Course detail
Programming in Bioinformatics
FEKT-MPA-PRGAcad. year: 2022/2023
The course is oriented to programming in bioinformatics area. It studies introduction to programming and alghoritms used for DNA and protein sequence analysis.
Guarantor
Offered to foreign students
The home faculty only
Learning outcomes of the course unit
Student is able to:
- Solve problems iteratively and recursively
- Evaluate the performance of algorithms
- Implement algorithms for searching (brute force, branch-and-bound, greedy algorithms)
- Implement algorithms for sequence alignment using dynamic programming with recursion
- Implement algorithms for learning hidden Markov models and their use
- Solve problems iteratively and recursively
- Evaluate the performance of algorithms
- Implement algorithms for searching (brute force, branch-and-bound, greedy algorithms)
- Implement algorithms for sequence alignment using dynamic programming with recursion
- Implement algorithms for learning hidden Markov models and their use
Prerequisites
The subject knowledge on the Bachelor's degree level is requested.
Co-requisites
Not applicable.
Recommended optional programme components
Not applicable.
Literature
Jones N.C., Pevzner P.A: An Introduction to Bioinformatics Algorithms. The MIT Press, 2004
Moorhouse M, Barry P: Bioinformatics Biocomputing and Perl: An Introduction to Bioinformatics Computing Skills and Practice. Wiley; 1 edition, 2004.
Chao K.-M., Zhang L.: Sequence Comparison. Springer-Verlag, 2009
Zaplatílek K, Doňar B: Matlab tvorba uživatelských aplikací, Technická literatura BEN, Praha 2004
Moorhouse M, Barry P: Bioinformatics Biocomputing and Perl: An Introduction to Bioinformatics Computing Skills and Practice. Wiley; 1 edition, 2004.
Chao K.-M., Zhang L.: Sequence Comparison. Springer-Verlag, 2009
Zaplatílek K, Doňar B: Matlab tvorba uživatelských aplikací, Technická literatura BEN, Praha 2004
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
Students have to obtain at least 50 points out of 100 in the sub-activities:
1. midterm test (max. 30 points),
2. final test (max. 30 points),
3. implementation of algorithms (min 20 points, max 40 points).
Individual activities check students' ability to implement algorithms in chosen programming language.
1. midterm test (max. 30 points),
2. final test (max. 30 points),
3. implementation of algorithms (min 20 points, max 40 points).
Individual activities check students' ability to implement algorithms in chosen programming language.
Language of instruction
English
Work placements
Not applicable.
Course curriculum
1. Basics of algorithmization.
2. Types of algorithms, recursion and iteration.
3. Regular expressions.
4. Sorting algorithms (greedy algoritmy).
5. Restriction mapping (exhaustive search).
6. Motive search (branch and bound algorithms).
7. Dynamic programming with recursion.
8. Algorithms for de novo genome assembly.
9. Markov models in bioinformatics.
10. Suffix trees.
2. Types of algorithms, recursion and iteration.
3. Regular expressions.
4. Sorting algorithms (greedy algoritmy).
5. Restriction mapping (exhaustive search).
6. Motive search (branch and bound algorithms).
7. Dynamic programming with recursion.
8. Algorithms for de novo genome assembly.
9. Markov models in bioinformatics.
10. Suffix trees.
Aims
The aim of the course is introduction to algorithms for analyzing DNA and protein sequences and their detailed analysis. Students program the discussed algorithms in programming environment R.
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.
Classification of course in study plans
- Programme MPC-BTB Master's, 1. year of study, winter semester, 5 credits, compulsory
Type of course unit
eLearning
eLearning: currently opened course