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We are a computational biology group in the Department of Bioinformatics and Computational Biology at UT MD Anderson Cancer Center in Houston, Texas.
We are interested in developing computational methods to systematically and accurately characterize the genomics and the transcriptomics of human cell populations using high throughput sequencing technology. For example, we have developed approaches that successfully discovered and reconstructed structural variation in a variety of cancer genomes. We are also interested in developing integrative approaches that help understand the etiology of cancer. We are actively involved in several large-scale genomics projects such as the Cancer Genome Atlas (TCGA) and the 1000 Genomes projects. Since 2012, we have worked with the Khalifa Institute of Personalized Cancer Therapy to establish routine genome sequencing at MD Anderson cancer center.
Dr. Ken Chen (h-index: 28, 2015) received his B.E. from Tsinghua University (Beijing), Ph.D. from University of Illinois at Urbana-Champaign, and postdoctoral training from University of California at San Diego. From 2005 to 2011, he worked for Washington University School of Medicine in St. Louis as a senior scientist and a research faculty. Having a background in machine learning, statistical signal processing, and cancer genomics, his primary goal is to develop computational tools to analyze and interpret human genomics and clinical data towards the realization of genomic medicine. Dr. Chen has designed, developed, and co-developed a set of computational tools such as BreakDancer, TIGRA, CREST, BreakTrans, BreakFusion, PolyScan, SomaticSniper, VarScan, TransVar, novoBreak and Monovar that have been widely applied to characterize individual and population genomics in various large-scale next-generation sequencing projects such as those in the Cancer Genome Atlas (TCGA) and the 1000 Genomes Project. He is particularly 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. He is also interested in developing integrative approaches to identify biomarkers that are useful for diagnosis and prognosis. He has served as the Director of Bioinformatics for the Khalifa Institute of Personalized Cancer Therapy since 2012.
1 Computational approaches for next/third generation sequencing data analysis
2 Detection of structural and copy number variation in normal and cancer genomes
3 Accurate assembly of novel DNA or RNA sequence variants
4 Discovery and genotyping of sequence variants in heterogenous sample or population
5 Graphic approaches for RNA-seq analysis
6 Integrative characterization of genome and transcriptome
7 Computational approaches for single cell genomics
8 High quality bioinformatics tools for clinical sequencing and clinical decision support
9 Cancer genetic driver prediction for biomarker and therapeutic target discovery
10 Computational cancer immunogenomics