Structural variant discovery from long-read sequencing data on the cloud with Galaxy in Terra
- Regular Member - Free!
- Early Career Member - Free!
- Resident/Clinical Fellow Member - Free!
- Postdoctoral Fellow Member - Free!
- Graduate Student Member - Free!
- Undergraduate Student Member - Free!
- Emeritus Member - Free!
- Life Member - Free!
- Trainee Member - Free!
- Nonmember - $25
Growing evidence that structural variants (SVs) are responsible for many types of diseases and traits is fueling interest in taking a fresh look at different disease types using long-read sequencing. Although short-read technologies have long been cheaper and more readily available, long-read sequencing produces data that can yield significantly more accurate results for identifying SVs.
However, the large amounts of data and complexity of the computational methods involved can make it difficult for newcomers to access this exciting area of research, particularly in the context of the traditional computing environments that are provided by default to academic researchers.
In this workshop, we will guide you through an end-to-end SV identification journey using Galaxy, a platform designed to facilitate access to computational methods for researchers without a programming background. Specifically, we will use Galaxy in Terra, in the context of the NHGRI Genomic Data Science Analysis, Visualization and Informatics Lab-space (AnVIL). This cloud-based environment enables you to analyze large genomic datasets with familiar tools and reproducible workflows securely.
Through live demonstrations and interactive exercises, you will learn how to:
- Bring data into a project workspace in Terra
- Combine data (your own or controlled-access) with an open-access dataset
- Launch a Galaxy instance in Terra and run a complete workflow to identify SVs
- Visualize results and identify potentially pathogenic variants
The skills you will learn in this workshop will extend to other scientific use cases, datasets and tools beyond the examples shown.
Liz Kiernan, PhD
Senior Science Writer, Data Sciences Platform
Senior Science Writer, Data Sciences Platform, Broad Institute of MIT and Harvard
Liz Kiernan is a Senior Science Writer for the Broad Institute’s Data Sciences Platform. Prior to joining Broad, she was a postdoctoral fellow at the University of Wisconsin-Madison, studying the long-term effects of hypoxia on brain development and immune function in the lab of Dr. Jyoti Watters. Outside of her research activities, Kiernan passionately pursued science outreach and teaching in her local community, working with organizations like PEOPLE and Expanding Your Horizons to empower burgeoning young scientists. She completed her Ph.D. in Neuroscience from the University of Wisconsin-Madison in 2019 where she was awarded the Ruth L. Kirschstein National Research Service Award for research focusing on hypoxia-induced epigenetic changes in microglia, the resident immune cells of the brain. Kiernan received her M.S. in Biology (2012) and B.S. in Psychology/Pre-Med (2010) from the College of William and Mary.
Johns Hopkins University
Natalie Kucher is a Project Manager for the AnVIL Platform at Johns Hopkins University. Her focus is on outreach and training, efforts to diversify genomic data science, and supporting Galaxy on AnVIL. She spent 2 years at the National Human Genome Research Institute (NHGRI) where she supported the Computational Genomic Data Science Program and served as the Executive Secretary of the Genomic Data Science Working Group of Council. She received her B.S. in Biology from Davidson College in 2019.
Michael C. Schatz
Bloomberg Distinguished Professor of Computer Science and Biology
Johns Hopkins University
Michael Schatz is the Bloomberg Distinguished Professor of Computer Science and Biology at Johns Hopkins University, and co-lead of the NHGRI AnVIL platform. His research is at the intersection of computer science, biology, and biotechnology, and focuses on development of novel algorithms and systems for comparative genomics, human genetics, and personalized medicine. For this work, he is a recipient of the 2015 Alfred P. Sloan Foundation Fellowship, an 2014 NSF CAREER award, and since 2018 has been named a Clarivate Web of Science “Highly Cited Researcher” three times based on the publication of multiple papers that rank in the top 1% by citations at any institution in the world. Schatz received his Ph.D. and M.S. in Computer Science from the University of Maryland in 2010 and 2008, his B.S. in Computer Science from Carnegie Mellon University in 2000, and spent 5 years at the Institute for Genomic Research (TIGR) in between. More information is available on his lab website: http://schatz-lab.org