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      appreci8: a pipeline for precise variant calling integrating 8 tools

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          Abstract

          Motivation

          The application of next-generation sequencing in research and particularly in clinical routine requires valid variant calling results. However, evaluation of several commonly used tools has pointed out that not a single tool meets this requirement. False positive as well as false negative calls necessitate additional experiments and extensive manual work. Intelligent combination and output filtration of different tools could significantly improve the current situation.

          Results

          We developed appreci8, an automatic variant calling pipeline for calling single nucleotide variants and short indels by combining and filtering the output of eight open-source variant calling tools, based on a novel artifact- and polymorphism score. Appreci8 was trained on two data sets from patients with myelodysplastic syndrome, covering 165 Illumina samples. Subsequently, appreci8’s performance was tested on five independent data sets, covering 513 samples. Variation in sequencing platform, target region and disease entity was considered. All calls were validated by re-sequencing on the same platform, a different platform or expert-based review. Sensitivity of appreci8 ranged between 0.93 and 1.00, while positive predictive value ranged between 0.65 and 1.00. In all cases, appreci8 showed superior performance compared to any evaluated alternative approach.

          Availability and implementation

          Appreci8 is freely available at https://hub.docker.com/r/wwuimi/appreci8/. Sequencing data (BAM files) of the 678 patients analyzed with appreci8 have been deposited into the NCBI Sequence Read Archive (BioProjectID: 388411; https://www.ncbi.nlm.nih.gov/bioproject/PRJNA388411).

          Supplementary information

          Supplementary data are available at Bioinformatics online.

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          Most cited references17

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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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            Performance comparison of benchtop high-throughput sequencing platforms.

            Three benchtop high-throughput sequencing instruments are now available. The 454 GS Junior (Roche), MiSeq (Illumina) and Ion Torrent PGM (Life Technologies) are laser-printer sized and offer modest set-up and running costs. Each instrument can generate data required for a draft bacterial genome sequence in days, making them attractive for identifying and characterizing pathogens in the clinical setting. We compared the performance of these instruments by sequencing an isolate of Escherichia coli O104:H4, which caused an outbreak of food poisoning in Germany in 2011. The MiSeq had the highest throughput per run (1.6 Gb/run, 60 Mb/h) and lowest error rates. The 454 GS Junior generated the longest reads (up to 600 bases) and most contiguous assemblies but had the lowest throughput (70 Mb/run, 9 Mb/h). Run in 100-bp mode, the Ion Torrent PGM had the highest throughput (80–100 Mb/h). Unlike the MiSeq, the Ion Torrent PGM and 454 GS Junior both produced homopolymer-associated indel errors (1.5 and 0.38 errors per 100 bases, respectively).
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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
                Role: Associate Editor
                Journal
                Bioinformatics
                Bioinformatics
                bioinformatics
                Bioinformatics
                Oxford University Press
                1367-4803
                1367-4811
                15 December 2018
                26 June 2018
                26 June 2018
                : 34
                : 24
                : 4205-4212
                Affiliations
                [1 ]Institute of Medical Informatics, University of Münster, Münster, Germany
                [2 ]Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
                [3 ]Laboratory Hematology, RadboudUMC, Nijmegen GA, The Netherlands
                [4 ]Department of Hematology, Oncology, and Rheumatology, Heidelberg University Hospital, Heidelberg, Germany
                [5 ]Department of Medicine Huddinge, Karolinska Institutet, Stockholm, Sweden
                [6 ]Departments of Hematology Oncology & Molecular Medicine, Fondazione IRCCS Policlinico San Matteo & University of Pavia, Pavia, Italy
                Author notes
                To whom correspondence should be addressed. E-mail: sarah.sandmann@ 123456uni-muenster.de
                Author information
                http://orcid.org/0000-0002-5011-0641
                Article
                bty518
                10.1093/bioinformatics/bty518
                6289140
                29945233
                b05dc5e4-1568-4cc2-b5d7-8776ba29ea12
                © The Author(s) 2018. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                : 19 March 2018
                : 30 May 2018
                : 25 June 2018
                Page count
                Pages: 8
                Funding
                Funded by: European Union
                Funded by: Triage-MDS
                Funded by: ERA-Net TRANSCAN BMBF
                Award ID: 01KT1401
                Funded by: -Horizon2020 MDS-RIGHT
                Award ID: 634789
                Funded by: Deutsche Krebshilfe 10.13039/501100005972
                Funded by: Verbesserung der Diagnostik von Tumorerkrankungen durch neue DNA-Sequenzierverfahren und Algorithmen
                Award ID: 110495
                Funded by: Swedish Cancer Society to Eva Hellström-Lindberg
                Categories
                Original Papers
                Sequence Analysis

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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