GPU-Accelerated BWT Construction for Large Collection of Short Reads

Chi-Man Liu, Ruibang Luo, Tak-Wah Lam
HKU-BGI Bioinformatics Algorithms and Core Technology Research Center, Department of Computer Science, University of Hong Kong
arXiv:1401.7457 [q-bio.GN], (29 Jan 2014)


   author={Liu, Chi-Man and Luo, Ruibang and Lam, Tak-Wah},

   title={GPU-Accelerated BWT Construction for Large Collection of Short Reads},

   journal={ArXiv e-prints},




   keywords={Quantitative Biology,Genomics, Distributed, Parallel, and Cluster Computing, Data Structures and Algorithms, Quantitative Methods},




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Advances in DNA sequencing technology have stimulated the development of algorithms and tools for processing very large collections of short strings (reads). Short-read alignment and assembly are among the most well-studied problems. Many state-of-the-art aligners, at their core, have used the Burrows-Wheeler transform (BWT) as a main-memory index of a reference genome (typical example, NCBI human genome). Recently, BWT has also found its use in string-graph assembly, for indexing the reads (i.e., raw data from DNA sequencers). In a typical data set, the volume of reads is tens of times of the sequenced genome and can be up to 100 Gigabases. Note that a reference genome is relatively stable and computing the index is not a frequent task. For reads, the index has to computed from scratch for each given input. The ability of efficient BWT construction becomes a much bigger concern than before. In this paper, we present a practical method called CX1 for constructing the BWT of very large string collections. CX1 is the first tool that can take advantage of the parallelism given by a graphics processing unit (GPU, a relative cheap device providing a thousand or more primitive cores), as well as simultaneously the parallelism from a multi-core CPU and more interestingly, from a cluster of GPU-enabled nodes. Using CX1, the BWT of a short-read collection of up to 100 Gigabases can be constructed in less than 2 hours using a machine equipped with a quad-core CPU and a GPU, or in about 43 minutes using a cluster with 4 such machines (the speedup is almost linear after excluding the first 16 minutes for loading the reads from the hard disk). The previously fastest tool BRC is measured to take 12 hours to process 100 Gigabases on one machine; it is non-trivial how BRC can be parallelized to take advantage a cluster of machines, let alone GPUs.
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