RT Journal Article
SR Electronic
T1 Identification of single nucleotide variants using position-specific error estimation in deep sequencing data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 475947
DO 10.1101/475947
A1 Dimitrios Kleftogiannis
A1 Marco Punta
A1 Anuradha Jayaram
A1 Shahneen Sandhu
A1 Stephen Q. Wong
A1 Delila Gasi Tandefelt
A1 Vincenza Conteduca
A1 Daniel Wetterskog
A1 Gerhardt Attard
A1 Stefano Lise
YR 2019
UL http://biorxiv.org/content/early/2019/07/24/475947.abstract
AB Background Targeted deep sequencing is a highly effective technology to identify known and novel single nucleotide variants (SNVs) with many applications in translational medicine, disease monitoring and cancer profiling. However, identification of SNVs using deep sequencing data is a challenging computational problem as different sequencing artifacts limit the analytical sensitivity of SNV detection, especially at low variant allele frequencies (VAFs).Methods To address the problem of relatively high noise levels in amplicon-based deep sequencing data (e.g. with the Ion AmpliSeq technology) in the context of SNV calling, we have developed a new bioinformatics tool called AmpliSolve. AmpliSolve uses a set of normal samples to model position-specific, strand-specific and nucleotide-specific background artifacts (noise), and deploys a Poisson model-based statistical framework for SNV detection.Results Our tests on both synthetic and real data indicate that AmpliSolve achieves a good trade-off between precision and sensitivity, even at VAF below 5% and as low as 1%. We further validate AmpliSolve by applying it to the detection of SNVs in 96 circulating tumor DNA samples at three clinically relevant genomic positions and compare the results to digital droplet PCR experiments.Conclusions AmpliSolve is a new tool for in-silico estimation of background noise and for detection of low frequency SNVs in targeted deep sequencing data. Although AmpliSolve has been specifically designed for and tested on amplicon-based libraries sequenced with the Ion Torrent platform it can, in principle, be applied to other sequencing platforms as well. AmpliSolve is freely available at https://github.com/dkleftogi/AmpliSolve.