Identification of single nucleotide variants using position-specific error estimation in deep sequencing data

Dimitrios Kleftogiannis,Daniel Wetterskog,Anuradha Jayaram,Shahneen Sandhu,Stefano Lise,Stephen Q Wong,Delila Gasi Tandefelt,Marco Punta,Gerhardt Attard,Vincenza Conteduca

doi:10.1186/s12920-019-0557-9

Abstract

BackgroundTargeted 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).MethodsTo 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.ResultsOur 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.ConclusionsAmpliSolve 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.

Highlights

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
We focus on the Ion Torrent Personal Genome Machine (PGM) and a custom AmpliSeq panel, a technology known to have relatively high rates of sequencing error compared to others
AmpliSolve assigns separate error levels to each genomic position, each alternative allele and each strand. These are utilized to build the Poisson models that are at the core of AmpliSolve SNV calling (Methods)

Summary

Introduction

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. 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). Targeted next-generation sequencing (NGS) is a powerful technology to identify known and novel variants in selected genomic regions of interest [1]. It allows achieving high coverage levels (i.e., higher than 1000x) and, in principle, to confidently identify variants even when they occur at low allele frequencies. There is a need to reliably detect SNVs in more conventional deep sequencing data

Methods

Results

Discussion

Conclusion