Parallel-META: efficient metagenomic data analysis based on high-performance computation

Xiaoquan Su, Jian Xu, Kang Ning
Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong, China
BMC Systems Biology, 6(Suppl 1):S16, 2012


   title={Parallel-META: efficient metagenomic data analysis based on high-performance computation},

   author={Su, X. and Xu, J. and Ning, K.},

   journal={BMC Systems Biology},


   number={Suppl 1},



   publisher={BioMed Central Ltd}


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BACKGROUND: Metagenomics method directly sequences and analyses genome information from microbial communities. There are usually more than hundreds of genomes from different microbial species in the same community, and the main computational tasks for metagenomic data analyses include taxonomical and functional component examination of all genomes in the microbial community. Metagenomic data analysis is both data- and computation- intensive, which requires extensive computational power. Most of the current metagenomic data analysis softwares were designed to be used on a single computer or single computer clusters, which could not match with the fast increasing number of large metagenomic projects’ computational requirements. Therefore, advanced computational methods and pipelines have to be developed to cope with such need for efficient analyses. RESULT: In this paper, we proposed Parallel-META, a GPU- and multi-core-CPU-based open-source pipeline for metagenomic data analysis, which enabled the efficient and parallel analysis of multiple metagenomic datasets and the visualization of the results for multiple samples. In Parallel-META, the similarity-based database search was parallelized based on GPU computing and multi-core CPU computing optimization. Experiments have shown that Parallel-META has at least 15 times speed-up compared to traditional metagenomic data analysis method, with the same accuracy of the results http://www.computationalbioenergy.org/parallel-meta.html webcite. CONCLUSION: The parallel processing of current metagenomic data would be very promising: with current speed up of 15 times and above, binning would not be a very time-consuming process any more. Therefore, some deeper analysis of the metagenomic data, such as the comparison of different samples, would be feasible in the pipeline, and some of these functionalities have been included into the Parallel-META pipeline.
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