Using GPUs for the Exact Alignment of Short-Read Genetic Sequences by Means of the Burrows-Wheeler Transform

• Published in: IEEE/ACM transactions on computational biology and bioinformatics Vol. 9; no. 4; pp. 1245 - 1256
• Main Authors: Torres, J. S., Espert, I. B., Dominguez, A. T., Garcia, V. Hernendez, Castello, I. Medina, Gimenez, J. Terraga, Blazquez, J. Dopazo
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2012; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Animals; Bioinformatics; Burrows-Wheeler Transform; Computational Biology - methods; Computer Graphics; CUDA; Data Compression - methods; Drosophila melanogaster - genetics; Genes, Insect; Genomics; GPU; Graphics processing unit; Image Processing, Computer-Assisted - methods; Indexes; Models, Genetic; Sequence Alignment - methods; Sequence Analysis, DNA - methods; Short-read alignment; Studies; Transforms; Vectors
• ISSN: 1545-5963; 1557-9964
• DOI: 10.1109/TCBB.2012.49

General Purpose Graphic Processing Units (GPGPUs) constitute an inexpensive resource for computing-intensive applications that could exploit an intrinsic fine-grain parallelism. This paper presents the design and implementation in GPGPUs of an exact alignment tool for nucleotide sequences based on the Burrows-Wheeler Transform. We compare this algorithm with state-of-the-art implementations of the same algorithm over standard CPUs, and considering the same conditions in terms of I/O. Excluding disk transfers, the implementation of the algorithm in GPUs shows a speedup larger than 12×, when compared to CPU execution. This implementation exploits the parallelism by concurrently searching different sequences on the same reference search tree, maximizing memory locality and ensuring a symmetric access to the data. The paper describes the behavior of the algorithm in GPU, showing a good scalability in the performance, only limited by the size of the GPU inner memory.