High performance sequence mining using pairwise statistical significance

Yuhong Zhang, Feng Chen
College of Information Science and Engineering, Henan University of Technology, Zhengzhou, P.R. China
Book Chapter in "Advances in Parallel Computing", IOS press, pp. 1-20, 2013
@article{zhang2013high,

   title={High performance sequence mining using pairwise statistical significance},

   author={ZHANG, Yuhong and CHEN, Feng},

   year={2013}

}

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With the amount of sequence data deluge as a result of next generation sequencing, there comes a need to leverage the large-scale biological sequence data. Therefore, the role of high performance computational methods to mining interesting information solely from these sequence data becomes increasingly important. Almost everything in bioinformatics counts on the inter-relationship between sequences, structure and function. Although pairwise statistical significance (PSS) has been found to be capable of accurately mining related sequences (homologs), its estimation is both computationally and data intensive. To keep it from being a performance bottleneck, high performance computation (HPC) approaches are used for accelerating the computation. In this chapter, we first present the algorithm of pairwise statistical significance, then highlights the use of such HPC approaches in acceleration of estimation of pairwise statistical significance using multi-core CPU, many-core GPU, respectively, which both enable significant improvement of accelerating pairwise statistical significance estimation (PSSE).
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