Genome-wide RNA interference screening has emerged as a powerful tool for functional genomic studies of disease-related phenotypes and the discovery of molecular therapeutic targets for human diseases. Commercial short hairpin RNA (shRNA) libraries are commonly used in this area, and state-of-the-art technologies including microarray and next-generation sequencing have emerged as powerful methods to analyze shRNA-triggered phenotypes. However, computational analysis of this complex data remains challenging due to noise and small sample size from such large-scaled experiments. In this chapter we discuss the pipelines and statistical methods of processing, quality assessment, and post-analysis for both microarray- and sequencing-based screening data.