C3760 AI in structural bioinformatics

Faculty of Science
Autumn 2025

The course is not taught in Autumn 2025

Extent and Intensity
1/1/0. 3 credit(s). Type of Completion: k (colloquium).
In-person direct teaching
Teacher(s)
doc. RNDr. Radka Svobodová, Ph.D. (lecturer)
RNDr. Tomáš Raček, Ph.D. (assistant)
Guaranteed by
doc. RNDr. Radka Svobodová, Ph.D.
National Centre for Biomolecular Research – Faculty of Science
Contact Person: RNDr. Tomáš Raček, Ph.D.
Supplier department: National Centre for Biomolecular Research – Faculty of Science
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The course is centered in providing practical skills in data management and data visualization using R for students and researchers, leveraging on the availability and applicability of artificial intelligence (AI) tools focused principally on GitHub Copilot. The objectives of the course are: improve the data manipulation skills using tidyverse package ; develop data visualization knowledge using ggplot2 package; integrate visualization into reports and web applications; application of AI tools in data analysis.
Learning outcomes
After finishing the course, the students will be able to manipulate data effectively using the tidyverse package, with the ability to get clean, analysis-ready datasets from raw data. The participant will also be able to create visualizations of the data using the ggplot2 package, and to use the obtained plots into dynamic reports and web applications using RMarkdown and Shiny packages respectively. Ultimately, participants will develop the skills to write efficient R code, augmented by AI tools, to increase code reproducibility, efficiency, and troubleshooting.
Syllabus
During this course, we will go through the main features of tidyverse package for data manipulation, such as performing mathematical operations, filtering, table joining, reshaping, with the objective to prepare cleaned datasets from raw data. Following, we will discuss the usage of the ggplot2 package, its features and variables, the functions implied and the data structure required to perform the data visualization successfully. Finally, the data manipulation steps and data visualization will be coupled with the usage of RMarkdown and Shiny packages with the objective to check their advantages and disadvantages so they can be applied in real case scenarios depending on the requirements. The usage of AI tools will be focused on GitHub Copilot for reproducibility issues and its integration with RStudio, however other alternatives will be introduced to the participant, and all the sections will include an explanation on a predefined code to achieve the ultimate results (data management, visualization or reporting) and then it will be shown how to use AI tools to obtain comparable results by writing clear and efficient prompts.
Literature
  • Moon, K. W. (2017). Learn ggplot2 using shiny app. Springer.
  • Quicke, D. L., Butcher, B. A., & Welton, R. A. K. (2020). Practical R for Biologists: An Introduction. CABI.
  • BECKERMAN, Andrew P. and Owen L. PETCHEY. Getting started with R : an introduction for biologists. 1st ed. Oxford: Oxford University Press, 2012, x, 113. ISBN 9780199601615. info
  • YAU, Nathan. Visualize this : the FlowingData guide to design, visualization, and statistics. Indianapolis, Indiana: Wiley, 2011, xxvi, 358. ISBN 9780470944882. info
Teaching methods
Student projects, their presentations and discussions.
Assessment methods
Compulsory homework and practical final assignment.
Language of instruction
English
Further Comments
The course is taught annually.
The course is taught every week.
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