NormiRazor: Tool Applying GPU-accelerated Computing for Determination of Internal References in MicroRNA Transcription Studies

Motivation: Multi-gene expression assays are an attractive tool in revealing complex regulatory mechanisms in living organisms. Normalization is an indispensable step of data analysis in all those studies, since it removes unwanted, non-biological variability from data. In targeted qPCR assays the normalization is typically performed with respect to prespecified reference genes, but the lack of robust strategy of their selection is reported in literature, especially in studies concerning circulating microRNAs (miRNA). Results: Previous studies concluded that averaged expressions of multi-miRNA combinations are more stable references than single genes. However, due to the number of such combinations the computational load is considerable and may be hindering for objective reference selection in large datasets. Existing implementations of normalization algorithms (geNorm, NormFinder and BestKeeper) have poor performance as every combination is evaluated sequentially. Thus, we designed an integrative tool which implemented those methods in a parallel manner on a graphics processing unit (GPU) using CUDA platform. We tested our approach on publicly available microRNA expression datasets. As a result the times of executions decreased 19-, 105- and 77-fold respectively for geNorm, BestKeeper and NormFinder. Availability: NormiRazor is available as web application at norm.btm.umed.pl.