Comparison of somatic variant detection algorithms using Ion Torrent targeted deep sequencing data

Qing Wang,Pei-Chen Hsu,Eleni Giannoulatou,Vassiliki Kotoula,Kyriaki Papadopoulou,George Fountzilas,Joshua W K Ho

doi:10.1186/s12920-019-0636-y

Abstract

BackgroundThe application of next-generation sequencing in cancer has revealed the genomic landscape of many tumour types and is nowadays routinely used in research and clinical settings. Multiple algorithms have been developed to detect somatic variation from sequencing data using either paired tumour-blood or tumour-only samples. Most of these methods have been developed and evaluated for the identification of somatic variation using Illumina sequencing datasets of moderate coverage. However, a comprehensive evaluation of somatic variant detection algorithms on Ion Torrent targeted deep sequencing data has not been performed.MethodsWe have applied three somatic detection algorithms, Torrent Variant Caller, MuTect2 and VarScan2, on a large cohort of ovarian cancer patients comprising of 208 paired tumour-blood samples and 253 tumour-only samples sequenced deeply on Ion Torrent Proton platform across 330 amplicons. Subsequently, the concordance and performance of the three somatic variant callers were assessed.ResultsWe have observed low concordance across the algorithms with only 0.5% of SNV and 0.02% of INDEL calls in common across all three methods. The intersection of all methods showed better performance when assessed using correlation with known mutational signatures, overlap with COSMIC variation and by examining the variant characteristics. The Torrent Variant Caller also performed well with the advantage of not eliminating a high number of variants that could lead to high type II error.ConclusionsOur results suggest that caution should be taken when applying state-of-the-art somatic variant algorithms to Ion Torrent targeted deep sequencing data. Better quality control procedures and strategies that combine results from multiple methods should ensure that higher accuracy is achieved. This is essential to ensure that results from bioinformatics pipelines using Ion Torrent deep sequencing can be robustly applied in cancer research and in the clinic.

Highlights

The application of next-generation sequencing in cancer has revealed the genomic landscape of many tumour types and is nowadays routinely used in research and clinical settings
Distribution of variant allele frequency (VAF) and read depth of single nucleotide variants (SNVs) To further compare the performance of the variant callers, we investigated the relationship between variant allele frequency (VAF) and read depth for each set of algorithm-specific SNVs as well as the common SNVs across two or all three callers in the 208 paired tumourblood samples (Fig. 2)
The patterns of somatic variants called by different variant callers were described by mutational signatures according to 96 possible substitution types defined by the bases 5′ and 3′ to the mutated base [30]

Summary

Introduction

The application of next-generation sequencing in cancer has revealed the genomic landscape of many tumour types and is nowadays routinely used in research and clinical settings. Multiple algorithms have been developed to detect somatic variation from sequencing data using either paired tumour-blood or tumour-only samples. Dividing progenitor cells for tissue renewal are prone to the acquisition of somatic variants [5], i.e., single nucleotide variants (SNVs) and insertions/deletions (INDELs), as well as large structural variants and complex rearrangements. Accumulation of such variants in somatic cells, especially combinations with variants of deleterious potential for the function of key molecular pathways, may lead to the development of cancer [6]. The most common strategy for the detection of somatic variation is through the application of nextgeneration sequencing (NGS) that allows an efficient, high-throughput characterisation of all types of genomic variation [7]

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