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

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.

[1]  Gonçalo R. Abecasis,et al.  The Sequence Alignment/Map format and SAMtools , 2009, Bioinform..

[2]  Monya Baker,et al.  Next-generation sequencing: adjusting to data overload , 2010, Nature Methods.

[3]  Helga Thorvaldsdóttir,et al.  Integrative Genomics Viewer , 2011, Nature Biotechnology.

[4]  Zhen Xuan Yeo,et al.  Improving Indel Detection Specificity of the Ion Torrent PGM Benchtop Sequencer , 2012, PloS one.

[5]  H. Swerdlow,et al.  A tale of three next generation sequencing platforms: comparison of Ion Torrent, Pacific Biosciences and Illumina MiSeq sequencers , 2012, BMC Genomics.

[6]  Puay Hoon Tan,et al.  Development of a next-generation sequencing method for BRCA mutation screening: a comparison between a high-throughput and a benchtop platform. , 2012, The Journal of molecular diagnostics : JMD.

[7]  Kathleen M Murphy,et al.  Comparison of Sanger sequencing, pyrosequencing, and melting curve analysis for the detection of KRAS mutations: diagnostic and clinical implications. , 2010, The Journal of molecular diagnostics : JMD.

[8]  Richard Durbin,et al.  Sequence analysis Fast and accurate short read alignment with Burrows – Wheeler transform , 2009 .

[9]  M. DePristo,et al.  A framework for variation discovery and genotyping using next-generation DNA sequencing data , 2011, Nature Genetics.

[10]  S. Cook,et al.  Towards Clinical Molecular Diagnosis of Inherited Cardiac Conditions: A Comparison of Bench-Top Genome DNA Sequencers , 2013, PloS one.

[11]  Teresa Kay,et al.  Nonoptical Massive Parallel DNA Sequencing of BRCA1 and BRCA2 Genes in a Diagnostic Setting , 2013, Human mutation.

[12]  P. Ang,et al.  Evaluation of nanofluidics technology for high-throughput SNP genotyping in a clinical setting. , 2011, The Journal of molecular diagnostics : JMD.

[13]  Yuri E Nikiforov,et al.  Pulmonary Langerhans cell histiocytosis: profiling of multifocal tumors using next-generation sequencing identifies concordant occurrence of BRAF V600E mutations. , 2013, Chest.

[14]  Jens Stoye,et al.  Updating benchtop sequencing performance comparison , 2013, Nature Biotechnology.

[15]  Bernard P. Puc,et al.  An integrated semiconductor device enabling non-optical genome sequencing , 2011, Nature.

[16]  Timothy B. Stockwell,et al.  Evaluation of next generation sequencing platforms for population targeted sequencing studies , 2009, Genome Biology.

[17]  Helga Thorvaldsdóttir,et al.  Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration , 2012, Briefings Bioinform..

[18]  T. Dallman,et al.  Performance comparison of benchtop high-throughput sequencing platforms , 2012, Nature Biotechnology.

[19]  Ashish Choudhary,et al.  Targeted, high-depth, next-generation sequencing of cancer genes in formalin-fixed, paraffin-embedded and fine-needle aspiration tumor specimens. , 2013, The Journal of molecular diagnostics : JMD.

[20]  A. Elliott,et al.  Rapid detection of the ACMG/ACOG-recommended 23 CFTR disease-causing mutations using ion torrent semiconductor sequencing. , 2012, Journal of biomolecular techniques : JBT.

[21]  Madhuri Hegde,et al.  Assessment of clinical analytical sensitivity and specificity of next-generation sequencing for detection of simple and complex mutations , 2013, BMC Genetics.

[22]  Yingrui Li,et al.  Estimation of allele frequency and association mapping using next-generation sequencing data , 2011, BMC Bioinformatics.

[23]  R. Durbin,et al.  Mapping Quality Scores Mapping Short Dna Sequencing Reads and Calling Variants Using P

, 2022 .

[24]  D. Harmsen,et al.  Ion Torrent Personal Genome Machine Sequencing for Genomic Typing of Neisseria meningitidis for Rapid Determination of Multiple Layers of Typing Information , 2012, Journal of Clinical Microbiology.

[25]  Philip Hugenholtz,et al.  Shining a Light on Dark Sequencing: Characterising Errors in Ion Torrent PGM Data , 2013, PLoS Comput. Biol..