Evaluation and optimisation of indel detection workflows for ion torrent sequencing of the BRCA1 and BRCA2 genes

Abstract

BackgroundThe Ion Torrent PGM is a popular benchtop sequencer that shows promise in replacing conventional Sanger sequencing as the gold standard for mutation detection. Despite the PGM’s reported high accuracy in calling single nucleotide variations, it tends to generate many false positive calls in detecting insertions and deletions (indels), which may hinder its utility for clinical genetic testing.ResultsRecently, the proprietary analytical workflow for the Ion Torrent sequencer, Torrent Suite (TS), underwent a series of upgrades. We evaluated three major upgrades of TS by calling indels in the BRCA1 and BRCA2 genes. Our analysis revealed that false negative indels could be generated by TS under both default calling parameters and parameters adjusted for maximum sensitivity. However, indel calling with the same data using the open source variant callers, GATK and SAMtools showed that false negatives could be minimised with the use of appropriate bioinformatics analysis. Furthermore, we identified two variant calling measures, Quality-by-Depth (QD) and VARiation of the Width of gaps and inserts (VARW), which substantially reduced false positive indels, including non-homopolymer associated errors without compromising sensitivity. In our best case scenario that involved the TMAP aligner and SAMtools, we achieved 100% sensitivity, 99.99% specificity and 29% False Discovery Rate (FDR) in indel calling from all 23 samples, which is a good performance for mutation screening using PGM.ConclusionsNew versions of TS, BWA and GATK have shown improvements in indel calling sensitivity and specificity over their older counterpart. However, the variant caller of TS exhibits a lower sensitivity than GATK and SAMtools. Our findings demonstrate that although indel calling from PGM sequences may appear to be noisy at first glance, proper computational indel calling analysis is able to maximize both the sensitivity and specificity at the single base level, paving the way for the usage of this technology for future clinical genetic testing.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-516) contains supplementary material, which is available to authorized users.

Development and validation of a variant detection workflow for BRCA1 and BRCA2 genes and its clinical application based on the Ion Torrent technology

“…Even with the optimization of the bioinformatics parameters used in our pipeline, which improved the quality of mapping and variant calling, our in-house pipeline has a high false positive rate (4.3%), which is due mostly to homopolymers regions. This has been previously reported by other authors [12][13][14] and highlights the need for orthogonal confirmations. Having this scenario, we opted for the confirmation of every pathogenic or variant of unknown significance through Sanger sequencing in our clinical analysis test.…”

Creating a universal SNP and small indel variant caller with deep neural networks

“…Generalizing these models to other sequencing technologies has proven difficult due to the need for manual retuning or extending these statistical models (see e.g. Ion Torrent 8,9 ), a major problem in an area with such rapid technological progress 12,13 , and the life sciences [14][15][16][17] . This toolchain, which we call DeepVariant, (Figure 1) begins by finding candidate SNPs and indels in reads aligned to the reference genome with high-sensitivity but low specificity.…”

“…Generalizing these models to other sequencing technologies has proven difficult due to the need for manual retuning or extending these statistical models (see e.g. Ion Torrent 8,9 ), a major problem in an area with such rapid technological progress 1 .…”

Altered molecular profile in thyroid cancers from patients affected by the Three Mile Island nuclear accident

“…Indels were called using a modification of the Genome Analysis ToolKit UnifiedGenotyper tool within the Ampliseq Tumor/Normal workflow that identifies candidate indels present at 10 × coverage or higher. This algorithm accounts for the homopolymer‐induced indels intrinsic to Ion Torrent sequencing data . Large indels are identified by scanning Binary Alignment/Map (BAM) files for > 10 bp of nonaligned sequences.…”