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GEO Import
Faisal Rezwan, Helen Parkinson, Alvis Brazma
EMBL-EBI, United Kingdom

To analyze data in GEO (Gene Expression Omnibus) at National Center for Biotechnology Information (NCBI) and design a set of rules how data can be mapped to ArrayExpress (at European Bioinformatics Institute, EMBL-EBI) infrastructure, in particular the data warehouse and the repository and to import some GEO data

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An Optimized Oligonucleotide Array Design for ChIP-on-chip
Fiona Nielsen, Stefan Graef, Xinmin Zhang, Stefan Kurtz, Sergei Denisov, Roland Green, Ewan Birney, Paul Flicek, Martijn Huynen, Henk Stunnenberg
NCMLS, Radboud Universiteit Nijmegen, Netherlands

We have developed a new oligonucleotide array design algorithm using a novel approach to evaluate sequence uniqueness. Using this algorithm, we have tested the performance of different designs in order to optimise the design parameters towards minimal noise and maximal genome coverage.

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Meta-analysis of six Thyroid Tumor Microarray Datasets: a one-gene Classifier (SERPINA1) for Papillary Thyroid Carcinoma
Klemens Vierlinger, Martin Lauss, Christa Nöhammer, Klaus Kaserer, Bruno Niederle, Friedrich Leisch
ARCS, Austria

A Microarray meta-analysis approach revealed a large abundance of discriminative genes between papillary thyroid disease and benign thyroid across 4 distinct datasets. After data integration using DWD, even a single gene (SERPINA1) could correctly identify 99% of thyroid samples.

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puma: a Bioconductor package for Propagating Uncertainty in Microarray Analysis
Richard Pearson, Xuejun Liu, Guido Sanguinetti, Marta Milo, Neil Lawrence, Magnus Rattray
University of Manchester, United Kingdom

Most analyses of microarray data are based on point estimates of expression levels and ignore the uncertainty of such estimates. By propagating uncertainty to downstream analyses we can improve results. For the first time, the puma package makes a suite of uncertainty propagation methods freely available to a general audience.

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ChIP-on-chip significance analysis reveals ubiquitous transcription factor binding
Adam Margolin, Teresa Palomero, Adolfo Ferrando, Andrea Califano, Gustavo Stolovitzky
Columbia University, United States

We present a novel statistical method for inferring significance of transcription factor / target interactions measured by ChIP-on-chip experiments. We infer an order of magnitude more interactions than traditional methods, and predictions are confirmed by ChIP / qPCR experiments, binding site enrichment analysis, and gene expression profiling upon TF inhibition.

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What is the response? Identifying interesting behaviour in microarray time series data
Katherine Lawler, Alvis Brazma
European Bioinformatics Institute, United Kingdom

Microarray studies frequently produce short time series of fewer than 20 timepoints for thousands of genes. It remains a challenging problem to make statistically sound inferences from these short time series. Here we ask: given a microarray timecourse experiment, which genes are showing a response, and what is that response?

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