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From Benchtop to Desktop: Important Considerations when Designing Amplicon Sequencing Workflows

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PLoS ONE

Public Library of Science

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      Abstract

      Amplicon sequencing has been the method of choice in many high-throughput DNA sequencing (HTS) applications. To date there has been a heavy focus on the means by which to analyse the burgeoning amount of data afforded by HTS. In contrast, there has been a distinct lack of attention paid to considerations surrounding the importance of sample preparation and the fidelity of library generation. No amount of high-end bioinformatics can compensate for poorly prepared samples and it is therefore imperative that careful attention is given to sample preparation and library generation within workflows, especially those involving multiple PCR steps. This paper redresses this imbalance by focusing on aspects pertaining to the benchtop within typical amplicon workflows: sample screening, the target region, and library generation. Empirical data is provided to illustrate the scope of the problem. Lastly, the impact of various data analysis parameters is also investigated in the context of how the data was initially generated. It is hoped this paper may serve to highlight the importance of pre-analysis workflows in achieving meaningful, future-proof data that can be analysed appropriately. As amplicon sequencing gains traction in a variety of diagnostic applications from forensics to environmental DNA (eDNA) it is paramount workflows and analytics are both fit for purpose.

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      Most cited references 74

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      A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.
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        MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods.

        Comparative analysis of molecular sequence data is essential for reconstructing the evolutionary histories of species and inferring the nature and extent of selective forces shaping the evolution of genes and species. Here, we announce the release of Molecular Evolutionary Genetics Analysis version 5 (MEGA5), which is a user-friendly software for mining online databases, building sequence alignments and phylogenetic trees, and using methods of evolutionary bioinformatics in basic biology, biomedicine, and evolution. The newest addition in MEGA5 is a collection of maximum likelihood (ML) analyses for inferring evolutionary trees, selecting best-fit substitution models (nucleotide or amino acid), inferring ancestral states and sequences (along with probabilities), and estimating evolutionary rates site-by-site. In computer simulation analyses, ML tree inference algorithms in MEGA5 compared favorably with other software packages in terms of computational efficiency and the accuracy of the estimates of phylogenetic trees, substitution parameters, and rate variation among sites. The MEGA user interface has now been enhanced to be activity driven to make it easier for the use of both beginners and experienced scientists. This version of MEGA is intended for the Windows platform, and it has been configured for effective use on Mac OS X and Linux desktops. It is available free of charge from http://www.megasoftware.net.
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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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            Author and article information

            Affiliations
            Trace and Environmental DNA Laboratory, Department of Environment and Agriculture, Curtin University, Perth, Western Australia, Australia
            Natural History Museum of Denmark, DENMARK
            Author notes

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

            Conceived and designed the experiments: DCM MB. Performed the experiments: DCM MLC. Analyzed the data: DCM. Contributed reagents/materials/analysis tools: MB. Wrote the paper: DCM MB. Edited the manuscript: DCM MB MLC.

            Contributors
            Role: Academic Editor
            Journal
            PLoS One
            PLoS ONE
            plos
            plosone
            PLoS ONE
            Public Library of Science (San Francisco, CA USA )
            1932-6203
            22 April 2015
            2015
            : 10
            : 4
            25902146
            4406758
            10.1371/journal.pone.0124671
            PONE-D-14-48621
            (Academic Editor)

            This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

            Counts
            Figures: 4, Tables: 1, Pages: 21
            Product
            Funding
            MB was funded from the Australian Research Council grant FT0991741 and DP120103725. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
            Categories
            Research Article
            Custom metadata
            All relevant data are within the paper, Supporting Information files or are available from Data Dryad (doi: 10.5061/dryad.2qf0t).

            Uncategorized

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