Personalized medicine is defined as "a model of healthcare that is predictive, personalized, preventive, and participator" and has very broad content. With the rapid development of high-throughput technologies, an explosive accumulation of biological information is collected from multiple layers of biological processes, including genomics, transcriptomics, proteomics, metabonomics, and interactomics (omics). Implementing integrative analysis of these multiple omics data is the best way of deriving systematical and comprehensive views of living organisms, achieving better understanding of disease mechanisms, and finding operable personalized health treatments. With the help of computational methods, research in the field of biology and biomedicine has gained tremendous benefits over the past few decades. In the new era of personalized medicine, we will rely more on the assistance of computational analysis. In this paper, we briefly review the generation of multiple omics and their basic characteristics. And then the challenges and opportunities for computational analysis are discussed and some state-of-art analysis methods that were recently proposed by peers for integrative analysis of multiple omics data are reviewed. We foresee that further integrated omics data platform and computational tools would help to translate the biological knowledge to clinical usage and accelerate development of personalized medicine.