Yidong Chen, PhD
Department: Population Health Sciences
Division: Computational Biology & Bioinformatics, Director
Computational Biology and Bioinformatics Initiative
Computational Biology and Bioinformatics (CBBI) focuses on developing computational solutions and statistical modeling to bridge between quantitative 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
- Bioinformatics and Computational Biology
My specialization is in Computational Biology, Bioinformatics, and biostatistics in the area of next-generation sequencing data analysis. I have extensive experience in genomic data analysis and visualization, gene regulation network analysis, Machine Learning/Deep Learning methodology development for pediatric cancer research, drug response prediction, and precision medicine.
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 a 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 network was generated from TCGA breast cancer data by examining their standard miRNA programs. We also performed a stability test for intervention target identification.
A set of pediatric cancer cell-lines profiled by using RNA_seq,exome-capture-seq, overlapping with the collection of Ewing sarcomaDNA 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. A total of 191 patient samples and 41 breast cancer cell-lines are in the database.
RNA methylation, a new epigenetic regulation mechanism, has been examined for their interplay with DAN methylation, microRNA interplay, and gene expression correlation. We are 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 on 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
- 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)
Yu-Chiao (Chris) Chiu, PhD
NIH Pathway to Independence Award (K99/R00)
Johathan Gelfond, PhD, PHS
Aparna Gorthi, PhD
Yufei Huang, PhD
Dias Kurmashev, PhD
Tabrez Mohammad, PhD
Michael H. Nipper
Graduate Research Assistant
Li-Ju (Kathy) Wang
- Cancer Letters: Androgen deprivation-induced elevated nuclear SIRT1 promotes prostate tumor cell survival by reactivation of AR signaling February 19, 2021
- Methods: Prediction and interpretation of cancer survival using graph convolution neural networks January 21, 2021
- Nature Communications: Interaction between SNAI2 and MYOD enhances oncogenesis and suppresses differentiation in Fusion Negative Rhabdomyosarcoma (Ignatius, Houghton, Chen) January 14, 2021