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A mpli V ar: Mutation Detection in High‐Throughput Sequence from Amplicon‐Based Libraries
Author(s) -
Hsu Arthur L.,
Kondrashova Olga,
Lunke Sebastian,
Love Clare J.,
Meldrum Cliff,
MarquisNicholson Renate,
Corboy Greg,
Pham Kym,
Wakefield Matthew,
Waring Paul M.,
Taylor Graham R.
Publication year - 2015
Publication title -
human mutation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.981
H-Index - 162
eISSN - 1098-1004
pISSN - 1059-7794
DOI - 10.1002/humu.22763
Subject(s) - amplicon , biology , sequence (biology) , computational biology , dna sequencing , genetics , contig , massive parallel sequencing , computer science , polymerase chain reaction , gene , genome
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 A mpli V ar 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.