Computational analysis of genetic disease mechanisms using single cell genomics
Gabriel Loeb, Professor
Medicine, UCSF
Applications for Spring 2025 are closed for this project.
I am a scientist and physician investigating molecular mechanisms underlying human disease--with a particular focus on kidney disease. My laboratory integrates computational approaches with human genetics and advanced experimental models to discover genetic mechanisms of disease. We have a particular interest in understanding the molecular basis of kidney disease, but the approaches we develop and use are widely applicable to human disease.
Students in this project will learn to independently analyze single cell genomics datasets to answer important questions in disease biology and human genetics including:
1) Which genes are disrupted by disease-causing genetic variants?
2) Which genes are good therapeutic targets?
3) During what periods of life do disease genes cause disease?
4) What cell types are affected by disease-causing genetic variants?
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Please include in the application
(1) Your career plan
(2) Schedule of hours you can commit to performing research in the lab and how often you would be able to commute to UCSF Mission Bay. Much of this work can be performed remotely, but you will benefit from spending time in the laboratory at UCSF Mission Bay.
(3) Computational experience--particularly experience with R, Python, working on HPC clusters, bash scripting
(4) Prior research experience if any
Role: Analysis of single cell genomics data.
We are a young and growing laboratory, allowing me to work directly with students, but you will also interact with other students and postdocs in the laboratory.
Experience acquired during the training will include:
1) Understanding of single cell genomics and its analysis.
2) Visualization of single cell genomics data.
3) Understanding open computational challenges in single cell genomics and human genetics.
4) Understanding how gene expression is regulated by the 99% of the human genome that does not encode proteins.
5) Understanding open questions in the human genetics of disease.
Qualifications: Experience working in python or R is required.
Experience working in R, on HPC clusters, and with bash scripting are desired by not essential.
Hours: to be negotiated
Off-Campus Research Site: UCSF Mission Bay, Cardiovascular Research Institute, 555 Mission Bay Blvd S
Biological & Health Sciences