Parallelized preprocessing algorithms for high-density oligonucleotide array data

Abstract. Studies of gene expression using high-density oligonucleotide microar-rays have become standard in a variety of biological contexts. The data recorded using the microarray technique are characterized by high levels of noise and bias. These failures have to be removed, therefore preprocessing of raw-data has been a research topic of high priority over the past few years. Actual research and computations are limited by the available computer hard-ware. For many researchers the available main memory limits the number of arrays that may be processed. Furthermore most of the existing preprocessing methods are very time consuming and therefore not useful for first and fast checks in laborato-ries. To solve these problems, the potential of parallel computing should be used. In microarray technologies and statistical computing parallel computing does not ap-pear to have been used extensively. For parallelization on multicomputers, message passing (MPI) methods and the R language will be used. Ideas for parallelization of VSN and FARMS as well as a large project in applied bioinformatics (> 5000 microarrays) will be discussed. Furthermore this presentatio