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      Challenges of Identifying Clinically Actionable Genetic Variants for Precision Medicine

      review-article
      Author(s): 1 , 1 , 2 , 3 , *
      Publication date (Electronic):
      Journal: Journal of Healthcare Engineering
      Publisher: Hindawi Publishing Corporation

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          Abstract

          Advances in genomic medicine have the potential to change the way we treat human disease, but translating these advances into reality for improving healthcare outcomes depends essentially on our ability to discover disease- and/or drug-associated clinically actionable genetic mutations. Integration and manipulation of diverse genomic data and comprehensive electronic health records (EHRs) on a big data infrastructure can provide an efficient and effective way to identify clinically actionable genetic variants for personalized treatments and reduce healthcare costs. We review bioinformatics processing of next-generation sequencing (NGS) data, bioinformatics infrastructures for implementing precision medicine, and bioinformatics approaches for identifying clinically actionable genetic variants using high-throughput NGS data and EHRs.

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          Most cited references63

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          An integrated semiconductor device enabling non-optical genome sequencing.

          Jonathan Rothberg, Wolfgang Hinz, Todd M. Rearick … (2011)
          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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            Is Open Access

            Comparison of Next-Generation Sequencing Systems

            Lin Liu, Yinhu Li, Siliang Li … (2012)
            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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              CPIC: Clinical Pharmacogenetics Implementation Consortium of the Pharmacogenomics Research Network.

              M V Relling, T E Klein (2011)
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                Author and article information

                Journal
                Journal ID (nlm-ta): J Healthc Eng
                Journal ID (iso-abbrev): J Healthc Eng
                Journal ID (publisher-id): JHE
                Title: Journal of Healthcare Engineering
                Publisher: Hindawi Publishing Corporation
                ISSN (Print): 2040-2295
                ISSN (Electronic): 2040-2309
                Publication date (Print): 2016
                Publication date (Electronic): 6 April 2016
                Volume: 2016
                Electronic Location Identifier: 3617572
                Affiliations
                1Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, WI 54449, USA
                2Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, WI 54449, USA
                3Computation and Informatics in Biology and Medicine, University of Wisconsin-Madison, Madison, WI 53706, USA
                Author notes

                Academic Editor: Saverio Affatato

                Author information
                http://orcid.org/0000-0002-2107-7838
                http://orcid.org/0000-0002-5347-6374
                Article
                DOI: 10.1155/2016/3617572
                PMC ID: 4955563
                PubMed ID: 27195526
                SO-VID: a37d6a26-e027-4f13-b0d3-bcdf17120521
                Copyright © Copyright © 2016 T. C. Carter and M. M. He.
                License:

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 5 August 2015
                Date revision received : 16 March 2016
                Date accepted : 17 March 2016
                Categories
                Subject: Review Article

                Data availability:

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