Informatics and Bioinformatics I

Data

Official data in SubjectManager for the following academic year: 2025-2026

Topic

Students will learn the fundamental concepts of bioinformatics and how to plan scientific research effectively. They will explore statistical methods for data analysis, including selecting appropriate tests, applying T-tests for comparing groups, and using ANOVA for analyzing variance. They will also gain insights into correlation, regression, and predictive modeling. The course will emphasize the importance of effective data visualization for clear communication of results. Students will learn to access and analyze high-throughput biological data using the GEO database. Additionally, they will explore pathway analysis tools like Ingenuity Pathway Analysis (IPA) to interpret biological networks and understand the regulatory roles of miRNAs in gene expression.

Lectures

Practices

• 1. Introduction to Bioinformatics - Ádám Zoltán Mihály
• 2. Introduction to Bioinformatics - Ádám Zoltán Mihály
• 3. Scientific Research Planning - Ádám Zoltán Mihály
• 4. Scientific Research Planning - Ádám Zoltán Mihály
• 5. Application of Statistics: Choosing Tests - Ádám Zoltán Mihály
• 6. Application of Statistics: Choosing Tests - Ádám Zoltán Mihály
• 7. Normal or Not? Unveiling Data Patterns with T-Tests - Garai Kitti
• 8. Normal or Not? Unveiling Data Patterns with T-Tests - Garai Kitti
• 9. ANOVA: Decoding Differences, One Variance at a Time - Garai Kitti
• 10. ANOVA: Decoding Differences, One Variance at a Time - Garai Kitti
• 11. Beyond Association: Correlation, Regression, and Predicting the Future - Garai Kitti
• 12. Beyond Association: Correlation, Regression, and Predicting the Future - Garai Kitti
• 13. Tests - Ádám Zoltán Mihály, Garai Kitti
• 14. Tests - Ádám Zoltán Mihály, Garai Kitti
• 15. Effective Data Visualization: Communicating Insights with Clarity - Ádám Zoltán Mihály
• 16. Effective Data Visualization: Communicating Insights with Clarity - Ádám Zoltán Mihály
• 17. Exploring the GEO Database: Accessing and Analyzing High-Throughput Data - Ádám Zoltán Mihály
• 18. Exploring the GEO Database: Accessing and Analyzing High-Throughput Data - Ádám Zoltán Mihály
• 19. From Theory to Practice: Applying Statistical Knowledge in Research - Ádám Zoltán Mihály
• 20. From Theory to Practice: Applying Statistical Knowledge in Research - Ádám Zoltán Mihály
• 21. Introduction to Ingenuity Pathway Analysis (IPA): Unravelling Biological Networks - Garai Kitti
• 22. Introduction to Ingenuity Pathway Analysis (IPA): Unravelling Biological Networks - Garai Kitti
• 23. Deciphering Biological Pathways: Core Analysis in IPA - Garai Kitti
• 24. Deciphering Biological Pathways: Core Analysis in IPA - Garai Kitti
• 25. Unveiling Regulatory Roles: Approaches to miRNA Data Analysis - Garai Kitti
• 26. Unveiling Regulatory Roles: Approaches to miRNA Data Analysis - Garai Kitti
• 27. Tests - Ádám Zoltán Mihály, Garai Kitti
• 28. Tests - Ádám Zoltán Mihály, Garai Kitti

Seminars

• 1. Introduction to Bioinformatics - Ádám Zoltán Mihály
• 2. Introduction to Bioinformatics - Ádám Zoltán Mihály
• 3. Scientific Research Planning - Ádám Zoltán Mihály
• 4. Scientific Research Planning - Ádám Zoltán Mihály
• 5. Application of Statistics: Choosing Tests - Ádám Zoltán Mihály
• 6. Application of Statistics: Choosing Tests - Ádám Zoltán Mihály
• 7. Normal or Not? Unveiling Data Patterns with T-Tests - Garai Kitti
• 8. Normal or Not? Unveiling Data Patterns with T-Tests - Garai Kitti
• 9. ANOVA: Decoding Differences, One Variance at a Time - Garai Kitti
• 10. ANOVA: Decoding Differences, One Variance at a Time - Garai Kitti
• 11. Beyond Association: Correlation, Regression, and Predicting the Future - Garai Kitti
• 12. Beyond Association: Correlation, Regression, and Predicting the Future - Garai Kitti
• 13. Tests - Ádám Zoltán Mihály, Garai Kitti
• 14. Tests - Ádám Zoltán Mihály, Garai Kitti
• 15. Effective Data Visualization: Communicating Insights with Clarity - Ádám Zoltán Mihály
• 16. Effective Data Visualization: Communicating Insights with Clarity - Ádám Zoltán Mihály
• 17. Exploring the GEO Database: Accessing and Analyzing High-Throughput Data - Ádám Zoltán Mihály
• 18. Exploring the GEO Database: Accessing and Analyzing High-Throughput Data - Ádám Zoltán Mihály
• 19. From Theory to Practice: Applying Statistical Knowledge in Research - Ádám Zoltán Mihály
• 20. From Theory to Practice: Applying Statistical Knowledge in Research - Ádám Zoltán Mihály
• 21. Introduction to Ingenuity Pathway Analysis (IPA): Unravelling Biological Networks - Garai Kitti
• 22. Introduction to Ingenuity Pathway Analysis (IPA): Unravelling Biological Networks - Garai Kitti
• 23. Deciphering Biological Pathways: Core Analysis in IPA - Garai Kitti
• 24. Deciphering Biological Pathways: Core Analysis in IPA - Garai Kitti
• 25. Unveiling Regulatory Roles: Approaches to miRNA Data Analysis - Garai Kitti
• 26. Unveiling Regulatory Roles: Approaches to miRNA Data Analysis - Garai Kitti
• 27. Tests - Ádám Zoltán Mihály, Garai Kitti
• 28. Tests - Ádám Zoltán Mihály, Garai Kitti

Reading material

Obligatory literature

Micheal J. Crawley: The R Book 2007

Peter Dalgaard: Introductory statistics with R 2002

Literature developed by the Department

Slides and notes of all lectures are available electronically.

Notes

Slides and notes of all lectures are available electronically.

Recommended literature

T. K. Attwood, D. Parry-Smith: Introduction to bioinformatics; Longman, 2001, ISBN 978-0582327887

Conditions for acceptance of the semester

No additional requirements.

Mid-term exams

Two tests written on the 7th and 14th weeks.

Making up for missed classes

Electronic materials provided.

Exam topics/questions

Introduction to Bioinformatics: What is bioinformatics, and how does it contribute to modern biological research? Provide an example of its application.

Scientific Research Planning: Describe the key steps in scientific research planning. Why is proper planning crucial for the success of a research project?

Application of Statistics: Choosing Tests: When analyzing experimental data, how do you determine whether to use a parametric or non-parametric test?

Normal or Not? Unveiling Data Patterns with T-Tests: Explain the assumptions of a t-test and describe a research scenario where it would be appropriate to use one.

ANOVA: Decoding Differences, One Variance at a Time: Compare one-way and two-way ANOVA. In what situations would you use each?

Beyond Association: Correlation, Regression, and Predicting the Future: Differentiate between correlation and regression analysis. How can regression be used for predictive modeling?

Effective Data Visualization: Communicating Insights with Clarity: What are the key principles of effective data visualization, and how do they impact the interpretation of scientific results?

Exploring the GEO Database: Accessing and Analyzing High-Throughput Data: What types of biological data can be retrieved from the GEO database, and how can this data be used in research?

Introduction to Ingenuity Pathway Analysis (IPA): Unraveling Biological Networks: How does Ingenuity Pathway Analysis (IPA) help in understanding biological pathways, and what types of data inputs are required for analysis?

Unveiling Regulatory Roles: Approaches to miRNA Data Analysis: Explain the role of miRNAs in gene regulation. How can bioinformatics tools be used to analyze miRNA-related pathways?

Examiners

Instructor / tutor of practices and seminars

• Ádám Zoltán Mihály
• Garai Kitti