After the reference genomes of many organisms are sequenced in the post-genetic era, an important issue is to do the re-sequencing or de novo sequencing of individual genomes with high-throughput reads. In the recent years, Next Generational Sequencing (NGS) technologies, such as Illumina/Solexa, ABI/SOLiD and Roche/454 Pyrosequencing are revolutionizing the acquisition of genomics data. NGS technologies are rapidly changing the approach to complex genomic studies, opening the way to personalized drugs development and personalized medicine. NGS technologies are characterized by a massive throughput for relatively short-sequences (reads). NGS datasets are continuing to increase in size, and even small genomic projects now generate terabytes of data. Therefore new computational methods are needed, specifically designed for the type and the amount of data generated by NGS technologies, to replace earlier commonly used genome alignment algorithms that are unable to cope with such massive amount of data. For instants, the development of NGS is forcing a reconsideration of the computational methods used for genome analysis, with the problems of read mapping and genome assembly becoming much more complex (billion of reads). The huge scale of these datasets now poses a great computational challenge: how can we improve software pipelines to analyze data more efficiently? It is difficult to manage and process big biological data using conventional methods, due to their size but also their complexity. It requires entirely different thoughts, while the major obstacle could be the complexity, size, or integration of various data sources. These barriers spur the revolutions of both storage and computing technologies whereby the developed tool and service can be highly scalable, totally reliable, more elastic, and so on. Therefore, high performance computing is feasible solution for processing big biological data.