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    PLoS One. Published online May Received Feb 5; Accepted Apr This article has been cited by other articles in PMC. Abstract Background The advent of next-generation sequencing technologies and the resultant lower costs of sequencing have enabled production of massive amounts of data, including the generation of full genome sequences of pathogens. However, the small genome size of the influenza virus arguably justifies the use of the more conventional Sanger sequencing technology which is still currently more readily available in most diagnostic laboratories. The entire genome sequencing was completed with 19 reverse transcription-polymerase chain reactions RT-PCRs and 39 sequencing reactions. This method was tested on 15 native clinical samples and 15 culture isolates, respectively, collected between and The 15 native clinical samples registered quantification cycle values ranging from Notably, high quality sequencing data up to bp were generated for all the samples tested. Of all the bases produced, an average of Conclusions This sequencing protocol has been shown to be cost-effective and less labor-intensive in obtaining full influenza genomes. The constant high quality of sequences generated imparts confidence in extending the application of this non-purified amplicon sequencing approach to other gene sequencing assays, with appropriate use of suitably designed primers.

    The genome of the influenza A virus family Orthomyxoviridae consists of eight segmented, negative-stranded RNAs, ranging from to 2, nucleotides nt , constituting 13, nt per genome.

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    The introduction of next-generation sequencing NGS , which delivers high throughput readings [11] compared to the traditional Sanger dideoxy chain-termination method [12] , has provided a remarkable cost reduction for microbial genome sequencing. However, a higher error rate due to homopolymeric miscalling and other systematic base-calling biases have been observed in NGS techniques, compared with the Sanger methods [13] — [16].

    The Sanger technique is regarded to be low throughput and more tedious, due to the requirement of multiple purification or plasmid cloning steps [4] , [8] , [19] — [23].

    This protocol, when applied systematically, should hasten the routine genome sequencing work for local influenza surveillance studies.

    It was also demonstrated that this protocol is highly applicable for both clinical samples and Madin-Darby canine kidney- MDCK- cultured samples. Fifteen of the 30 were sequenced from cultured isolates of the original clinical sample using a MDCK. Primer Design To ensure the utility of the assay for the sequencing of older as well as future circulating strains, two reference gene sequences were randomly chosen per month from depositions from different countries and dates of collection to available at the NCBI Influenza Virus Resource.

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    Primer target regions for RT-PCRs for the different gene segments were selected from the conserved regions of the respective aligned gene sequences. The space is enriched by antiques and traditional artefacts and in the back of the pavilion, the villa's chef does magic in the professional kitchen. Villa Avalon 1 also features an air-conditioned media room with a large library of books, comfy sofas and a state-of-the-art TV set for cosy in-villa movie evenings.

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    The corresponding sample mean is What is the value of the appropriate test statistic for the test of the population mean, and what is the correct decision at the 1 percent significance level? The denominator of the z-test statistic is standard error instead of standard deviation.

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    If the denominator takes the value of standard deviation 4. Therefore, reject the null hypothesis because the computed test statistics of 2. Gujarati, Essentials of Econometrics, 3rd ed. It becomes more difficult to reject a null hypothesis when it is actually true. The probability of making a type I error increases. The probability of making a type II error decreases.

    Answer: a Explanation: Type I error: The rejection of the null hypothesis when it is actually true. Type II error: The failure to reject the null hypothesis when it is actually false. The significance level is the probability of making a type I error.

    Decreasing the probability level makes it more difficult to reject the null when it is true. Decreases the probability of making a type I error.