A Short Reads Alignment Algorithm Oriented To Massive Data

DNA sequencing technology has seen rapid development in recent years, and both the sequencing throughput and read lengths are growing. Besides, new properties such as paired-end sequencing are emerging. Therefore, it is of great value to develop a sequence alignment algorithm for this new type of DNA data. In this paper, an alignment algorithm is proposed. Instead of the Smith-Waterman algorithm, a local alignment algorithm oriented to sparse mutation is used to accelerate seed extension. Besides, instead of aligning short reads one by one, this software puts all reads with similar seeds together to accelerate seed location. This paper uses human genome reference sequences and short sequencing data from GenBank (40 times coverage) to evaluate our algorithm. And we compare our work with Bowtie2 in terms of speed and accuracy. The results show our algorithm has significant advantages in alignment speed and space overhead with large scale data.