Dr. Ken Chen
The University of Texas MD Anderson Cancer Center
Department of Bioinformatics and Computational Biology
- Cancer Genomics
- Structural Variation
- Next Generation Sequencing
My Lab is interested in comprehensively and accurately constructing the genomes and the transcriptomes of various cancer cell populations towards understanding the heterogeneity and the evolution of cancer as a consequence of genetics and environment. We are also interested in developing integrative approaches to identify biomarkers that are useful for diagnosis and prognosis.
Project and Techniques:
Students will be involved in learning Statistics, Computer Science, and Biology to solve cutting edge bioinformatics problems in the context of large-scale next-generation genome sequencing projects such as the cancer genome atlas (TCGA) and the 1000 genomes project. They will acquire computer skills in analyzing large-scale high dimensional data set, develop algorithms in C, perl, and R for DNA-seq and RNA-seq data analysis, and apply them to characterize genetic variants that affect cancer biology.
J. Welch et al, "Use of Whole-Genome Sequencing to Diagnose a Cryptic Fusion Oncogene", Journal of American Medical Association, 2011;305(15):1577-1584.
R. Mills, K. Walter, C. Stewart, R. Handsaker, K. Chen, …, E. Eichler, M. Gerstein, M. Hurles, C. Lee, S. McCarroll, and J. Korbel on behalf of the 1000 Genomes Project, "Mapping structural variation at fine scale by population scale genome sequencing", Nature, 2011 Feb 03;470:59-65
K. Chen, J. Wallis, M. McLellan, D. Larson, J. Kalicki, C. Pohl, S. McGrath, M. Wendl, D. Locke, X. Shi, R. Fulton, T. Ley, R. Wilson, L. Ding, and E. Mardis, "BreakDancer: An algorithm for high resolution mapping of genomic structural variation", Nature Methods, 2009 Sep; 6(9): 677-81.
K. Chen, M. D. McLellan, L. Ding, M. C. Wendl, Y. Kasai, R. K. Wilson, and E. R. Mardis, "PolyScan: An automatic indel and SNP detection approach to the analysis of human resequencing data", Genome Research, 2007; 17: 659-666