Introduction to AnVIL (The Course)
Course Info
This course includes 2 attempts.
Description
AnVIL is a cloud based ecosystem that colocates high value data sets with commonly used bioinformatics tools in a secure environment.
By providing access to data and tools in a secure environment that can scale to meet the computational needs of researchers, AnVIL eliminates logistical concerns and allows researchers to focus on biological problems. Providing access to such a platform with tens of thousands of human genome sequences will allow the genomics community to ask new questions that were not logistically feasible before.
This course is designed to be a brief introduction to cloud computing and the AnVIL platform. Learn how this new resource can help you access data, scale your computing resources, and democratize access to genomic data science.
Learning objectives
After taking this course you will be able to:
- Understand the components of the AnVIL computing platform
- Define cloud computing
- Understand the components of cloud costs
- Explain example workflows and how they run on AnVIL
Things you need to do this course
This course is designed for people with limited background in genomics and genomic data science and so is a good introduction for anyone looking to understand cloud computing for genomics and the AnVIL platform. The only requirements are:
- A computer with a web browser and internet connection
- The ability to type and follow instructions.
How you will be graded
The course has a series of short quizzes, one for each lesson. You will get two attempts at each quiz and your best score for each quiz will count toward your final score. If you receive more than 70% of the points across all quizzes you will pass. If you receive more than 90% of the points across all quizzes you will pass with honors. You get two attempts at the class with each class purchase.
Course Content License
All course content for this course is licensed CC-BY
Course Material
- Welcome
- What is AnVIL
- Learning Objectives
- Lesson
- What is AnVIL Quiz2 attempts allowed
- Cloud Computing
- Learning Objectives
- Lesson
- Cloud Computing Quiz2 attempts allowed
- Cloud Costs
- Learning Objectives
- Lesson
- Cloud Costs Quiz2 attempts allowed
- Use Case - GATK
- Learning Objectives
- Lesson
- Use Case - GATK Quiz2 attempts allowed
- Use Case - GWAS
- Learning Objectives
- Lesson
- Use Case - GWAS Quiz2 attempts allowed
- Use Case - eQTL
- Learning Objectives
- Lesson
- Use Case - eQTL Quiz2 attempts allowed
Instructors
Frederick Tan is on the Bioinformatics Research Faculty at Carnegie Institution, Department of Embryology, and an Adjunct Assistant Research Scientist at Johns Hopkins University, Department of Biology. His educational activities include the Practical Genomics Workshop, Quantitative Biology Bootcamp, C-MOOR, and AnVIL Outreach.
Jeff is Chief Data Officer, Vice President, and J Orin Edson Foundation Chair of Biostatistics at the Fred Hutchinson Cancer Center. Previously, he was a professor of Biostatistics and Oncology at the Johns Hopkins Bloomberg School of Public Health and co-director of the Johns Hopkins Data Science Lab. His group develops statistical methods, software, data resources, and data analyses that help people make sense of massive-scale genomic and biomedical data. As the co-director of the Johns Hopkins Data Science Lab he helped to develop massive online open programs that have enrolled more than 8 million individuals and partnered with community-based non-profits to use data science education for economic and public health development. He is a Fellow of the American Statistical Association and a recipient of the Mortimer Spiegelman Award and Committee of Presidents of Statistical Societies Presidential Award.
Sarah Wheelan is an associate professor of Oncology and Molecular Biology and Genetics at the Johns Hopkins University School of Medicine, and has an appointment in the department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. She is a founder and co-director of the Center for Computational Genomics and a co-director of the Experimental and Computational Genomics Core.
Kai Kammers is an Assistant Professor of Oncology in the Division of Biostatistics and Bioinformatics at the Johns Hopkins University School of Medicine. His work is fundamentally motivated by applications to real-life genomic research questions through close collaborations involving researchers from a variety of scientific backgrounds.
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