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SVI: A Simple Single-Nucleotide Human Variant Interpretation Tool for Clinical Use

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Data Integration in the Life Sciences (DILS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 9162))

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Abstract

The rapid evolution of Next Generation Sequencing technology will soon make it possible to test patients for genetic disorders at population scale. However, clinical interpretation of human variants extracted from raw NGS data in the clinical setting is likely to become a bottleneck, as long as it requires expert human judgement. While several attempts are under way to try and automate the diagnostic process, most still assume a specialist’s understanding of the variants’ significance. In this paper we present our early experiments with a simple process and prototype clinical tool for single-nucleotide variant filtering, called SVI, which automates much of the interpretation process by integrating disease-gene and disease-variant mapping resources. As the content and quality of these resources improve over time, it is important to identify past patients’ cases which may benefit from re-analysis. By persistently recording the entire diagnostic process, SVI can selectively trigger case re-analysis on the basis of updates in the external knowledge sources.

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Notes

  1. 1.

    http://www.genomicsengland.co.uk/.

  2. 2.

    Funding for Cloud-e-Genome comes from the NIHR (National Institute for Health and Research) and Biomedical Research Centre in the UK.

  3. 3.

    http://www.ncbi.nlm.nih.gov/clinvar/.

  4. 4.

    http://www.ncbi.nlm.nih.gov/omim.

  5. 5.

    http://www.hgmd.cf.ac.uk/.

  6. 6.

    http://grenada.lumc.nl/LSDB_list/lsdbs.

  7. 7.

    http://www.lovd.nl/.

  8. 8.

    https://genomics.med.miami.edu/.

  9. 9.

    http://www.omim.org/.

  10. 10.

    http://www.human-phenotype-ontology.org/.

  11. 11.

    http://genetics.bwh.harvard.edu/pph2/.

  12. 12.

    Experts were not available to confirm whether any of the other Red variants had also been detected.

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Authors and Affiliations

  1. School of Computing Science, Newcastle University, Newcastle upon Tyne, UK

    Paolo Missier, Eldarina Wijaya & Ryan Kirby

  2. Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, UK

    Michael Keogh

Authors
  1. Paolo Missier
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  2. Eldarina Wijaya
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  3. Ryan Kirby
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  4. Michael Keogh
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Corresponding author

Correspondence to Paolo Missier .

Editor information

Editors and Affiliations

  1. USC Stevens Neuroimaging and Informatics Institute, Los Angeles, California, USA

    Naveen Ashish

  2. USC Information Sciences Institute, Marina del Rey, California, USA

    Jose-Luis Ambite

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Missier, P., Wijaya, E., Kirby, R., Keogh, M. (2015). SVI: A Simple Single-Nucleotide Human Variant Interpretation Tool for Clinical Use. In: Ashish, N., Ambite, JL. (eds) Data Integration in the Life Sciences. DILS 2015. Lecture Notes in Computer Science(), vol 9162. Springer, Cham. https://doi.org/10.1007/978-3-319-21843-4_14

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  • DOI: https://doi.org/10.1007/978-3-319-21843-4_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21842-7

  • Online ISBN: 978-3-319-21843-4

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