Shuying Sun

Summary

Affiliation: Case Western Reserve University
Country: USA

Publications

  1. pmc Identifying differentially methylated genes using mixed effect and generalized least square models
    Shuying Sun
    Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio 44106, USA
    BMC Bioinformatics 10:404. 2009
  2. pmc Identifying hypermethylated CpG islands using a quantile regression model
    Shuying Sun
    Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio, USA
    BMC Bioinformatics 12:54. 2011
  3. pmc Comparing a few SNP calling algorithms using low-coverage sequencing data
    Xiaoqing Yu
    Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio 44106, USA
    BMC Bioinformatics 14:274. 2013
  4. pmc Preprocessing differential methylation hybridization microarray data
    Shuying Sun
    Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio, 44106, USA
    BioData Min 4:13. 2011
  5. pmc MethyQA: a pipeline for bisulfite-treated methylation sequencing quality assessment
    Shuying Sun
    Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland 44106, Ohio, USA
    BMC Bioinformatics 14:259. 2013

Collaborators

Detail Information

Publications5

  1. pmc Identifying differentially methylated genes using mixed effect and generalized least square models
    Shuying Sun
    Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio 44106, USA
    BMC Bioinformatics 10:404. 2009
    ..However, patient samples or cell lines are heterogeneous, so their methylation pattern may be very different. In addition, neighboring probes at each CGI are correlated. How these factors affect the analysis of DMH data is unknown...
  2. pmc Identifying hypermethylated CpG islands using a quantile regression model
    Shuying Sun
    Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio, USA
    BMC Bioinformatics 12:54. 2011
    ..However, we were unsure whether 75% was the best quantile level for identifying hypermethylated CGIs. In this paper, we attempt to determine which quantile level should be used to identify hypermethylated CGIs and their associated genes...
  3. pmc Comparing a few SNP calling algorithms using low-coverage sequencing data
    Xiaoqing Yu
    Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio 44106, USA
    BMC Bioinformatics 14:274. 2013
    ..These metrics are highly correlated in complex patterns, making it extremely difficult to select SNPs for further experimental validations...
  4. pmc Preprocessing differential methylation hybridization microarray data
    Shuying Sun
    Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio, 44106, USA
    BioData Min 4:13. 2011
    ..abstract:..
  5. pmc MethyQA: a pipeline for bisulfite-treated methylation sequencing quality assessment
    Shuying Sun
    Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland 44106, Ohio, USA
    BMC Bioinformatics 14:259. 2013
    ..To the best of our knowledge, no existing software packages can generally assess the quality of methylation sequencing data generated based on different bisulfite-treated protocols...