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AmpliVar: Mutation Detection in High‐Throughput Sequence from Amplicon‐Based Libraries
Authors:Arthur L. Hsu  Olga Kondrashova  Sebastian Lunke  Clare J. Love  Cliff Meldrum  Renate Marquis‐Nicholson  Greg Corboy  Kym Pham  Matthew Wakefield  Paul M. Waring  Graham R. Taylor
Affiliation:1. Department of Pathology, The University of Melbourne, Victoria, Australia;2. Molecular Genetics Laboratory, Pathology North, New South Wales, Australia;3. Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Victoria, Australia
Abstract:Conventional means of identifying variants in high‐throughput sequencing align each read against a reference sequence, and then call variants at each position. Here, we demonstrate an orthogonal means of identifying sequence variation by grouping the reads as amplicons prior to any alignment. We used AmpliVar to make key‐value hashes of sequence reads and group reads as individual amplicons using a table of flanking sequences. Low‐abundance reads were removed according to a selectable threshold, and reads above this threshold were aligned as groups, rather than as individual reads, permitting the use of sensitive alignment tools. We show that this approach is more sensitive, more specific, and more computationally efficient than comparable methods for the analysis of amplicon‐based high‐throughput sequencing data. The method can be extended to enable alignment‐free confirmation of variants seen in hybridization capture target‐enrichment data.
Keywords:next generation sequencing  amplicon sequencing  mutation detection  grouped reads
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