Literature DB >> 26358141

B-MIC: An Ultrafast Three-Level Parallel Sequence Aligner Using MIC.

Yingbo Cui1, Xiangke Liao1, Xiaoqian Zhu2, Bingqiang Wang3, Shaoliang Peng4.   

Abstract

Sequence alignment is the central process for sequence analysis, where mapping raw sequencing data to reference genome. The large amount of data generated by NGS is far beyond the process capabilities of existing alignment tools. Consequently, sequence alignment becomes the bottleneck of sequence analysis. Intensive computing power is required to address this challenge. Intel recently announced the MIC coprocessor, which can provide massive computing power. The Tianhe-2 is the world's fastest supercomputer now equipped with three MIC coprocessors each compute node. A key feature of sequence alignment is that different reads are independent. Considering this property, we proposed a MIC-oriented three-level parallelization strategy to speed up BWA, a widely used sequence alignment tool, and developed our ultrafast parallel sequence aligner: B-MIC. B-MIC contains three levels of parallelization: firstly, parallelization of data IO and reads alignment by a three-stage parallel pipeline; secondly, parallelization enabled by MIC coprocessor technology; thirdly, inter-node parallelization implemented by MPI. In this paper, we demonstrate that B-MIC outperforms BWA by a combination of those techniques using Inspur NF5280M server and the Tianhe-2 supercomputer. To the best of our knowledge, B-MIC is the first sequence alignment tool to run on Intel MIC and it can achieve more than fivefold speedup over the original BWA while maintaining the alignment precision.

Keywords:  BWA; MIC coprocessor; MPI; NGS; Parallelization; Sequence aligner; Xeon Phi

Mesh:

Year:  2015        PMID: 26358141     DOI: 10.1007/s12539-015-0278-5

Source DB:  PubMed          Journal:  Interdiscip Sci        ISSN: 1867-1462            Impact factor:   2.233


  1 in total

Review 1.  Computing Platforms for Big Biological Data Analytics: Perspectives and Challenges.

Authors:  Zekun Yin; Haidong Lan; Guangming Tan; Mian Lu; Athanasios V Vasilakos; Weiguo Liu
Journal:  Comput Struct Biotechnol J       Date:  2017-08-14       Impact factor: 7.271

  1 in total

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