460-4143/01 – Bioinformatics - algorithms and data analysis (BAAD)
| Gurantor department | Department of Computer Science | Credits | 4 |
| Subject guarantor | Ing. Michal Vašinek, Ph.D. | Subject version guarantor | Ing. Michal Vašinek, Ph.D. |
| Study level | undergraduate or graduate | Requirement | Optional |
| Year | 2 | Semester | winter |
| | Study language | Czech |
| Year of introduction | 2022/2023 | Year of cancellation | |
| Intended for the faculties | FEI | Intended for study types | Follow-up Master |
Subject aims expressed by acquired skills and competences
The graduate of the course will gain the following knowledge and skills:
theoretical foundations of bioinformatics,
implementation and application of selected methods for DNA, RNA and protein analysis.
Teaching methods
Lectures
Tutorials
Summary
In the course, students will get acquainted with the basic approaches, methods and algorithms in bioinformatics.
Lectures will provide the necessary amount of theory so that it can be applied in students' independent work on exercises.
The exercises will offer a space for discussing the issue, a demonstration of practical tasks and exercises on simple assignments.
Compulsory literature:
1) Wing-Kin Sung. Algorithms in Bioinformatics: A Practical Introduction. Chapman & Hall/CRC Mathematical & Computational Biology. 2009
2) Arthur Lesk. Introduction to Bioinformatics. Oxford University Press, 2014.
3) Fatima Cvrčková. Úvod do praktické bioinformatiky. 1. vyd. Praha: Academia, 2006.
4) Pierre Baldi;G. Wesley Hatfield. DNA Microarrays and Gene Expression: From Experiments to Data Analysis and Modeling. Cambridge University Press 2002.
Recommended literature:
1) Caroline St. Clair, Jonathan E. Visick. Exploring Bioinformatics: A Project-Based Approach. Jones & Bartlett Learning, 2013.
Way of continuous check of knowledge in the course of semester
The course will be successfully completed by awarding a graded credit. The student will work independently on an assigned tasks/project. The solution of the project will be checked during semester. The final evaluation of all achieved results will be done at the end of semester.
E-learning
Other requirements
It is assumed that the student has a good knowledge of programming in C, C++, C# or Python.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
Lectures:
1) Introduction to the principles of functioning of organisms at the DNA level
2) Sequence similarity
3) Data structures
4) Alignment
5) Genome assembly
6) Algorithms for searching in biological databases
7) Prediction of genes
8) Principles of technologies in the analysis of biological data
9) Detection of variants
10) Gene expression
11) Statistical methods for gene expression analysis
12) Phylogenetic data analysis
Exercises in the computer lab:
1) Practicing basic concepts for working with DNA
2) Practicing algorithms for calculating sequence similarity
3) Algorithms for construction of suffix trees
4) Practicing algorithms for global and local alignment
5) Practicing algorithms for genome assembly
6) Access to BLAST databases and practice of algorithms for searching in biological databases
7) Practicing the concepts needed for gene prediction
8) Getting acquainted with various representations of biological data
9) Practicing the concepts needed for the detection of variants
10) Practicing the concepts needed for gene expression
11) Practice of statistical analysis of gene expression data
12) Practicing algorithms for creating evolutionary trees
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction