Efficient computation of motif discovery on Intel Many Integrated Core (MIC) Architecture

Novel sequence motifs detection is becoming increasingly essential in computational biology. However, the high computational cost greatly constrains the efficiency of most motif discovery algorithms. In this paper, we accelerate MEME algorithm targeted on Intel Many Integrated Core (MIC) Architectur...

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Bibliographic Details
Published in:BMC bioinformatics 2018-08, Vol.19 (Suppl 9), p.282-282, Article 282
Main Authors: Peng, Shaoliang, Cheng, Minxia, Huang, Kaiwen, Cui, YingBo, Zhang, Zhiqiang, Guo, Runxin, Zhang, Xiaoyu, Yang, Shunyun, Liao, Xiangke, Lu, Yutong, Zou, Quan, Shi, Benyun
Format: Article
Language:English
Subjects:
Algorithms
Architecture
Bioinformatics
Biopolymers
Computational biology
Computational Biology - methods
Computational efficiency
Computer applications
Computer Graphics
Coprocessors
Databases, Genetic
Datasets
Deoxyribonucleic acid
DNA
Equipment and supplies
Humans
Internet
Iterative methods
MEME
Methods
MIC
Motif discovery
Multiple core processors
Nucleotide Motifs
Offload mode
Parallel processing
Processor architectures
Promoter Regions, Genetic
Properties
Proteins
Regulatory Elements, Transcriptional
Software
Source code
Transcription Factors - metabolism
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Summary:Novel sequence motifs detection is becoming increasingly essential in computational biology. However, the high computational cost greatly constrains the efficiency of most motif discovery algorithms. In this paper, we accelerate MEME algorithm targeted on Intel Many Integrated Core (MIC) Architecture and present a parallel implementation of MEME called MIC-MEME base on hybrid CPU/MIC computing framework. Our method focuses on parallelizing the starting point searching method and improving iteration updating strategy of the algorithm. MIC-MEME has achieved significant speedups of 26.6 for ZOOPS model and 30.2 for OOPS model on average for the overall runtime when benchmarked on the experimental platform with two Xeon Phi 3120 coprocessors. Furthermore, MIC-MEME has been compared with state-of-arts methods and it shows good scalability with respect to dataset size and the number of MICs. Source code: https://github.com/hkwkevin28/MIC-MEME .
ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-018-2276-1