Department of Molecular and Cell Biology

School of Natural Sciences and Mathematics

Faculty and Research

Zhenyu Xuan, PhD

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B.S. , Theoretical physics, Nanjing University, China
Ph.D., Biophysics, Institute of Biophysics, Chinese Academy of Science, China
Postdoctoral Fellow, Bioinformatics, Cold Spring Harbor Lab, NY, USA

Our research focuses on developing computational methods to analyze cancer genomics data and discover the mechanism of carcinogenesis. With the fast development of high-throughput technologies, such as microarray and next generation sequencing (NGS), there are dramatic amount of biological data produced. We need to develop efficient algorithm and use systems biology concept to analyze them.

Understand cancer with next generation sequencing technology
Cancer is complex disease and arises from combinations of changes that occur in the same cell. Detecting those changes will help in cancer diagnosis and treatment. With the newly developed next generation sequencing technology, we are working on two types of cancer-related variations: single nucleotide variation (SNV), and fusion gene/transcript.
Although each human genome contains millions of variations, most of them cause no changes or only have harmless effects. However, in cancer cells, there exist many harmful variations, also called mutations.   These variations can either change gene’s expression level, or produce dysfunctional proteins. By genotyping both cancer and normal samples with NGS technology, we will be able to find cancer specific mutations, and genes frequently having those mutations. In this way, we could identify potential markers for cancer diagnosis, and also understand the biological mechanism of cancer.
Besides detecting cancer related SNV through genotyping, we also plan to identify other type of variations called fusion genes, caused by genomic structural variations, such as insertion, deletion, transversion, translocation. We use RNA-seq, an application of NGS technology on RNA, to detect the products of fusion gene –fusion transcripts. It is a very challenge work because the output from NGS, called read, is short (~30-100 bp), and may also contain splicing site. We are developing sophisticated algorithms to map those short reads and to detect the fusion events.

Recent publications:

  1. Yoon S, Xuan Z, Makarov V, Ye K, Sebat J. (2009) Sensitive and accurate detection of copy number variants using read depth of coverage. Genome Res. 19(9):1586-92. Epub 2009 Aug 5.
  2. Wang X*, Xuan Z*, Zhao X, Li Y, Zhang MQ.(2009) High-resolution human core-promoter prediction with CoreBoost_HM. Genome Res. 19(2):266-75. Epub 2008 Nov 7.
  3. Smith AD*, Xuan Z*, Zhang MQ.(2008) Using quality scores and longer reads improves accuracy of Solexa read mapping.BMC Bioinformatics. 9:128.
  4. Hodges E*, Xuan Z*, Balija V, Kramer M, Molla MN, Smith SW, Middle CM, Rodesch MJ, Albert TJ, Hannon GJ, McCombie WR. (2007) Genome-wide in situ exon capture for selective resequencing. Nat Genet. 39(12):1522-7. Epub 2007 Nov 4.
  5. He L, He X, Lim LP, de Stanchina E, Xuan Z, Liang Y, Xue W, Zender L, Magnus J, Ridzon D, Jackson AL, Linsley PS, Chen C, Lowe SW, Cleary MA, Hannon GJ. (2007) A microRNA component of the p53 tumour suppressor network.Nature. 447(7148):1130-4. Epub 2007 Jun 6.
  • Updated: April 21, 2010
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