Predicting the Pathogenic Impact of Sequence Variation in the Human Genome

Mark F Rogers, Hashem A Shihab, Michael Ferlaino, Tom R Gaunt, Colin K Campbell

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

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Abstract

Sequencing data will become widely available in clinical practice within the near future. Uptake of sequence data is currently being stimulated within the UK through the government-funded 100,000 genomes project (Genomics England), with many similar initiatives being planned and supported internationally. The analysis of the large volumes of data derived from sequencing programmes poses a major challenge for data analysis. In this paper we outline progress we have made in the development of predictors for estimating the pathogenic impact of single nucleotide variants, indels and haploinsufficiency in the human genome. The accuracy of these methods is enhanced through the development of disease-specific predictors, trained on appropriate data, and used within a specific disease context. We outline current research on the development of disease-specific predictors, specifically in the context of cancer research.
Original languageEnglish
Title of host publicationInformatics for Health
Subtitle of host publicationConnected Citizen-Led Wellness and Population Health
EditorsRebecca Randell, Ronald Cornet, Colin McCowan, Niels Peek, Philip J Scott
PublisherIOS Press
Pages91-95
Number of pages5
ISBN (Electronic)9781614997535
ISBN (Print)9781614997528
DOIs
Publication statusE-pub ahead of print - 17 May 2017
EventInformatics for Health 2017 - Manchester, United Kingdom
Duration: 24 Apr 201726 Apr 2017

Publication series

NameStudies in Health Technology and Informatics
PublisherIOS Press
Volume235
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Conference

ConferenceInformatics for Health 2017
CountryUnited Kingdom
CityManchester
Period24/04/1726/04/17

Keywords

  • Prediction
  • sequence data
  • variant
  • annotation
  • point mutation
  • indel

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