Parallel architecture for DNA sequence inexact matching with Burrows-Wheeler Transform

Yao, Xin; Liu, Benben; Min, Biao; Li, Will X. Y.; Cheung, Ray C. C.; Fong, Anthony S.; Chan, Ting‐Fung

doi:10.1016/j.mejo.2013.05.004

Cited by 17 publications

(7 citation statements)

References 26 publications

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“…A significant number of papers have been published that either use FPGAs (see, e.g., [1], [9], [24], [25], [25], [26], [27], [28], [29], [30], [31]) or GPUs (see, e.g., [32], [33]). For instance in [24], the authors propose a hybrid system for short read mapping utilizing both FPGA-based hardware and CPU-based software: the hardware implements parallel block-wise alignment structure to approximate the conventional dynamic programming algorithm.…”

Section: Related Workmentioning

confidence: 99%

<sc>FHAST</sc>: FPGA-Based Acceleration of <sc>Bowtie</sc> in Hardware

Fernandez

Villarreal

Lonardi

et al. 2015

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Abstract-While the sequencing capability of modern instruments continues to increase exponentially, the computational problem of mapping short sequenced reads to a reference genome still constitutes a bottleneck in the analysis pipeline. A variety of mapping tools (e.g., BOWTIE, BWA) is available for general-purpose computer architectures. These tools can take many hours or even days to deliver mapping results, depending on the number of input reads, the size of the reference genome and the number of allowed mismatches or insertion/deletions, making the mapping problem an ideal candidate for hardware acceleration. In this paper, we present FHAST (FPGA hardware accelerated sequence-matching tool), a drop-in replacement for BOWTIE that uses a hardware design based on field programmable gate arrays (FPGA). Our architecture masks memory latency by executing multiple concurrent hardware threads accessing memory simultaneously. FHAST is composed by multiple parallel engines to exploit the parallelism available to us on an FPGA. We have implemented and tested FHAST on the Convey HC-1 and later ported on the Convey HC-2ex, taking advantage of the large memory bandwidth available to these systems and the shared memory image between hardware and software. A preliminary version of FHASTrunning on the Convey HC-1 achieved up to 70x speedup compared to BOWTIE (single-threaded). An improved version of FHAST running on the Convey HC-2ex FPGAs achieved up to 12x fold speed gain compared to BOWTIE running eight threads on an eight-core conventional architecture, while maintaining almost identical mapping accuracy. FHAST is a drop-in replacement for BOWTIE, so it can be incorporated in any analysis pipeline that uses BOWTIE (e.g., TOPHAT).

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Section: Related Workmentioning

confidence: 99%

<sc>FHAST</sc>: FPGA-Based Acceleration of <sc>Bowtie</sc> in Hardware

Fernandez

Villarreal

Lonardi

et al. 2015

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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“…Yao Xin et al [51] presented a hardware architecture for the BWT-based inexact sequence mapping algorithm using the Field Programmable Gate Array(FPGA). The proposed design can handle up to two errors, including mismatches and gaps.…”

Section: Hardware Related Based Approachesmentioning

confidence: 99%

Parallel String Matching Problems with Computing Models An Analysis of the Most Recent Studies

Raju¹,

Rao²,

Raju³

2013

IJCA

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We survey the current techniques to handle with the problem of parallel string matching with computing models. This is becoming a more and more relevant issue for many fast growing areas such as information retrieval and computational biology. We focus on current developments of parallel string matching, computing models, and the central ideas of the algorithms and their complexities. We present the performance of the different algorithms and their effectiveness. Finally this analysis helps the researchers to develop the better technique.

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“…More recently, SOAP3-dp [ 12 ], CUSHAW2 [ 13 ], Barracuda [ 14 ] and our previous work [ 15 ] support GPU computing. FPGA implementations [ 16 ] are also available.…”

Section: Introductionmentioning

confidence: 99%

Fast inexact mapping using advanced tree exploration on backward search methods

et al. 2015

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BackgroundShort sequence mapping methods for Next Generation Sequencing consist on a combination of seeding techniques followed by local alignment based on dynamic programming approaches. Most seeding algorithms are based on backward search alignment, using the Burrows Wheeler Transform, the Ferragina and Manzini Index or Suffix Arrays. All these backward search algorithms have excellent performance, but their computational cost highly increases when allowing errors. In this paper, we discuss an inexact mapping algorithm based on pruning strategies for search tree exploration over genomic data.ResultsThe proposed algorithm achieves a 13x speed-up over similar algorithms when allowing 6 base errors, including insertions, deletions and mismatches. This algorithm can deal with 400 bps reads with up to 9 errors in a high quality Illumina dataset. In this example, the algorithm works as a preprocessor that reduces by 55% the number of reads to be aligned. Depending on the aligner the overall execution time is reduced between 20–40%.ConclusionsAlthough not intended as a complete sequence mapping tool, the proposed algorithm could be used as a preprocessing step to modern sequence mappers. This step significantly reduces the number reads to be aligned, accelerating overall alignment time. Furthermore, this algorithm could be used for accelerating the seeding step of already available sequence mappers. In addition, an out-of-core index has been implemented for working with large genomes on systems without expensive memory configurations.

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Parallel architecture for DNA sequence inexact matching with Burrows-Wheeler Transform

Cited by 17 publications

References 26 publications

<sc>FHAST</sc>: FPGA-Based Acceleration of <sc>Bowtie</sc> in Hardware

<sc>FHAST</sc>: FPGA-Based Acceleration of <sc>Bowtie</sc> in Hardware

Parallel String Matching Problems with Computing Models An Analysis of the Most Recent Studies

Fast inexact mapping using advanced tree exploration on backward search methods

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