Xiaowei.Zhan@UTSouthwestern.edu +1 (214) 648-5194 +1 (214) 648-1663

Welcome to Zhan Lab

Our mission is to advance medical research through cutting-edge data science models and computationally efficient software tools.

Dr. Xiaowei Zhan

Associate Professor of the Quantitative Biomedical Research Center in the Department of Population and Data Sciences at UT Southwestern Medical Center.

Before joining UT Southwestern, Dr. Zhan was a doctoral student in the Center of Statistical Genetics in Department of Biostatistics at the University of Michigan, a master student in the Mathematics and Statistics program at the University of Minnesota-Duluth, and a bachelor student in the department of automation at Tsinghua University, Beijing, China.

Dr. Zhan’s group focuses on statistical genetics and statistical computations. We use bioinformatics and statistical approaches to address emerging medical questions. Since 2014, our group has published more than 50 papers in human medical genetics, human microbiome, and antibiotic resistance research. For example, our group has developed Bayesian statistical models to analyze microbiome data from cancer immune checkpoint therapy patients and derived microbial biomarkers in a prediction model. All our research is accompanied with easy-to-use and efficient software package to facilitate both internal and external research communities. Please visit the publication page for more details.

Research Interests

We develop translational models to tackle complex biomedical challenges, with the goal of promoting health for all.

Human genetics

We use statistical genetics to study human complex trait diseases. This includes genetic association studies accompanied with efficient analysis software packages.

Microbiome

The human microbiome consists of both beneficial and harmful bacteria that coexist with us. We develop a series of quantitative approach to study them, mainly in the context of cancer immune checkpoint therapy patients and microbial biomarkers research.

Antibiotic resistance

Antibiotic resistance poses a global health threat. We utilize the cutting-edge sequencing technology and machine algorithms. With close collaboraiton wtih clinicians, we have been developing diagnosis tools to tackle this threat.

Deep learning

To address biomedical challenges, we develop state-of-the-art deep learning models that can solve domain-specific questions.

Software

We develop scientific software for the broad research communities.

IsoAnalytics

We have created a web server, IsoAnalytics, that gives users without computational or bioinformatics background the ability to directly analyze and interactively visualize data obtained from the Isoplexis single cell technology platform.

MB-SupCon

We developed a novel supervised contrastive learning framework for general microbiome and metabolomics studies. It is a general method to improve microbiome-based prediction models and it can generate microbiome embeddings form separatable clusters in lower-dimensional space.

VAMPr

We develop variant mapping and prediction of antibiotic resistance (VAMPr) to (1) derive gene ortholog-based sequence features for protein variants; (2) interrogate these explainable gene-level variants for their known or novel associations with AMR; and (3) build accurate models to predict AMR based on whole genome sequencing data.

MetaPrism

We developed a novel bioinformatic toolkit to analyze sequence reads for a set of joint taxa/gene analyses.

FMAP

FMAP is a comprehensive tool for providing functional analysis of metagenomic/metatranscriptomic sequencing data. With the added features of integrated alignment, operon-level analysis, and the ability to process large datasets, FMAP will be a valuable addition to the currently available functional analysis toolbox.

MB-GAN

Microbiome data have instrinsic complex data properties. We use Generative Adversarial Network to simulate high-quality microbiome abundance data

RVTESTS

Fast, handy association test software for family/unrelated studies and quantitative/binary trait. Single variant test (score test, Wald test), burden tests (CMC, SKAT, VT) are implemented. It also facilitate meta-analysis.

checkVCF

Validation software for VCF file (used in lipid consortium)

SEQMINER

Fast annotation software, and efficiently load and process VCF files. This software is integrated into EPACTS

QPLOT

Quality check and visualize sequence alignment files

LASER

Ancestry inference using off-target reads (suitable for target/exome sequencing)

Polymutt

Family-based genotype caller

ThunderVCF

Low-coverage genotype calling using Hidden Markov-chain Model (HMM) [Developing]

Selected Publications

For a complete list of publication, please visit PubMed

Our Team

My lab members includes doctoral students, staff members, and adminitration support members. We shared resources with the Quantitative Biomedical Research Center. We also collaborate closely within UT Southwestern campus and worldwide. We are active in seeking potential doctoral students, post-docs, and staff members

Jiwoong Kim

Computational Biologist

Wen Fan

Doctoral student

Art Taychameekiatchai

Doctoral student

Shuheng Gan

Computational Biologist

Suzette Palmer

Doctoral Student

Ruheng Wang

Doctoral Student

Jonathan Wang

Intern Student

Ziyun Zhou

Visiting Scholar

Shuang Jiang

Alumni

Yiqing Wang

Alumni

Sen Yang

Alumni

Contact

Location

5323 Harry Hines Blvd. Ste. H9.124
Dallas, TX 75390-8821

Email

Xiaowei.Zhan@UTSouthwestern.edu

Office

+1 (214) 648-5194

Fax

+1 (214) 648-1663