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We first summarize our recent efforts on translational prostate cancer research, and then discuss the challenges in exploiting the flood of sequence data and translating them into routine diagnostics and prognosis of prostate cancer.We highlight some fundamental issues of translational bioinformatics and the potential use of cloud computing in NGS data processing for the improvement of prostate cancer treatment.