Bioinformatics Applications in Human Disease

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.

Mohit Jain

Faculty Status
Active
Title
Associate Professor
Email
mjain@ucsd.edu
Phone
(858) 822-5854
Track(s)
Bioinformatics and Systems Biology
Department
Brief Research Description
Human Biology, Metabolic Biochemistry, Integrative Biology, Mass Spectrometry, Metabolomics
Lab Description

Bringing together Disease Research, Analytical Chemistry, Computational Methods and Drug Discovery

Kit Curtius

Faculty Status
Active
Title
Assistant Professor
Email
kitcurtius@gmail.com
Track(s)
Bioinformatics and Systems Biology
Biomedical Informatics
Brief Research Description
Mathematical models of cancer evolution, optimization of cancer screening and personalized surveillance, epigenetic aging, translational risk prediction tools

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.

Niema Moshiri

Faculty Status
Active
Title
Assistant Teaching Professor
Email
a1moshir@ucsd.edu
Phone
(619) 993-1642
Track(s)
Bioinformatics and Systems Biology
Biomedical Informatics
Brief Research Description
Computational HIV epidemiology, phylogenetics, models of sequence and tree evolution, Massive Adaptive Interactive Text (MAIT) development
Lab Description

We are interested in developing scalable methods for performing epidemiological analyses of large viral (primarily HIV) sequence and phylogenetic datasets. Topics of interest include large-scale phylogenetic analyses, developing novel models of sequence and tree evolution, performing epidemiological simulation experiments, and developing methods for predicting epidemic outcomes.

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.

Lukas Chavez

Faculty Status
Active
Title
Assistant Professor
Email
lukaschavez@ucsd.edu
Track(s)
Bioinformatics and Systems Biology
Biomedical Informatics
Department
Brief Research Description
Chromatin and Gene Regulation in Childhood Cancers
Lab Description

The main objective of the Chavez laboratory is the molecular characterization of malignant childhood cancers in order to identify drug targets and improve treatment options. Our focus is mainly on pediatric brain tumors such as medulloblastoma, glioblastoma, and ependymoma. Recently, we have demonstrated how to leverage epigenetic information such as DNA methylation and enhancer profiling in pediatric brain tumors and normal human tissues to identify clinically relevant tumor subgroups, oncogenic enhancers, transcription factors, and pathways amenable to pharmacologic targeting. To reveal regulatory circuitries disturbed in childhood brain tumors, we generate and integrate public high-dimensional data from primary tumors and patient-derived cell lines. We are specifically interested in the analysis of somatic and germline DNA mutations, chromatin and DNA modifications, transcription factor binding, and gene expression.

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

Ferhat Ay

Faculty Status
Active
Title
Assistant Adjunct Professor
Email
ferhatay@lji.org
Phone
(858) 752-6612
Track(s)
Bioinformatics and Systems Biology
Biomedical Informatics
Department
Brief Research Description
Epigenetics, genomics, chromatin structure, 3D/4D genome/nucleome, statistical methods for analysis of Hi-C and HiChIP data, gene regulation in immune cells, cancer genomics
Lab Description

We are interested in the analysis and modeling of the three-dimensional chromatin structure from high-throughput sequencing experiments. We develop methods that are based in statistics, machine learning, optimization and graph theory to understand how changes in the 3D genome affect cellular outcome such as development, differentiation and gene expression. We have ongoing interests in the systems level analysis and reconstruction of regulatory networks, inference of enhancer-promoter contacts, predictive models of gene expression and integration of three-dimensional chromatin structure with one-dimensional epigenetic measurements in the context of cancer, malaria, asthma and several autoimmune diseases.