Xiaoquan SU


    Ph.D., Associate Professor

    Bioinformatics Group


    Education and work experiences: 

           Xiaoquan SU received a B.S. in Computer Science from Wuhan University in 2009, a M.S. in Computer Science from State University of New York at Stony Brook in 2011, and a Ph.D in Microbiology from Qingdao Institute of Bioenergy and Bioprocess Technology (QIBEBT), Chinese Academy of Sciences (CAS). He worked in City University of Hong Kong as Senior Research Associate in 2014, and University of California, San Diego as Visiting Scholar supported by China Scholarship Council (CSC) in 2016. His research was funded by National Natural Science Foundation of China (NSFC), Shandong Provincial Natural Science Foundation of China and Chinese Academy of Sciences, which has been published in Bioinformatics, BMC Systems Biology, BMC Genomics, etc.


    Research interests:

      Bioinformatics, Computational Biology, Microbiome, Big data mining


      1. Su*, et al. Identifying and Predicting Novelty in Microbiome Studies. mBio 2018.

      2. Zhou1, Su1, et al. RNA-QC-chain: comprehensive and fast quality control for RNA-Seq data. BMC Genomics 2018.
      3. Jing, Su*, et al. Parallel-META 3: Comprehensive taxonomical and functional analysis platform for efficient comparison of microbial communities. Scientific Reports 2017.
      4. Su, et al. Application of Meta-Mesh on the analysis of microbial communities from human associated-habitats. Quantitative Biology 2015.
      5. Su, et al. GPU-Meta-Storms: Computing the structure similarities among massive amount of microbial community samples using GPU. Bioinformatics 2014.
      6. Su, et al. Rapid comparison and correlation analysis among massive number of microbial community samples based on MDV data model, Scientific Reports 2014.
      7. Su, et al. Parallel-META 2.0: Enhanced Metagenomic Data Analysis with Functional Annotation, High Performance Computing and Advanced Visualization. PLoS One 2014.
      8. Zhou1, Su1, et al. Assessment of the quality control approaches for metagenomic data, Scientific Reports 2014.
      9. Cheng1, Su1, et al. Biological ingredient analysis of traditional Chinese medicine preparation based on high-throughput sequencing: the story for Liuwei Dihuang Wan. Scientific Reports 2014.
      10. Su, et al. Meta-Storms: Efficient Search for Similar Microbial Communities Based on a Novel Indexing Scheme and Similarity Score for Metagenomic Data. Bioinformatics 2012.
      11. X. Su, et al. Parallel-META: efficient metagenomic data analysis based on high-performance computation. BMC Systems Biology 2012.
      12. X. Su, et al. An Open-source Collaboration Environment for Metagenomics Research. IEEE International Conference on E-Science 2011.
      13. P. Yang, X. Su, et al. Microbial community pattern detection in human body habitats via ensemble clustering framework, BMC Systems Biology 2013.
      14. Q. Zhou, X. Su, et al. QC-Chain: Fast and Holistic Quality Control Method for Next-Generation Sequencing Data, PLoS ONE 2013.
      15. B. Song, X. Su and K. Ning. MetaSee: An interactive and extendable visualization toolbox for metagenomic sample analysis and comparison, PLoS One 2012.

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