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    is-rSNP: a novel technique for in silico regulatory SNP detection

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    18
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    Author
    Macintyre, G; Bailey, J; Haviv, I; Kowalczyk, A
    Date
    2010-09-01
    Source Title
    BIOINFORMATICS
    Publisher
    OXFORD UNIV PRESS
    University of Melbourne Author/s
    Bailey, James; Haviv, Izhak; Kowalczyk, Adam; MACINTYRE, GEOFFREY
    Affiliation
    Computer Science and Software Engineering
    Metadata
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    Document Type
    Journal Article
    Citations
    Macintyre, G., Bailey, J., Haviv, I. & Kowalczyk, A. (2010). is-rSNP: a novel technique for in silico regulatory SNP detection. BIOINFORMATICS, 26 (18), pp.i524-i530. https://doi.org/10.1093/bioinformatics/btq378.
    Access Status
    Access this item via the Open Access location
    URI
    http://hdl.handle.net/11343/31773
    DOI
    10.1093/bioinformatics/btq378
    Open Access at PMC
    http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2935445
    Abstract
    MOTIVATION: Determining the functional impact of non-coding disease-associated single nucleotide polymorphisms (SNPs) identified by genome-wide association studies (GWAS) is challenging. Many of these SNPs are likely to be regulatory SNPs (rSNPs): variations which affect the ability of a transcription factor (TF) to bind to DNA. However, experimental procedures for identifying rSNPs are expensive and labour intensive. Therefore, in silico methods are required for rSNP prediction. By scoring two alleles with a TF position weight matrix (PWM), it can be determined which SNPs are likely rSNPs. However, predictions in this manner are noisy and no method exists that determines the statistical significance of a nucleotide variation on a PWM score. RESULTS: We have designed an algorithm for in silico rSNP detection called is-rSNP. We employ novel convolution methods to determine the complete distributions of PWM scores and ratios between allele scores, facilitating assignment of statistical significance to rSNP effects. We have tested our method on 41 experimentally verified rSNPs, correctly predicting the disrupted TF in 28 cases. We also analysed 146 disease-associated SNPs with no known functional impact in an attempt to identify candidate rSNPs. Of the 11 significantly predicted disrupted TFs, 9 had previous evidence of being associated with the disease in the literature. These results demonstrate that is-rSNP is suitable for high-throughput screening of SNPs for potential regulatory function. This is a useful and important tool in the interpretation of GWAS. AVAILABILITY: is-rSNP software is available for use at: www.genomics.csse.unimelb.edu.au/is-rSNP.
    Keywords
    Information Systems

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