Wang, Chi

Our group focuses on the study design, data analysis pipeline construction, and novel statistical methods development of high- or moderate-throughput genomic data. We have developed a novel statistical method for differential expression identification based on RNA-seq data. We are also working on the construction of pipelines for whole exome sequencing data processing and analysis. In addition, we are working on the development of novel statistical methods for the analysis of NanoString nCounter data.

Pipelines for Exome-sequencing data processing and analysis

The goal of this project is to construct whole exome sequencing data processing and analysis pipelines. We will integrate a number of existing software and develop efficient and robust pipelines for automated processing of exome sequencing data. We will also apply the pipelines to analyze cancer-related exome sequencing data.

Collaborators

Collaborators: Drs. Jinze Liu, Heidi Weiss, Susanne Arnold, Chunming Liu Staff involved in the project: Jinpeng Liu (already has an HPC account)

Methods for NanoString nCounter data processing and analysis

The NanoString nCounter Platform is a new and promising technology for measuring abundances of mRNAs, microRNAs, or DNA targets. The goal of this project is to develop novel statistical methods for the processing and analysis of NanoString nCounter data. Specifically, data will be processed by lane normalization, background correction and sample content normalization.
Samples under different experimental conditions will be compared and differentially expressed features will be identified. An R package will be developed for method implementation and dissemination.

Students

PhD student involved in the project:
Li Xu, Grad
Hong Wang
Leon C Su (STEPS Tech Scientific/UKHC)
Yuntong Li, Grad
Menghan Wang, Grad, Statistics

Tiantian Zeng, Grad, Statistics
Kun Liu, Grad, Statistics

Shashank Gupta, Grad, Internal Medicine & Div - Biomedical, Added 01/05/2022

Ryan A Goetti, Bioinformatics Analyst, Added on MCC resources 12/05/2022 

Shouyi Liang, Graduate, Added to MCC Resources 02/13/2023 

Abu Saleh Mosa Faisal, Bioinformatics Analyst, Added on MCC resources 03/17/2023 

Jinge Liu, Bioinformatics Analyst, Added on MCC resources 06/05/2023 

Ruiyi Jiang, Added on MCC resources, 10/23/2023 

Software

R

Cancer Mutation Pattern Analysis based on DNA-seq Data

Cancer driver mutations associated with genes within a pathway often show a mutually exclusive pattern, meaning that each patient carries exactly one mutation in the pathway, which is sufficient to perturb the function of that pathway. Another prominent pattern is that driver mutations of genes from several different pathways may co-occur, since perturbation of multiple pathways is required for tumor formation. Screening for mutual exclusivity and co-occurrence patterns can greatly facilitate the identification of novel sets of related driver gene mutations. This project is to develop a novel statistical method to detect prominent somatic mutation patterns using whole genome/exome sequencing data.

Student

Satrio Husodo, Grad (Removed from Group 08/22/2017)
Sisheng Liu
Daheng He, Temporary Technical/Paraprofessional

Evaluate the association between personality and alcohol use with computationally intensive statistical methods.

Student

Leon C Su, Grad

Computational Methods

Project 1: We will use a number of publicly availably software. Some of them are not on HPC yet, but can be installed.

Project 2: The computational methods will involve fitting probabilistic models. We will develop R scripts for this task.

Project 3: Cancer Mutation Pattern Analysis based on DNA-seq Data.

Project 4: Evaluate the association between personality and alcohol use with computationally intensive statistical methods.

Grants