Using Graphics Processors for a High Performance Normalization of Gene Expressions

Ultra high density oligonucleotide microarrays allow several millions of genetic markers in a single experiment to be observed. Current bioinformatics software for gene expression quantile data normalization is unable to process such huge datasets. In parallel with this perception, the huge volume of molecular data produced by current high-throughput technologies in modern molecular biology has increased at a similar pace the challenge in our capacity to process and understand data. On the other hand, the arrival of CUDA has unveiled the extraordinary power of Graphics Processors (GPUs) to accelerate data intensive general purpose computing more and more as times goes by. This work takes these two emerging trends to benefit side by side during the development of a high performance version for a biomedical application of growing popularity: gene expression normalization. A variety of experimental issues are analyzed for this execution, including cost, performance and scalability of the graphics architecture on three different platforms. Our study reveals advantages and drawbacks of using the GPU as target hardware, providing lessons to benefit a broad set of existing genetic applications, either based on those pillars or having similarities with their procedures.