Massively parallel mapping of next generation sequence reads using GPU
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The high throughput sequencing (HTS) methods have already started to fundamentally revolutionize the area of genome research through low-cost and highthroughput genome sequencing. However, the sheer size of data imposes various computational challenges. For example, in the Illumina HiSeq2000, each run produces over 7-8 billion short reads and over 600 Gb of base pairs of sequence data within less than 10 days. For most applications, analysis of HTS data starts with read mapping, i.e. nding the locations of these short sequence reads in a reference genome assembly. The similarities between two sequences can be determined by computing their optimal global alignments using a dynamic programming method called the Needleman-Wunsch algorithm. The Needleman-Wunsch algorithm is widely used in hash-based DNA read mapping algorithms because of its guaranteed sensitivity. However, the quadratic time complexity of this algorithm makes it highly timeconsuming and the main bottleneck in analysis. In addition to this drawback, the short length of reads ( 100 base pairs) and the large size of mammalian genomes (3.1 Gbp for human) worsens the situation by requiring several hundreds to tens of thousands of Needleman-Wunsch calculations per read. The fastest approach proposed so far avoids Needleman-Wunsch and maps the data described above in 70 CPU days with lower sensitivity. More sensitive mapping approaches are even slower. We propose that e cient parallel implementations of string comparison will dramatically improve the running time of this process. With this motivation, we propose to develop enhanced algorithms to exploit the parallel architecture of GPUs.