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dc.contributor.authorMacintyre, G
dc.contributor.authorBailey, J
dc.contributor.authorHaviv, I
dc.contributor.authorKowalczyk, A
dc.date.available2014-05-22T04:11:49Z
dc.date.issued2010-09-01
dc.identifierpii: btq378
dc.identifier.citationMacintyre, 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.
dc.identifier.issn1367-4803
dc.identifier.urihttp://hdl.handle.net/11343/31773
dc.description.abstractMOTIVATION: 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.
dc.languageEnglish
dc.publisherOXFORD UNIV PRESS
dc.subjectInformation Systems
dc.titleis-rSNP: a novel technique for in silico regulatory SNP detection
dc.typeJournal Article
dc.identifier.doi10.1093/bioinformatics/btq378
melbourne.peerreviewPeer Reviewed
melbourne.affiliationThe University of Melbourne
melbourne.affiliation.departmentComputer Science and Software Engineering
melbourne.source.titleBIOINFORMATICS
melbourne.source.volume26
melbourne.source.issue18
melbourne.source.pagesi524-i530
dc.research.codefor0806
dc.rights.licenseCC BY-NC
dc.description.pagestart524
melbourne.publicationid147543
melbourne.elementsid324371
melbourne.openaccess.pmchttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC2935445
melbourne.contributor.authorBailey, James
melbourne.contributor.authorHaviv, Izhak
melbourne.contributor.authorKowalczyk, Adam
melbourne.contributor.authorMACINTYRE, GEOFFREY
dc.identifier.eissn1460-2059
melbourne.accessrightsAccess this item via the Open Access location


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