Computing and Information Systems - Research Publications
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Itemis-rSNP: a novel technique for in silico regulatory SNP detection(OXFORD UNIV PRESS, 2010-09)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.
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ItemMIRAGAA-a methodology for finding coordinated effects of microRNA expression changes and genome aberrations in cancer(OXFORD UNIV PRESS, 2010-01-15)MOTIVATION: Cancer evolves through microevolution where random lesions that provide the biggest advantage to cancer stand out in their frequent occurrence in multiple samples. At the same time, a gene function can be changed by aberration of the corresponding gene or modification of microRNA (miRNA) expression, which attenuates the gene. In a large number of cancer samples, these two mechanisms might be distributed in a coordinated and almost mutually exclusive manner. Understanding this coordination may assist in identifying changes which significantly produce the same functional impact on cancer phenotype, and further identify genes that are universally required for cancer. Present methodologies for finding aberrations usually analyze single datasets, which cannot identify such pairs of coordinating genes and miRNAs. RESULTS: We have developed MIRAGAA, a statistical approach, to assess the coordinated changes of genome copy numbers and miRNA expression. We have evaluated MIRAGAA on The Cancer Genome Atlas (TCGA) Glioblastoma Multiforme datasets. In these datasets, a number of genome regions coordinating with different miRNAs are identified. Although well known for their biological significance, these genes and miRNAs would be left undetected for being less significant if the two datasets were analyzed individually. AVAILABILITY AND IMPLEMENTATION: The source code, implemented in R and java, is available from our project web site at http://www.csse.unimelb.edu.au/~rgaire/MIRAGAA/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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ItemMining Distribution Change in Stock Order Streams(IEEE COMPUTER SOC, 2010)
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ItemDocument clustering of scientific texts using citation contexts(SPRINGER, 2010-04)