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Accelerating Disease Gene Identification Through Integrated SNP Data Analysis

  • Paolo Missier
  • Suzanne Embury
  • Conny Hedeler
  • Mark Greenwood
  • Joanne Pennock
  • Andy Brass
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4544)

Abstract

Information about small genetic variations in organisms, known as single nucleotide polymorphism (SNPs), is crucial to identify candidate genes that have a role in disease susceptibility, a long-standing research goal in biology. While a number of established public SNP databases are available, the specification of effective techniques for SNP analysis remains an open issue. We describe a secondary SNP database that integrates data from multiple public sources, designed to support various experimental ranking models for SNPs. By prioritizing SNPs within large regions of the genome, scientists are able to rapidly narrow their search for candidate genes. In the paper we describe the ranking models, the data integration architecture, and preliminary experimental results.

Keywords

Single Nucleotide Polymorphism Score Model Single Nucleotide Polymorphism Data Single Nucleotide Polymorphism Analysis Single Nucleotide Polymorphism Density 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Paolo Missier
    • 1
  • Suzanne Embury
    • 1
  • Conny Hedeler
    • 1
  • Mark Greenwood
    • 1
  • Joanne Pennock
    • 2
  • Andy Brass
    • 1
    • 2
  1. 1.University of Manchester, School of Computer Science, ManchesterUK
  2. 2.University of Manchester, School of Biological Sciences, ManchesterUK

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