Sun, Shuying

Contact Information
Shuying Sun
Assistant Professor

Office: MCS 571
Phone: 512-245-3422
Fax: 512-245-3425
Research Interest
Statistical genetics and bioinformatics

Dr. Shuying Sun received her Ph.D. in statistics from the University of Toronto in Canada in 2007. Her research focuses on addressing challenging genetic and epigenetic questions using statistical and computational methods. In particular, Dr. Sun has been working on statistical genetics and bioinformatics with a focus on methylation microarray and sequencing data analysis, haplotype inference, mutation age estimation, and genetic variant identification. Dr. Sun has collaborated with biomedical researchers from different research groups in Canada and the United States on projects related to complex diseases (e.g., cancer and arthritis). She has also been developing statistical methodologies and software packages for genomic and epigenomic problems using Bayesian methods, hidden Markov models, Markov Chain Monte Carlo algorithms, and linear models.

Selected Publications:

  • Sun S, Yu X. (2016) HMM-Fisher: Identifying differential methylation using a hidden Markov model and Fisher’s exact test. Statistical Application in Genetics and Molecular Biology. ISSN (Online) 1544-6115, ISSN (Print) 2194-6302, DOI: 10.1515/sagmb-2015-0076, February 2016.
  • Yu X, Sun S. (2016) HMM-DM: Identifying differentially methylated regions using a hidden Markov model Statistical Application in Genetics and Molecular Biology. ISSN (Online) 1544-6115, ISSN (Print) 2194-6302, DOI: 10.1515/sagmb-2015-0077, February 2016.
  • Yu X, Sun S. (2016) Comparing five statistical methods of differential methylation identification using bisulfite sequencing data. Statistical Application in Genetics and Molecular Biology. ISSN (Online) 1544-6115, ISSN (Print) 2194-6302, DOI: 10.1515/sagmb-2015-0078, February 2016.
  • Sun S., Li P. (2015) HMPL: A Pipeline for Identifying Hemimethylation Patterns by Comparing Two Samples. Cancer Informatics, 14(Suppl 2): 235–245.
  • Xu L, Mitra-Behura S, Alston B, Zong Z, Sun S. (2015) Identifying DNA Methylation Variation Patterns to Obtain Potential Breast Cancer Biomarker Genes. International Journal of Biomedical Data Mining, 4: 115.
  • Sun S., Noviski A., Yu X. (2013) MethyQA: a pipeline for bisulfite-treated methylation sequencing quality assessment. BMC bioinformatics 2013, 14:259, page 1-9. 
  • Yu X. Sun S. (2013) Comparing a few SNP calling algorithms using low-coverage sequencing data. BMC bioinformatics 2013, 14:274, page 1-15.
  • Adams M, Veigl M, Wang Z, Molyneux N, Sun S, Guda K, Yu X, Markowitz S, Willis J. (2012) Global Mutational Profiling of Formalin Fixed Human Colon Cancers from a Pathology Archive. Modern Pathology 25, 1599-160.
  • Yu X, Guda K, Willis J, Veigl M, Wang Z, Markowitz  S, Adams MD, and Sun  S. (2012) How well do alignment programs perform on sequencing data with varying qualities and from repetitive regions? BioData Mining 2012, 5:6 
  • Sun S, Chen Z., Yan P, Huang Y. Huang T, Lin S (2011). Identifying hypermethylated CpG islands using a quantile regression model. BMC bioinformatics 2011, 12:54.
  • Sun S, Huang Y., Yan P, Huang T, Lin S. (2011) Preprocessing differential methylation hybridization microarray data. BioData Mining 2011, 4:13.
  • Han L, Zheng S,  Sun S, Huang T. Zhao Z. Genome-wide DNA methylation profiling in 40 breast cancer cell lines. Advanced Intelligent Computing Theories and Applications. Lecture Notes in Computer Science 2010, 6215:277-284.
  • Greenwood C. Sun S, Veenstra J. Hamel N, Niell B, Gruber S, Foulkes W. (2010). How old is this mutation? – a study of three Ashkenazi Jewish founder mutations.  BMC Genetics 2010, 11:39.
  • Sun S, Yan P, Huang T, Lin S. (2009). Identifying differentially methylated genes using mixed effect and generalized least square models. BMC bioinformatics 2009, 10:404.
  • Clendenning M, Baze ME, Sun S, Walsh K, Liyanarachchi S, Fix D, Schumemann V, Comeras I, Deacon M, Wenstrup RJ, Thibodeau ST, Lynch HT, Hampel H, de la Chapelle A (2008). Origins and Prevalence of the American Founder Mutation of MSH2. Cancer Research; 68 (7):2145-2153.
  • Clendenning M,  Senter L,  Hampel H,  Robinson K L,  Sun S,   Buchanan D,  Walsh  MD,  Nilbert  M,  Green JS,  Potter  J,  Lindblom A, de la Chapelle A  (2008) A frame-shift mutation of PMS2 is a widespread cause of Lynch syndrome. Journal of Medical Genetics; 45: 340-345.
  • Lin HL, Zuo T, Lin C, Kuo CT, Liyanarachchi S, Sun S,  Shen  R, Deatherage  DE, Potter  D, Asamoto L, Lin S, Yan P, Cheng A, Ostrowski M, Huang TH (2008). Breast cancer-associated fibroblasts confer AKT1-mediated epigenetic silencing of cystatin M in epithelial cells. Cancer Research; 68:10257-10266.
  • Sun S, Greenwood CM, Neal RM. (2007) Haplotype inference using a Bayesian Hidden Markov Model. Genetic Epidemiology; 31 (8):937-948.
  • Rahman P, Sun S, Peddle L, Snelgrove T, Greenwood CM, Gladman D. (2006) Association between the interleukin-1 family gene cluster and psoriatic arthritis. Arthritis & Rheumatism; 54 (7): 2321-2325.
  • Sun S, Greenwood CM, Thiffault I, Hamel N, Chong G, Foulkes WD. (2005) The HNPCC associated MSH2*1906G->C founder mutation probably originated between 1440 CE and 1715 CE in the Ashkenazi Jewish population, Journal of Medical Genetics; 42: 766-768
  • Butt C, Sun S, Peddle L, Greenwood CM, Hamilton S, Gladman D, Rahman P. (2005) Association of Nuclear Factor-kB in Psoriatic Arthritis. Journal of Rheumatology; 32:1742-1744
  • Butt C, Sun S, Greenwood CM, Gladman D, Rahman P. (2005) Lack of association of SLC22A4, SLC22A5, SLC9A3R1 and RUNX1 variants in psoriatic arthritics, Rheumatology; 44:820-821.
  • Sun S, Liu Z. (2000) Cohomology Complex Projective Space with Actions of G5, Northeastern Mathematical Journal; 16 (3):307-314.