Course detail

Practics of Bioinformatics

FEKT-BPC-PBIAcad. year: 2021/2022

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

Czech

Number of ECTS credits

3

Mode of study

Not applicable.

Guarantor

Ing. Helena Vítková, Ph.D.

Department

Department of Biomedical Engineering (UBMI)

Learning outcomes of the course unit

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

Prerequisites

Student should have knowledge equivalent to completion of the course ABIN. Student should be able to work with Matlab or other programming language. Student have to know the basics of molecular biology of nucleotide and protein sequences and understand to principles of global and local sequence alignment and principle of protein secondary structure prediction.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Teaching methods depend on the type of the course unit as specified in the article 7 of BUT Rules for Studies and Examinations.

Assesment methods and criteria linked to learning outcomes

Student can get maximally 30 points for programming, maximally 30 points for practical work and maximally 40 points for theoretical test. It is necessary to get minimally 15 points for the programming, minimally 20 points for the theoretical test and minimally 50 points in sum to successfully pass the course.

Course curriculum

1. Basic programming in R.
2. Biostrings library.
3. Regular expressions and data formats.
4. Exon searching.
5. Sequence alignment with affine penalty.
6. Multialignment.
7. Phylogenetic tree construction.
8. Prediction of positive selection.
9. RNA structure prediction.
10. Protein structure prediction.

Work placements

Not applicable.

Aims

The goal of this course is to teach the students how to search in the basic genomic and proteomic databases like GenBank and Uniprot, analyse data from the databases with commonly used bioinformatic online tools, and the student are also teached how to program some basic bioinformatics algorithms in R programming language.

Specification of controlled education, way of implementation and compensation for absences

Computer exercises are mandatory, properly excused missed lectures can be compensated individually after discussion with teacher.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Cvrčková F: Úvod do praktické bioinformatiky, Academia, 2006 (CS)

Recommended reading

Not applicable.

Elearning

eLearning: currently opened course

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

39 hod., compulsory

Teacher / Lecturer

Ing. et Ing. Jana Schwarzerová, MSc
Ing. Vojtěch Bartoň

Syllabus

1. Bioinformatic data. Genome sequences of organisms., Databases accessible to the public. Reading, data imaging in Matlab. Basic problems from bioinformatics solved in the programming environment Matlab
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

eLearning: currently opened course