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Bioinformatics. 2014 Apr 1;30(7):1031-3. doi: 10.1093/bioinformatics/btt736. Epub 2013 Dec 19.

GPU-Meta-Storms: computing the structure similarities among massive amount of microbial community samples using GPU.

Author information

1
Shandong Key Laboratory of Energy Genetics, CAS Key Laboratory of Biofuels and Bioenergy Genome Center, Computational Biology Group of Single Cell Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, P. R. China.

Abstract

MOTIVATION:

The number of microbial community samples is increasing with exponential speed. Data-mining among microbial community samples could facilitate the discovery of valuable biological information that is still hidden in the massive data. However, current methods for the comparison among microbial communities are limited by their ability to process large amount of samples each with complex community structure.

SUMMARY:

We have developed an optimized GPU-based software, GPU-Meta-Storms, to efficiently measure the quantitative phylogenetic similarity among massive amount of microbial community samples. Our results have shown that GPU-Meta-Storms would be able to compute the pair-wise similarity scores for 10 240 samples within 20 min, which gained a speed-up of >17 000 times compared with single-core CPU, and >2600 times compared with 16-core CPU. Therefore, the high-performance of GPU-Meta-Storms could facilitate in-depth data mining among massive microbial community samples, and make the real-time analysis and monitoring of temporal or conditional changes for microbial communities possible.

AVAILABILITY AND IMPLEMENTATION:

GPU-Meta-Storms is implemented by CUDA (Compute Unified Device Architecture) and C++. Source code is available at http://www.computationalbioenergy.org/meta-storms.html.

PMID:
24363375
DOI:
10.1093/bioinformatics/btt736
[Indexed for MEDLINE]

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