genome Analyser (GA). Detailed analysis of the miscall pattern indicated that the underlying mechanism involves sequence-specific interference of the base elongation process during sequencing. The two major sequence patterns that trigger this sequence-specific error (SSE) are: (i) inverted repeats and (ii) GGC sequences. We speculate that these sequences favor dephasing by inhibiting single-base elongation, by: (i) folding single-stranded DNA and (ii) altering enzyme preference. This phenomenon is a major cause of sequence coverage variability and of the unfavorable bias observed for population-targeted methods such as RNA-seq and ChIP-seq. Moreover, SSE is a potential cause of false single-nucleotide polymorphism (SNP) calls and also significantly hinders de novo assembly. This article highlights the importance of recognizing SSE and its underlying mechanisms in the hope of enhancing the potential usefulness of the Illumina sequencers.
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            Comparative epidemiology of pandemic and seasonal influenza A in households.

            There are few data on the comparative epidemiology and virology of the pandemic 2009 influenza A (H1N1) virus and cocirculating seasonal influenza A viruses in community settings. We recruited 348 index patients with acute respiratory illness from 14 outpatient clinics in Hong Kong in July and August 2009. We then prospectively followed household members of 99 patients who tested positive for influenza A virus on rapid diagnostic testing. We collected nasal and throat swabs from all household members at three home visits within 7 days for testing by means of quantitative reverse-transcriptase-polymerase-chain-reaction (RT-PCR) assay and viral culture. Using hemagglutination-inhibition and viral-neutralization assays, we tested baseline and convalescent serum samples from a subgroup of patients for antibody responses to the pandemic and seasonal influenza A viruses. Secondary attack rates (as confirmed on RT-PCR assay) among household contacts of index patients were similar for the pandemic influenza virus (8%; 95% confidence interval [CI], 3 to 14) and seasonal influenza viruses (9%; 95% CI, 5 to 15). The patterns of viral shedding and the course of illness among index patients were also similar for the pandemic and seasonal influenza viruses. In a subgroup of patients for whom baseline and convalescent serum samples were available, 36% of household contacts who had serologic evidence of pandemic influenza virus infection did not shed detectable virus or report illness. Pandemic 2009 H1N1 virus has characteristics that are broadly similar to those of seasonal influenza A viruses in terms of rates of viral shedding, clinical illness, and transmissibility in the household setting. Copyright 2010 Massachusetts Medical Society.
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              Mutation rates among RNA viruses.

              The rate of spontaneous mutation is a key parameter in modeling the genetic structure and evolution of populations. The impact of the accumulated load of mutations and the consequences of increasing the mutation rate are important in assessing the genetic health of populations. Mutation frequencies are among the more directly measurable population parameters, although the information needed to convert them into mutation rates is often lacking. A previous analysis of mutation rates in RNA viruses (specifically in riboviruses rather than retroviruses) was constrained by the quality and quantity of available measurements and by the lack of a specific theoretical framework for converting mutation frequencies into mutation rates in this group of organisms. Here, we describe a simple relation between ribovirus mutation frequencies and mutation rates, apply it to the best (albeit far from satisfactory) available data, and observe a central value for the mutation rate per genome per replication of micro(g) approximately 0.76. (The rate per round of cell infection is twice this value or about 1.5.) This value is so large, and ribovirus genomes are so informationally dense, that even a modest increase extinguishes the population.
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                Author and article information

                Journal
                9216904
                2419
                Nat Genet
                Nat. Genet.
                Nature genetics
                1061-4036
                1546-1718
                14 January 2016
                04 January 2016
                February 2016
                04 July 2016
                : 48
                : 2
                : 195-200
                Affiliations
                [1 ]Public Health Laboratory Sciences, School of Public Health, The University of Hong Kong, Hong Kong, China
                [2 ]Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
                [3 ]Center for Genomics and Systems Biology, Department of Biology, New York University, New York, USA
                [4 ]Computer Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
                [5 ]J. Craig Venter Institute, Rockville, Maryland, USA
                [6 ]Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
                [7 ]Pittsburgh Supercomputer Center, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
                [8 ]Marie Bashir Institute for Infectious Diseases and Biosecurity, Charles Perkins Centre, School of Biological Sciences, The University of Sydney, Sydney, NSW, Australia
                [9 ]Sydney Medical School, The University of Sydney, Sydney, NSW, Australia
                [10 ]Tisch Cancer Institute, Departments of Medicine, Hematology and Medical Oncology, and Pathology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
                [11 ]Epidemiology and Biostatistics, School of Public Health, The University of Hong Kong, Hong Kong
                [12 ]College of Global Public Health, New York University, New York, New York, USA
                Author notes
                B.J.C. and E.G. are co-corresponding authors: Correspondence and requests for materials should be addressed to Elodie Ghedin elodie.ghedin@ 123456nyu.edu or Benjamin J. Cowling bcowling@ 123456hku.hk
                [13]

                Present addresses: Children’s Hospital of Pittsburgh, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA (M.B.R.); Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA (D.E.W.).

                L.L.M.P. and T. S. contributed equally to this work.

                Article
                NIHMS743429
                10.1038/ng.3479
                4731279
                26727660
                f19bd126-5c9d-4d7a-ba18-a1105d80ab15

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                Categories
                Article

                Genetics
                influenza a virus,evolution,diversity,virus transmission,next generation sequencing
                Genetics
                influenza a virus, evolution, diversity, virus transmission, next generation sequencing

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