Informatics and Bioinformatics II

Data

Official data in SubjectManager for the following academic year: 2024-2025

Topic

Students will learn about the biological sequence analysis and get skills to handling and checking sequencing data. Topics overwieved: univariate and bivariate statistical methods, probability theory and statistical hypothesis testing. R and RStudio for Genomics software, finding restriction sites. primer design. Depositing new sequences into primary databases. Search using optimal alignment algorithms: web implementations. Maximizing signal to noise ratio. Filtering false positive hits: avoiding low complexity regions, repetitive sequences, vector contaminations.

Lectures

Practices

• 1. Introduction to univariate and bivariate statistical methods I. - Herczeg Róbert
• 2. Introduction to univariate and bivariate statistical methods II. - Herczeg Róbert
• 3. Introduction to probability theory and statistical hypothesis testing - Herczeg Róbert
• 4. Application of confidence, T-and Mann-Whitney tests - Herczeg Róbert
• 5. Basics of regression analysis I. - Herczeg Róbert
• 6. Basics of regression analysis II. - Herczeg Róbert
• 7. Introduction to survival analysis and Kaplan-Meier method - Herczeg Róbert
• 8. Introduction to the command line I. - Herczeg Róbert
• 9. Introduction to the command line II. - Herczeg Róbert
• 10. Basic bioinformatics phrases and file formats. - Herczeg Róbert
• 11. Introduction to bioinformatics I. - Herczeg Róbert
• 12. Introduction to bioinformatics II. - Herczeg Róbert
• 13. Intro to R and RStudio for Genomics I. - Herczeg Róbert
• 14. Intro to R and RStudio for Genomics II. - Herczeg Róbert
• 15. Basic bioinformatics softwares I. - Herczeg Róbert
• 16. Basic bioinformatics softwares II. - Herczeg Róbert
• 17. Basic bioinformatics softwares III. - Herczeg Róbert
• 18. Short versus long reads I. - Herczeg Róbert
• 19. Short versus long reads II. - Herczeg Róbert
• 20. IGV - Herczeg Róbert
• 21. Project Organization and Management for Genomics - Herczeg Róbert
• 22. Data management and reproducibility - Herczeg Róbert
• 23. Genomic data analysis and visualization in R - Herczeg Róbert
• 24. Introduction to HPC (High Performance Computing) - Herczeg Róbert
• 25. Building pipelines I. - Herczeg Róbert
• 26. Building pipelines II. - Herczeg Róbert
• 27. Runnig pipelines I. - Herczeg Róbert
• 28. Runnig pipelines II. - Herczeg Róbert

Seminars

• 1. The basics of biological sequence analysis: Handling and checking sequencing data. - Kunsági-Máté Sándor
• 2. The basics of biological sequence analysis: Handling and checking sequencing data. - Kunsági-Máté Sándor
• 3. Contig assembly. Finding restriction sites. Primer design. Depositing new sequences into primary databases. - Kunsági-Máté Sándor
• 4. Contig assembly. Finding restriction sites. Primer design. Depositing new sequences into primary databases. - Kunsági-Máté Sándor
• 5. Sequence comparisons: Pairwise comparisons: dot-plot. Scoring systems, substitution matrices. PAM, BLOSUM matrices. - Kunsági-Máté Sándor
• 6. Sequence comparisons: Pairwise comparisons: dot-plot. Scoring systems, substitution matrices. PAM, BLOSUM matrices. - Kunsági-Máté Sándor
• 7. Pairwise sequence alignments: optimal alignment. Global and local alignment. - Kunsági-Máté Sándor
• 8. Pairwise sequence alignments: optimal alignment. Global and local alignment. - Kunsági-Máté Sándor
• 9. Similarity searches in sequence databases: Search using optimal alignment algorithms: web implementations. - Kunsági-Máté Sándor
• 10. Similarity searches in sequence databases: Search using optimal alignment algorithms: web implementations. - Kunsági-Máté Sándor
• 11. Maximizing signal to noise ratio. Filtering false positive hits: avoiding low complexity regions, repetitive sequences, vector contaminations. - Kunsági-Máté Sándor
• 12. Maximizing signal to noise ratio. Filtering false positive hits: avoiding low complexity regions, repetitive sequences, vector contaminations. - Kunsági-Máté Sándor
• 13. Heuristic search methods. Estimating the significance of a hit. Deciding which code to use. - Kunsági-Máté Sándor
• 14. Heuristic search methods. Estimating the significance of a hit. Deciding which code to use. - Kunsági-Máté Sándor
• 15. Basic bioinformatics softwares I. - Kunsági-Máté Sándor
• 16. Basic bioinformatics softwares II. - Kunsági-Máté Sándor
• 17. Basic bioinformatics softwares III. - Kunsági-Máté Sándor
• 18. Short versus long reads I. - Kunsági-Máté Sándor
• 19. Short versus long reads II. - Kunsági-Máté Sándor
• 20. Project Organization and Management for Genomics - Kunsági-Máté Sándor
• 21. Project Organization and Management for Genomics - Kunsági-Máté Sándor
• 22. Data management and reproducibility - Kunsági-Máté Sándor
• 23. Introduction to HPC (High Performance Computing) - Kunsági-Máté Sándor
• 24. Introduction to HPC (High Performance Computing) - Kunsági-Máté Sándor
• 25. Building pipelines I. - Kunsági-Máté Sándor
• 26. Building pipelines II. - Kunsági-Máté Sándor
• 27. Runnig pipelines I. - Kunsági-Máté Sándor
• 28. Runnig pipelines II. - Kunsági-Máté Sándor

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

John H. McDonald: Handbook of Biological Statistics, 2008

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

-

Exam topics/questions

Describe the univariate and bivariate statistical methods

Describe the probability theory and statistical hypothesis testing

T-and Mann-Whitney tests

Survival analysis and Kaplan-Meier method

Describe the R and RStudio for Genomics software

Comparison and differences: Short versus long reads

Basic rules of Project Organization and Management for Genomics

Biological sequence analysis: Handling and checking sequencing data.

Finding restriction sites. Primer design. Depositing new sequences into primary databases.

Similarity searches in sequence databases: Search using optimal alignment algorithms: web implementations.

Pairwise sequence alignments: optimal alignment. Global and local alignment.

Sequence comparisons: Pairwise comparisons: dot-plot. Scoring systems, substitution matrices. PAM, BLOSUM matrices.

Maximizing signal to noise ratio. Filtering false positive hits: avoiding low complexity regions, repetitive sequences, vector contaminations.

Heuristic search methods. Estimating the significance of a hit. Deciding which code to use.

Examiners

Instructor / tutor of practices and seminars

• Herczeg Róbert
• Kunsági-Máté Sándor