Yidong Chen, PhD

  • Rank: Professor
  • Department: Epidemiology Biostatistics
  • Division: Computational Biology & Bioinformatics, Director
  • Office: 4.100.06
  • Location: Greehey CCRI
  • Tel: 1.210.562.9163


Computational Biology and Bioinformatics (CBBI) focuses on developing computational solution and statistical modeling to bridge between quatitative science and the basic biology and translational research within Greehey Children’s Cancer Research Institute and around UT Health San Antonio. Our research contributions are in:

  • Support Genome Sequencing Facility (GSF) bioinformatics operation
  • Develop Next-Generation Sequencing (NGS) data analysis methods
  • Cancer genome profiling, gene expression analysis, Gene regulation networks, and
  • Provide computational biology and biostatistics collaboration for pediatric cancer research

Research Illustration

Gene expression profiles of a set of hepatoblastoma tumors show distinct expression patterns of genes regulation. Working with Dr. Tomlinson, we profiled ~60 tumors using Affymetrix GeneChip and Agilent microRNA microarray for gene expression and miRNA profiles. Genes were selected if they showed negative correlation with miRNA expression.

Competing endogenous RNAs (ceRNAs) are RNAs including mRNA, pseudogenes, and lncRNAs that can regulate each other through competing for common microRNA binding sites. A ceRNA interaction networks was generated from TCGA breast cancer data by examining their common miRNA programs. We also performed stability test for intervention target identification.

A set of pediatric cancer cell-lines profiled by using RNA_seq,exome-capture-seq, overlapping with set of Ewing sarcoma DNA copy number profiles (inner rings), and gene expression of drug-resistant and sensitive EWS tumors (outer rings). Working with Dr. A. Bishop’s Lab, we will further characterize EWS genomes for their unique features related to EWS-FLI1 fusions.

Cancer methylome system (CMS) is a web-based database application designed for the visualization, comparison and statistical analysis of human cancer-specific DNA methylation. Working with Dr. Tim Huang’s Lab, methylation intensities were obtained from MBDCap-sequencing, pre-processed and stored in the database. Total of 191 patient samples and 41 breast cancer cell-lines are in the database.

RNA methylation, a new epigentic regulation mechanism, has been examined for their interplay with DAN methylation, microRNA interplay and gene expression correlation. Collaborating with Dr. Rao and Dr. Y. Huang/UTSA, we have refined the lab protocol and a series of computational software for analyzing methyltranscriptome and its impact to cancer progression.

Meeting the Challenges

Working with Greehey Children's Cancer Research Institute's Genome Sequencing Facility (Dr. Zhao Lai), we processed:

  • processed > 6000 samples
  • 50/year research support letters
  • tools for DNA/RNA/metagenomics
  • developing new protocols

Research Achievements

  • Epigenetics and Systems biology Cancer methylomes of breast, prostate, liver, and others using MBDCap-seq
    (funded by NCI/NIH)
  • Methyltranscriptome of breast cancers using MeRIP-seq
    (funded by NIGMS, NCI/NIH)
  • Integrated genomic data analysis of Hepatoblastoma, soft-tissue sarcoma, and other pediatric cancers
    (funded by CPRIT)
  • Gene regulatory networks, regulation modulation, and competitive endogenous RNA network
    (funded by NSF)
  • NGS data analysis algorithm development such as algorithms for single cell gene expression profiles
    (funded by CTSA, NCI-CC)



Director of Computational Biology and Bioinformatics (CBBI) at Greehey Children's Cancer Research Institute, and Professor at Department of Epidemiology and Biostatistics, specialized in bioinformatics, computational modeling and biostatistics in the area of gene expression, DNA copy number, SNP and other data analysis method development. During the past 14 years research since the beginning of microarray technology, he is able to develop cutting-edge technologies to handle the data that scientists produce as they elucidate the links between genes and cancer. CBBI is of the leaders at the forefront of developing computational intensive analysis tools in UT Health San Antonio at San Antonio, and it provides cutting edge technology, the knowledge of genomic data sets in development around the world, a role in experiment designs, and the synergies from collaboration between computational and experimental biology. Dr. Chen focuses on finding ways to help scientists analyze and visualize their ever-expanding data with increasingly complex statistical methods, diverse computational implementation, specialized experiment design involved in genomic experiments, such as the joint analysis with DNA copy number and gene expression profiling of breast cancer cell lines by using the high-resolution tiling-path microarray technology.

Dr. Yidong Chen received his BS/MS degrees in Electrical Engineering from Fudan University, Shanghai, China, and Ph.D. in Imaging Science from Rochester Institute of Technology, Rochester, NY. He has been with Hewlett Packard Co as a Research Engineer before he joined National Institutes of Health (NIH) at 1996. At NIH, Dr. Chen joined microarray technology development effort at National Human Genome Research Institute (NHGRI), as a Special Expert, Staff Scientist, and later Associate Investigator for microarray image, statistical analysis, and bioinformatics. From 2006-2008 he joined the Genetics branch at National Cancer Institute (NCI) as a staff scientist. During the 13-year period with NHGRI and NCI, he has contributed about 90 peer reviewed publications and book chapters.



Bioinformatics and Computational Biology I am specialized in Computational Biology, Bioinformatics and biostatistics in the area of next-generation sequencing data analysis, microarray data analysis, genomic data analysis, genome annotation, with extensive experiences in gene expression, arrayCGH data analysis, SNP data analysis, data visualization in cancer research, microarray database management, gene regulation network modeling, pattern classification, and biomedical image processing.



The Computational Biology and Bioinformatics Initiative (CBBI) provides computational infrastructure, bioinformatics expertise, and interdisciplinary research to manage, distribute complex data sets, develop and perform bioinformatics tasks and statistical analyses in the area of genomics and systems biology. The CBBI aims to establish Greehey CCRI, as well as UT Health San Antonio, and its researchers as competitive leaders in the application of new biomedical technologies and computational methods to the varieties of biomedical study. With all their faculty members from Department of Epidemiology and Biostatistics (DEB), The CBBI also provides a wide range of biostatistical assistance. The supports include, but not limited to:

  • Data analysis and microarray data quality control for microarray-based, genome-wide profiling experiment;
  • Common genomic sequence-based bioinformatics tasks, such as sequence-alignment and genomic level annotation;
  • Assistance to investigators with study planning, genomic-based experiment design, sample size and power analyses;
  • Development of data analysis tools that will allow investigators to generate and validate new hypotheses based on the integration of genomic and clinical data;
  • Support for data resources, tools, and protocols that will enable the investigators in sharing and applying genomic data to basic and translational research. Support for clinical informatics resources, including database design and application development expertise; and
  • Other computational needs for basic/laboratory science, translational research, clinical-, and population-based research, including biomedical image analysis.
CBBI, directed by Dr. Yidong Chen, is an integral part of Greehey CCRI’s mission, and fully supported by DEB. By providing access to the expertise in data-driven and model-based computational research for investigators at Greehey CCRI, the CBBI opens new areas of research, enhances the quality and consistency of high-throughput data analysis and improves the UT Health San Antonio's ability to support research in this genomic and systems biology era.




Funding Agency NIH/NCI
Title Cancer Therapy and Research Center/Caner Center Admin
Status Active Active
Period 5/2014-4/2019
Role Contributor
Grant Detail This is a P30 Cancer Center Support Grant at the UT Health San Antonio. This grant provides funding for the conduct of cancer clinical trials, prevention studies and assistance with statistical design and analysis of translational cancer research projects. Specifically, this is for support of the biostatistics and medical informatics core.

Funding Agency NIH/NCATS
Title Institute for Integration of Medicine & Science (IIMS): A Partnership to Improve Health
Status Active Active
Period 9/2013-8/2018
Role Contributor

Funding Agency NIH/NIGMS
Title Graphical models for characterizing global RNA methylation
Status Active Active
Period 9/2014-8/2017
Role Co-Principal Investigator
Grant Detail The goal of this project is to develop, for the first time, computational graphical models to enable

  1. accurate and reproducible detection of global mRNA methylations, and
  2. context-specific differential RNA methylations in normal and disease state

Funding Agency NIH/NCI
Title The Cancer Bioinformatics Initiative: A UTSA/UT Health San Antonio Partnership
Status Active Active
Period 9/2012-8/2016
Role Co-Investigator
Grant Detail This program will provide opportunities for students and faculty at the University of Texas at San Antonio (UTSA), a minority serving institution, to gain relevant experience by interacting directly with cancer center members at the Cancer Therapy and Research Center (UT Health Cancer Center) at the UT Health San Antonio, an NCI designated Cancer Center. The interaction will also provide UT Health San Antonio cancer researchers with needed computational analysis and modeling assistance from quantitative scientists across both campuses. Additional opportunities will be provided for intensive short courses/workshops for computational biology training aimed for a mixed audience of biologists and quantitative scientists is also planned.

Funding Agency NCI
Title e Improving etoposide treatment of Ewing''s sarcoma
Status Active Active
Period 9/2011-8/2016
Role Contributor
Grant Detail To identify genes involved in surviving etoposide exposure and to determine the utility of targeting these genes to improve etoposide treatment of Ewings sarcoma

Funding Agency NIH/NIAID
Title Determination of Morphology and Virulence in Candida albicans
Status Active Active
Period 5/2010-4/2016
Role Consultant
Grant Detail The major goal of this grant is to determine how expression levels of a key filament-specific transcriptional regulator are controlled by host environmental cues and specify morphology and virulence in Candida albicans.


Funding Agency Cancer Prevention & Research Institute of Texas
Title Functional Genomics and Computation Corebr> Status Active Active
Period 8/2012-8/2017
Role Principal Investigator
Grant Detail A computational genomic core to perform integrative genomic analysis for a collection of soft-tissue sarcoma tumors from childrens in Texas, and genomic data from model organism.