The long-term research goal of the Sirota Lab is to develop integrative computational methods and apply these approaches in the context of disease diagnostics and therapeutics. We are specifically interested in leveraging and integrating different types of omics and clinical data to better understand the role of the immune system in disease. We are particularly interested in developing computational methods and using them to understand immune tolerance in the context of autoimmune disease and non-response (pregnancy, organ transplant, cancer). The laboratory is funded by several National Institutes of Health including NIA, NLM, NIAMS, Pfizer, March of Dimes and the Burroughs Wellcome Fund.
The Sirota Lab is part of the Department of Pediatrics and the Institute for Computational Health Sciences at UCSF.
We are located in Mission Hall on the 4th floor, 550 16th Street, San Francisco, CA.
With the advent of genotyping and whole genome sequencing technologies, more and more omics data is becoming available for integrative analysis and provides an opportunity to ask new questions about disease. We are interested in leveraging the genetic data across phenotypes to elucidate the;genetic architecture of disease with a special interest in autoimmune diseases (Plos Genetics 2009). Furthermore we are interested in leveraging and applying next-generation sequencing technologies in order to better understand the role of the immune system in disease (JCI 2012, STM 2014). This work is currently funded by National Institute of Arthritis and Musculoskeletal and Skin Diseases at NIH and Pfizer.
We are interested in using publicly available gene expression and genetics data to identify novel biomarkers and therapeutic strategies for cancer. We are actively working on developing computational approaches for target identification, therapeutic prediction and selection of validation models (CPT 2015, BMC Med Genomics 2015, PSB 2015). We have a special interest in studying the tumor microenvironment and the interaction of immune cells in the context of cancer (Nature Communications, 2015).
The identification of novel disease indications for approved drugs, or drug repositioning, offers several advantages over traditional drug development. The traditional paradigm of drug discovery is generally regarded as protracted and costly, with studies showing that it takes approximately 15 years and over $1 billion to develop and bring a novel drug to market. The repositioning of drugs already approved for human use mitigates the costs and risks associated with early stages of drug development, and offers shorter routes to approval for therapeutic indications. We are interested in leveraging publicly available data to carry out data-driven computational drug repositioning (STM 2011, JID 2016). This work is currently funded by the National Institue of Aging at NIH.
Each year, 15 million babies (representing 10% of the world’s births) are born preterm, defined as before the 37th week of gestation. Survival for most children born preterm has improved considerably, but surviving children remain at increased risk for a variety of serious complications, many of which contribute to lifelong challenges for individuals and their families, as well as to burdensome economic costs to society. The exact mechanism of spontaneous preterm birth is unknown, though a variety of social, environmental, and maternal factors have been implicated in its cause. We are in particular interested in applying computational integrative methods to investigate the role of the immune system in pregnancy and elucidating genetic, environmental and clinical determinants of preterm birth. This work is funded by the National Library of Medicine at NIH, March of Dimes and The Burroughs Wellcome Fund.
Marina is currently an Assistant Professor at the Institute for Computational Health Sciences at UCSF. Her research interests lie in developing computational integrative methods and applying these approaches in the context of disease diagnostics and therapeutics.
Aolin obtained her PhD in Epidemiology from UCLA in 2016. Her research interests lie in developing computational approaches to study the role of the environment in pregnancy outcomes.
Ali's research interests include using applying transcriptomic meta-analysis and computational drug repositioning approaches to identify new therapeutics for late onset Alzheimer's disease.
Silvia completed her PhD in Statistical Genetics at the Spanish National Cancer Research Centre jointly with the University of Liege. Her current research interests lie in the development of advanced statistical approaches to integrate omics and clinical data for organ transplantation.
Leandro is a Bioinformatics Scientist at the Gladstone Institutes. His research project is using integrative methods to identify novel therapeutic targets and diagnostic markers for Parkinson's Disease using omics data and is a collaboration with the Finkbeiner Lab.
Dmitry obtained his PhD in Theoretical Physics from USC. His is developing applying computational approaches to genomic data in the context of autoimmunity.
Idit has a PhD in computational biology from the Technion, Haifa, Israel, where she studied epigenetics, trancription and splicing co-regulation in the human gene expression pathways. Her current focus is in meta anaylsis and computational integrative methods of human microbiome towards better understanding of preterm birth.
Hongtai's main research interest lies in the interface between statistical analyses and health/environmental decision making. He is currently utilizing health and environmental data to assess and understand potential associations between adverse pregnancy outcomes and environmental exposures as part of PTBi.
Kat is a BMS gradauate student at UCSF who is interested in developing and applying computational methods to understand cancer heterogeneity and experimental models.
Brian graduated Cum Laude from UCLA as a double major in physics and applied mathematics and went on to obtain his doctorate in physics from the University of Illinois. He joined the Sirota Lab with the goal of learning more about data science applications to biology and medicine.
Ishan obtined his BS in Chemical Biology from UC Berkeley in 2017. He is spending a year in the Sirota lab developing computational integrative approaches to study rheumatic disease before starting medical school at Mt. Sinai next year.
Manish obtined his BS John Hopkins in 2018. He is spending a year in the Sirota lab developing computational integrative approaches to study Alzheimer's disease before starting medical school.
This course provides an overview of computational methods used in the analysis of immunological data, including transcriptomics, next generation sequencing, cytometry and CyTOF. We will provide a theoretical framework but focus on real applications.
The first hour of each session was a lecture format that was livestreamed and archived here, followed by an intimate disucssion session with the registered students. This course was co-directed with Matt Spitzer.
In collaboration with Stanford University, would like to invite you to join us for a Computational Immunology Seminar Series this summer. The seminars will be hosted at Stanford and interactively streamed at UCSF in Mission Hall. Click here to subscribe to our updates about the lectures. We have an array of exciting speakers lined up and hope you will join us!
The laboratory of Dr. Marina Sirota at UCSF is seeking highly motivated investigators to develop and study novel approaches in translational bioinformatics, or the application of analytic and interpretive methods to optimize the transformation of genomic and clinical data into precision medicine. We are currently recruiting investigators interested in focusing on using omics and integrative approaches to study pregnancy outcomes with a focus on preterm birth.
Ideal candidates will have an M.D. and/or Ph.D. with a strong background in bioinformatics, computational biology, biostatistics, and genomics, and a great publication record.
Strong problem-solving skills, creative thinking, and the ability to build new software tools as needed are required. Applicants must possess good communication skills and be fluent in both spoken and written English. A background in molecular biology or medicine or pharmacology will be a strong plus. Prior experience with genetic, microarray, drug, or clinical databases, text-mining, knowledge representation, or parallel computing platforms is a plus. This exciting work will be guided by multidisciplinary collaborations with top scientists in neonatology, genetics, environmental science, immunology and microbiome research at UCSF.
To apply, please send your CV, a brief statement of research interests, and contact information for three references in one PDF document to email@example.com.
The Sirota Lab has openings each quarter for rotating students.
What you will learn:
We strongly recommend you apply through the UCSF high school research summer program . Exceptional applicants outside this program will be considered.
We accept students through the UCSF Summer Internship Program. Exceptional candidates outside the program will be considered, especially if the applicants have fellowship funding and a track-record of publications in biomedical informatics.