SGA is a tool based on string graph theory for DNA sequence de novo assembly. In this paper, the sequence de novo assembly problem based on SGA is proved to be an NP-complete problem, and detailed analysis on SGA is provided. According to the result, SGA outperforms other similar tools in memory consumption, but cost much more on time in which 60%~70% is spent by index construction. To tackle these issues, this paper introduces a deep parallel optimization strategy, and implements a Tianhe-2 architecture oriented parallel framework. Experiments are carried out on different data sizes on ordinary cluster and Tianhe-2. For data of small size, the optimized solution is 3.06 times as fast as before, and for data of medium size, it's 1.60 times. The results demonstrate the evident overall improvement and the linear scalability for parallel FM-index construction. This study can be beneficial to the optimization research of other relevant issues, and it also affirms the powerful computing ability of Tianhe-2 as a useful tool in life sciences research.