Comparative and Population Genomics

Wendy Huang

Faculty Status
Active
Title
Assistant Professor
Email
wendyjmhuang@health.ucsd.edu
Phone
(858) 246-2258
Track(s)
Bioinformatics and Systems Biology
Brief Research Description
To assess the contributions of RNA-binding proteins and their associated non-coding RNAs to host immune homeostasis and autoimmune pathologies.
Lab Description

We strive to better understand how diverse members of the non-coding RNA family and their RNA binding protein partners, through their ability to modulate gene expression in the nucleus, contribute to immune pathologies in mouse models of colitis, multiple sclerosis, and cancers. Deeper understanding of how immune gene expression programs are controlled will facilitate development of new intervention strategies against inflammatory diseases.

Andrew Allen

Faculty Status
Active
Title
Associate Professor
Email
aallen@ucsd.edu
Phone
(858) 200-1826
Track(s)
Bioinformatics and Systems Biology
Brief Research Description
Ecological, comparative, and functional genomics of marine phytoplankton, Systems biology of microalgae, Genome evolution and evolutionary origins of metabolic function in marine microbes, Transcriptional regulatory networks, Metagenomics, Metatranscriptomics, Microbes, Evolution

Chi-Hua Chen

Faculty Status
Active
Title
Associate Professor
Email
chc101@ucsd.edu
Phone
(858) 822-3865
Track(s)
Bioinformatics and Systems Biology
Department
BISB Research Area(s)
Brief Research Description
Neuroimaging genetics, neuropsychiatric disorders
Lab Description

We have a variety of projects ranging from brain mapping to derive optimal brain atlases, integrated omic analyses to identify genetic underpinnings of the brain, to precision medicine approaches for drug response prediction and drug target identification.

Amit Majithia

Faculty Status
Active
Title
Assistant Professor of Medicine and Pediatrics, Division of Endocrinology
Email
amajithia@ucsd.edu
Phone
(858) 822-0727
Track(s)
Bioinformatics and Systems Biology
Biomedical Informatics
Department
Brief Research Description
Insulin resistance, functional genomics, pharmacogenetics, cardiometabolic disease, diabetes
Lab Description

Our goal is to identify genes causing insulin resistance in humans in order to find new therapeutic targets for diabetes and cardiometabolic diseases. Our approach to discovery is grounded in human genetics, clarified through systematic, high throughput experimentation in human cells, and calibrated by its relevance to clinical disease. We use massively parallel genome engineering to re-create mutations identified in patients and develop high-throughput assays to interrogate function in human cell models. We apply bioinformatics and statistics to make sense of this data integrating 1) human mutations, 2) cellular function, and 3) metabolic/glycemic phenotypes of the individuals who harbor them. Using this approach, we have discovered novel missense mutations that greatly increase risk for type 2 diabetes. As a complementary aim towards precision medicine, we develop tools for clinical genome interpretation powered by high-throughput experimental data.

Albert Hsiao

Faculty Status
Active
Title
Associate Professor in Residence
Email
hsiao@ucsd.edu
Phone
(858) 657-6650
Track(s)
Bioinformatics and Systems Biology
Biomedical Informatics
Department
BISB Research Area(s)
Brief Research Description
Artificial Intelligence, Cardiovascular, COVID-19, x-ray, CT, MRI, Point-of-care Ultrasound

Elizabeth Winzeler

Faculty Status
Active
Title
Professor
Email
ewinzeler@ucsd.edu
Phone
(858) 822-3339
Track(s)
Bioinformatics and Systems Biology
Department
Brief Research Description
My laboratory uses big data approaches to solve problems in global health. The lab also offers training in cheminformatics and chemical genomics.

Melissa Gymrek

Faculty Status
Active
Title
Assistant Professor
Title 2
Assistant Professor
Email
mgymrek@ucsd.edu
Phone
(858) 822-2496
Track(s)
Bioinformatics and Systems Biology
Department 2
BISB Research Area(s)
Brief Research Description
Contribution of complex genetic variation to human phenotypes
Lab Description

Our overall goal is to understand complex genetic variants that underlie human disease. We are particularly interested in repetitive DNA variants known as short tandem repeats (STRs) as a model for complex variation. Our work focuses on developing computational tools for analyzing and visualizing complex variation from large-scale sequencing data and applying these tools to learn about the contribution of repetitive variation to human disease.

Rob Knight

Faculty Status
Active
Title
Professor
Title 2
Affiliate Faculty
Email
rknight@ucsd.edu
Track(s)
Bioinformatics and Systems Biology
Department
BISB Research Area(s)
Brief Research Description
Human microbiome, microbial community ecology, multi-omics analyses
Lab Description

The Knight lab has broad interests in the human microbiome, the collection of trillions of microbes that inhabits our bodies, especially in developing techniques to read out these complex microbial communities and use the resulting data to understand human health, links between humans and the environment, and to prevent and cure disease. We offer a fast-paced environment with many collaborative opportunities on different projects.

Graham McVicker

Faculty Status
Active
Title
Assistant Professor
Email
gmcvicker@salk.edu
Phone
(858) 453-4100
Track(s)
Bioinformatics and Systems Biology
Brief Research Description
Human genetic variation, chromatin and gene regulation in immune cells
Lab Description

The McVicker laboratory aims to understand how chromatin state and organization are encoded by the human genome. Our approach to this problem is to exploit naturally occurring human genetic variation to identify sequence variants that disrupt chromatin function. We are currently focused on chromatin within immune cells and we are also interested in how variants that affect chromatin and gene regulation lead to disease risk. The problems that we work on often require the development of sophisticated computational and statistical methods that can extract subtle signals from noisy experimental data.