Metagenomics method could directly sequence and dynamically analyze taxonomic and functional information of microbial communities. It is able to potentially optimize bio-processes behind some key bioenergy applications, such as cellulose degradation, polysaccharide metabolism and biogas production. Data-mining among microbial community samples from different time and environment can discover valuable biological information hidden in the massive metagenomic data. However, current low-throughput metagenomic data analysis process has hampered large-scale microbial community analysis, thus it becomes the bottle neck in current metagenomic research.
Figure 1. GPU-Meta-Storms provides a parallel computing solution for the high efficient analysis among massive number of microbial community samples, which could thus enable real-time monitoring of microbial communities.
(Image by Single Cell Center at QIBEBT)
Assistant professor SU Xiaoquan and WANG Xuetao, from Bioinformatics Group of Single-Cell Center, at Qingdao Institute of Bioenergy and Bioprocess Technology (QIBEBT), have proposed highly efficient algorithm name GPU-Meta-Storms to evaluate the similarities among microbial communities based on General-Purpose computing on Graphics Processing Units (GPGPU). This method provides a parallel computing solution that can compare a pair of microbial community samples within one micro second, which enables real-time analysis and monitoring for microbial communities. This work has been published in Bioinformatics online in Dec. 2013. In addition, the Bioinformatics Group has also obtained 5 software authorities on relative works.
This work is led by Prof. NING Kang of QIBEBT, and partially supported by the QIBEBT-NVIDIA Compute Unified Device Architecture (CUDA) Research Center.
Current：