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      The Genetic Diversity of Influenza A Viruses in Wild Birds in Peru

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          Our understanding of the global ecology of avian influenza A viruses (AIVs) is impeded by historically low levels of viral surveillance in Latin America. Through sampling and whole-genome sequencing of 31 AIVs from wild birds in Peru, we identified 10 HA subtypes (H1-H4, H6-H7, H10-H13) and 8 NA subtypes (N1-N3, N5-N9). The majority of Peruvian AIVs were closely related to AIVs found in North America. However, unusual reassortants, including a H13 virus containing a PA segment related to extremely divergent Argentinian viruses, suggest that substantial AIV diversity circulates undetected throughout South America.

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          Most cited references 17

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          MUSCLE: multiple sequence alignment with high accuracy and high throughput.

           Robert Edgar (2004)
          We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
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            Universal primer set for the full-length amplification of all influenza A viruses.

             E. Hoffmann,  J Stech,  Y Guan (2001)
            To systematically identify and analyze the 15 HA and 9 NA subtypes of influenza A virus, we need reliable, simple methods that not only characterize partial sequences but analyze the entire influenza A genome. We designed primers based on the fact that the 15 and 21 terminal segment specific nucleotides of the genomic viral RNA are conserved between all influenza A viruses and unique for each segment. The primers designed for each segment contain influenza virus specific nucleotides at their 3'-end and non-influenza virus nucleotides at the 5'-end. With this set of primers, we were able to amplify all eight segments of N1, N2, N4, N5, and N8 subtypes. For N3, N6, N7, and N9 subtypes, the segment specific sequences of the neuraminidase genes are different. Therefore, we optimized the primer design to allow the amplification of those neuraminidase genes as well. The resultant primer set is suitable for all influenza A viruses to generate full-length cDNAs, to subtype viruses, to sequence their DNA, and to construct expression plasmids for reverse genetics systems.
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              RAxML-III: a fast program for maximum likelihood-based inference of large phylogenetic trees.

              The computation of large phylogenetic trees with statistical models such as maximum likelihood or bayesian inference is computationally extremely intensive. It has repeatedly been demonstrated that these models are able to recover the true tree or a tree which is topologically closer to the true tree more frequently than less elaborate methods such as parsimony or neighbor joining. Due to the combinatorial and computational complexity the size of trees which can be computed on a Biologist's PC workstation within reasonable time is limited to trees containing approximately 100 taxa. In this paper we present the latest release of our program RAxML-III for rapid maximum likelihood-based inference of large evolutionary trees which allows for computation of 1.000-taxon trees in less than 24 hours on a single PC processor. We compare RAxML-III to the currently fastest implementations for maximum likelihood and bayesian inference: PHYML and MrBayes. Whereas RAxML-III performs worse than PHYML and MrBayes on synthetic data it clearly outperforms both programs on all real data alignments used in terms of speed and final likelihood values. Availability RAxML-III including all alignments and final trees mentioned in this paper is freely available as open source code at http://wwwbode.cs.tum/~stamatak

                Author and article information

                Role: Editor
                PLoS One
                PLoS ONE
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                19 January 2016
                : 11
                : 1
                [1 ]Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
                [2 ]Virology and Emerging Infections Department, United States Naval Medical Research Unit No. 6, Callao, Peru
                [3 ]Marie Bashir Institute for Infectious Diseases and Biosecurity, University of Sydney, New South Wales, Australia
                [4 ]Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, United States of America
                [5 ]Universidad Nacional Mayor de San Marcos, School of Veterinary Medicine, San Borja, Lima, Peru
                [6 ]Tulane School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States of America
                Linneaus University, SWEDEN
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: MN BG EI AG MK DB JM. Performed the experiments: MN BG MPS KS. Analyzed the data: MN SP. Contributed reagents/materials/analysis tools: MS KS. Wrote the paper: MN BG EI AG MK DB JM MS MPS KS.


                This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

                Page count
                Figures: 2, Tables: 0, Pages: 10
                This work was supported by Global Emerging Infections Surveillance (GEIS) Work unit no.: 847705 82000 25GB B0016. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Research Article
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                All analytic data are available via NCBI GenBank (see S1, S3, S4 Tables for accession numbers).



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