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      Strategies for identification of somatic variants using the Ion Torrent deep targeted sequencing platform

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          Abstract

          Background

          ‘Next-generation’ (NGS) sequencing has wide application in medical genetics, including the detection of somatic variation in cancer. The Ion Torrent-based (IONT) platform is among NGS technologies employed in clinical, research and diagnostic settings. However, identifying mutations from IONT deep sequencing with high confidence has remained a challenge. We compared various computational variant-calling methods to derive a variant identification pipeline that may improve the molecular diagnostic and research utility of IONT.

          Results

          Using IONT, we surveyed variants from the 409-gene Comprehensive Cancer Panel in whole-section tumors, intra-tumoral biopsies and matched normal samples obtained from frozen tissues and blood from four early-stage non-small cell lung cancer (NSCLC) patients. We used MuTect, Varscan2, IONT’s proprietary Ion Reporter, and a simple subtraction we called “Poor Man’s Caller.” Together these produced calls at 637 loci across all samples. Visual validation of 434 called variants was performed, and performance of the methods assessed individually and in combination. Of the subset of inspected putative variant calls ( n=223) in genomic regions that were not intronic or intergenic, 68 variants (30%) were deemed valid after visual inspection. Among the individual methods, the Ion Reporter method offered perhaps the most reasonable tradeoffs. Ion Reporter captured 83% of all discovered variants; 50% of its variants were visually validated. Aggregating results from multiple packages offered varied improvements in performance.

          Conclusions

          Overall, Ion Reporter offered the most attractive performance among the individual callers. This study suggests combined strategies to maximize sensitivity and positive predictive value in variant calling using IONT deep sequencing.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12859-017-1991-3) contains supplementary material, which is available to authorized users.

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          Most cited references15

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          An integrated semiconductor device enabling non-optical genome sequencing.

          The seminal importance of DNA sequencing to the life sciences, biotechnology and medicine has driven the search for more scalable and lower-cost solutions. Here we describe a DNA sequencing technology in which scalable, low-cost semiconductor manufacturing techniques are used to make an integrated circuit able to directly perform non-optical DNA sequencing of genomes. Sequence data are obtained by directly sensing the ions produced by template-directed DNA polymerase synthesis using all-natural nucleotides on this massively parallel semiconductor-sensing device or ion chip. The ion chip contains ion-sensitive, field-effect transistor-based sensors in perfect register with 1.2 million wells, which provide confinement and allow parallel, simultaneous detection of independent sequencing reactions. Use of the most widely used technology for constructing integrated circuits, the complementary metal-oxide semiconductor (CMOS) process, allows for low-cost, large-scale production and scaling of the device to higher densities and larger array sizes. We show the performance of the system by sequencing three bacterial genomes, its robustness and scalability by producing ion chips with up to 10 times as many sensors and sequencing a human genome.
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            High-throughput sequencing technologies.

            The human genome sequence has profoundly altered our understanding of biology, human diversity, and disease. The path from the first draft sequence to our nascent era of personal genomes and genomic medicine has been made possible only because of the extraordinary advancements in DNA sequencing technologies over the past 10 years. Here, we discuss commonly used high-throughput sequencing platforms, the growing array of sequencing assays developed around them, as well as the challenges facing current sequencing platforms and their clinical application. Copyright © 2015 Elsevier Inc. All rights reserved.
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              Comparison of Next-Generation Sequencing Systems

              With fast development and wide applications of next-generation sequencing (NGS) technologies, genomic sequence information is within reach to aid the achievement of goals to decode life mysteries, make better crops, detect pathogens, and improve life qualities. NGS systems are typically represented by SOLiD/Ion Torrent PGM from Life Sciences, Genome Analyzer/HiSeq 2000/MiSeq from Illumina, and GS FLX Titanium/GS Junior from Roche. Beijing Genomics Institute (BGI), which possesses the world's biggest sequencing capacity, has multiple NGS systems including 137 HiSeq 2000, 27 SOLiD, one Ion Torrent PGM, one MiSeq, and one 454 sequencer. We have accumulated extensive experience in sample handling, sequencing, and bioinformatics analysis. In this paper, technologies of these systems are reviewed, and first-hand data from extensive experience is summarized and analyzed to discuss the advantages and specifics associated with each sequencing system. At last, applications of NGS are summarized.
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                Author and article information

                Contributors
                asd3002@med.cornell.edu
                wlang@mdanderson.org
                clmcdowell@mdanderson.orgd
                ssivakumar@mdanderson.org
                jiexinzhang@mdanderson.org
                jingwang@mdanderson.org
                fasaan@mdanderson.org
                rgfowler@mdanderson.org
                hk94@aub.edu.lb
                pscheet@alum.wustl.edu
                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central (London )
                1471-2105
                4 January 2018
                4 January 2018
                2018
                : 19
                : 5
                Affiliations
                [1 ]ISNI 0000 0001 2291 4776, GRID grid.240145.6, Departments of Epidemiology, , University of Texas MD Anderson Cancer Center, ; Houston, TX USA
                [2 ]ISNI 0000 0000 9206 2401, GRID grid.267308.8, University of Texas School of Public Health, ; Houston, TX USA
                [3 ]ISNI 0000 0001 2291 4776, GRID grid.240145.6, Translational Molecular Pathology, , University of Texas MD Anderson Cancer Center, ; Houston, TX USA
                [4 ]ISNI 0000 0001 2291 4776, GRID grid.240145.6, Bioinformatics and Computational Biology, , University of Texas MD Anderson Cancer Center, ; Houston, TX USA
                [5 ]ISNI 0000 0000 9206 2401, GRID grid.267308.8, University of Texas Graduate School of Biomedical Sciences, ; Houston, TX USA
                [6 ]ISNI 0000 0004 1936 9801, GRID grid.22903.3a, Department of Biochemistry and Molecular Genetics, Faculty of Medicine, , American University of Beirut, ; Beirut, Lebanon
                Article
                1991
                10.1186/s12859-017-1991-3
                5753459
                29301485
                984c7a7f-b63b-44da-9be1-38e226fd2ad0
                © The Author(s) 2017

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 13 January 2017
                : 6 December 2017
                Funding
                Funded by: Cancer Prevention and Research Institute of Texas (US)
                Award ID: RP150079
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000054, National Cancer Institute;
                Award ID: R01HG005859
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000054, National Cancer Institute;
                Award ID: P30CA016677
                Categories
                Methodology Article
                Custom metadata
                © The Author(s) 2018

                Bioinformatics & Computational biology
                next-generation sequencing,ion torrent,variant calling strategies,ion reporter,varscan2,mutect

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