CoreSNP: Parallel processing of microarray data

The availability of high-throughput technologies, such as next generation sequencing and microarray, and the diffusion of genomics studies to large populations are producing an increasing amount of experimental data. In particular, pharmacogenomics studies the impact of genetic variation on drug response in patients and correlates gene expression or single nucleotide polymorphisms (SNPs) with the toxicity or efficacy of a drug, with the aim to improve drug therapy with respect to the patients' genotype ensuring maximum efficacy with minimal adverse effects. However, the storage, preprocessing, and analysis of experimental data are becoming a main bottleneck in the pharmacogenomics analysis pipeline, due to the increasing number of genes and patients investigated. This paper presents a new parallel software tool named coreSNP for the parallel preprocessing and statistical analysis of DMET (Drug Metabolism Enzymes and Transporters) SNP microarray data produced by Affymetrix for pharmacogenomics studies. The scalable multi-threaded implementation of coreSNP allows to handle the huge volumes of experimental pharmacogenomics data in a very efficient way, while its easy to use graphical user interface and its ability to annotate significant SNPs allow biologists to interpret the results easily. Performance evaluation conducted using real datasets shows good speed-up and scalability and effective response times.