Functional genomics via multiscale analysis: application to gene expression and ChIP-on-chip data

We present a fast, versatile and adaptive-multiscale algorithm for analyzing a wide-variety of DNA microarray data. Its primary application is in normalization of array data as well as subsequent identification of 'enriched targets', e.g. differentially expressed genes in expression profiling arrays and enriched sites in ChIP-on-chip experimental data. We show how to accommodate the unique characteristics of ChIP-on-chip data, where the set of 'enriched targets' is large, asymmetric and whose proportion to the whole data varies locally. SUPPLEMENTARY INFORMATION: Supplementary figures, related preprint, free software as well as our raw DNA microarray data with PCR validations are available at http://www.math.umn.edu/~lerman/supp/bioinfo06 as well as Bioinformatics online.     

ArrayFusion: a web application for multi-dimensional analysis of CGH, SNP and microarray data

ArrayFusion annotates conventional CGH results and various types of microarray data from a range of platforms (cDNA, expression, exon, SNP, array-CGH and ChIP-on-chip) and converts them into standard formats which can be visualized in genome browsers (Affymetrix Integrated Genome Browser and GBrowse in the HapMap Project). Converted files can then be imported simultaneously into a single genome browser to benefit a collective interpretation between different array results. ArrayFusion therefore provides a new type of tool facilitating the integration of CGH and array results to provide new experimental directions. AVAILABILITY: http://microarray.ym.edu.tw/tools/arrayfusion     

Apport des puces à ADN en cancérologie

DNA microarrays are tools for a new discipline, oncogenomics. Pangenomics chips provide a landscape of DNA lesions in tumors (amplifications, deletions and other mutations), explore protein-DNA interaction in promoter regions (ChIP-on-chip assays) and quantify RNA (gene chip expression arrays, microRNA arrays). They generate huge data sets that need to be explored with data mining techniques. The goals are to reveal new therapeutic targets, to provide a limited number of biomarkers for cancer classification and pronostic that can be used in clinical laboratories with routine techniques such as PCR and immunohistochemistry. However low-cost dedicated microarrays are now also available for clinical laboratories. They establish gene expression signatures based upon 50 to 100 genes or detect mutations of tumor suppresor genes such as p53.