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Data Visualization for Genome Biology
Master genome data visualization with hands-on labs using web tools and R. Learn biological data viz paradigms and best practices.
Master genome data visualization with hands-on labs using web tools and R. Learn biological data viz paradigms and best practices.
Dive into the world of biological data visualization with this comprehensive course. Led by Nicholas James Provart, it covers theoretical aspects through mini-lectures and practical skills via hands-on labs. Using both web-based tools and R, students of all computer skill levels can benefit. The course explores common biological data visualization paradigms, chart types, and the importance of context in data presentation. Learn to visualize gene expression data, use Gene Ontology for data interpretation, and explore biological networks. Gain skills in dimensionality reduction methods and logic diagrams for large datasets. This course is ideal for biologists looking to enhance their data visualization capabilities in the era of next-generation sequencing.
4.7
(19 ratings)
2,188 already enrolled
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What you'll learn
Understand common biological data visualization paradigms and chart types
Learn to use R and web-based tools for creating effective visualizations
Master techniques for visualizing gene expression data and biological variation
Explore Gene Ontology and its application in data interpretation
Develop skills in analyzing and visualizing biological networks
Learn dimensionality reduction methods for large genomic datasets
Skills you'll gain
This course includes:
3 Hours PreRecorded video
12 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 6 modules in this course
This course introduces students to data visualization techniques for genome biology. It covers theoretical aspects through mini-lectures and practical skills via hands-on labs using both web-based tools and R. The curriculum includes common biological data visualization paradigms, chart types, and the importance of context in data presentation. Students learn to visualize gene expression data, use Gene Ontology for data interpretation, and explore biological networks. The course also covers dimensionality reduction methods and logic diagrams for large datasets, preparing students for the challenges of big data in genomics.
Module 1
Module 1 · 2 Hours to complete
Module 2
Module 2 · 2 Hours to complete
Module 3
Module 3 · 2 Hours to complete
Module 4
Module 4 · 2 Hours to complete
Module 5
Module 5 · 2 Hours to complete
Module 6
Module 6 · 2 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructor
Leader in Plant Cyberinfrastructure and Bioinformatics Research
Nicholas Provart, a professor of Plant Cyberinfrastructure and Systems Biology at the University of Toronto, earned his Ph.D. from the Free University of Berlin in 1996. After co-founding a plant biotechnology company in Germany, he joined Syngenta’s Torrey Mesa Research Institute in San Diego, where he analyzed arabidopsis gene expression. Since 2002, he has developed online bioinformatic resources like the Bio-Analytic Resource, widely used by plant researchers. His lab also tests plant stress response hypotheses. Dr. Provart has held leadership roles in bioinformatics education, genome biology programs, and international Arabidopsis research committees.
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4.7 course rating
19 ratings
Frequently asked questions
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