AmpliVar: Mutation detection in high-throughput sequence from amplicon-based libraries

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

Research output: Contribution to journalArticleResearchpeer-review

6 Citations (Scopus)


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. In this study, AmpliVar was applied to amplicon sequencing data derived from acute myeloid leukemia, breast and ovarian cancer samples. Using a grouped read and quality filtering approach, AmpliVar identified variants with the highest sensitivity and effective false positive reduction. It displayed superior processing speed and was able to be used as an orthogonal means of validation for hybridization capture data.

Original languageEnglish
Pages (from-to)411-418
Number of pages8
JournalHuman Mutation
Issue number4
Publication statusPublished - Apr 2015
Externally publishedYes


  • Amplicon sequencing
  • Grouped reads
  • Mutation detection
  • Next generation sequencing

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