Course detail
Practics of Bioinformatics
FEKT-BPC-PBIAcad. year: 2024/2025
The course is focused on practical application of basic bioinformatical analyses of DNA and amino acids sequences. Primarily, it is oriented on global, local and multi alignment algorithms and algorithms for RNA and protein sequence secondary structure prediction. The signal processing methods for genomic and proteomic data analyses are studied. Further, practical application of phylogenetic tree construction is applied on suitable dataset of DNA sequences. Students will learn how to analyse sequences in R programming language.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Entry knowledge
Rules for evaluation and completion of the course
Computer exercises are mandatory, properly excused missed lectures can be compensated individually after discussion with teacher.
Aims
The student is able to:
- find protein-coding DNA sequences in GenBank database and load the data in desired format
- find protein sequence, which is coded by the DNA sequence, in Uniprot database
- find coding regions in DNA sequences
- analyse sequences in R
- use alignment online tools and suitably choose scoring parameters according data type
- program algorithms for alignment with afinne penalty
- predict secondary structure of protein sequences with online tools
- predict positive selection in genes
- program calculation of DNA spectrograms
- construct phylogenetic tree from DNA sequences by online tools
Study aids
Prerequisites and corequisites
Basic literature
Recommended reading
Elearning
Classification of course in study plans
- Programme BPC-BTB Bachelor's 3 year of study, summer semester, compulsory
Type of course unit
Exercise in computer lab
Teacher / Lecturer
Syllabus
2. Processing of geonome sequences according to the principal statistical standards.
3. Comparison of sequences. Levelling of sequences. Coincidence rate.
4. Seeking patterns in sequences
5. Non-linear methods for comparison of samples, method of dynamic time warping
6. Hidden Markov models in resolution methods
7. Seeking patterns by means of non-linear methods for classification problems.
8. Cluster analysis using non-linear comparative approaches
9. Statistical evaluation of classification procedures, volumes of processed data.
10. Expert system as a classifier.
11. Presentation of individual work.
12. Presentation of individual work.
13. Test.
Elearning