Course detail
Programming in Bioinformatics
FEKT-FPRGAcad. year: 2018/2019
The course is oriented to programming in bioinformatics area. It studies introduction to programming and alghoritms used for DNA and protein sequence analysis.
Language of instruction
Czech
Number of ECTS credits
6
Mode of study
Not applicable.
Guarantor
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 (local, global, backtrace)
- Implement algorithms for cluster analysis
- 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 (local, global, backtrace)
- Implement algorithms for cluster analysis
- 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.
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. test (max 30 points)
2. test (max 30 points)
programming (max 40 points, min 20 points).
1. test (max 30 points)
2. test (max 30 points)
programming (max 40 points, min 20 points).
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.
Work placements
Not applicable.
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 or Matlab.
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
Chao K.-M., Zhang L.: Sequence Comparison. Springer-Verlag, 2009 (EN)
Jones N.C., Pevzner P.A: An Introduction to Bioinformatics Algorithms. The MIT Press, 2004 (EN)
Moorhouse M, Barry P: Bioinformatics Biocomputing and Perl: An Introduction to Bioinformatics Computing Skills and Practice. Wiley; 1 edition, 2004. (EN)
Zaplatílek K, Doňar B: Matlab tvorba uživatelských aplikací, Technická literatura BEN, Praha 2004 (CS)
Jones N.C., Pevzner P.A: An Introduction to Bioinformatics Algorithms. The MIT Press, 2004 (EN)
Moorhouse M, Barry P: Bioinformatics Biocomputing and Perl: An Introduction to Bioinformatics Computing Skills and Practice. Wiley; 1 edition, 2004. (EN)
Zaplatílek K, Doňar B: Matlab tvorba uživatelských aplikací, Technická literatura BEN, Praha 2004 (CS)
Recommended reading
Not applicable.
Classification of course in study plans
Type of course unit
Lecture
13 hod., optionally
Teacher / Lecturer
Syllabus
1. Fundamentals of Algorithms tasks (recursive and iterative algorithms, complexity of algorithms].
2. Search Algorithms - Exhaustive Search: restriction mapping, searching themes.
3. Search Algorithms - Greedy algorithm: analysis of genetic changes with reversion and sorting method Breakpoints.
4. Dynamic programming (general description of the method, a global alignment, local alignment, optimal path, applications).
5. Cluster analysis of genomic data for analysis (basic principles, applications).
6. Hidden Markov models for analyzing genomic data (basic principles, applications).
2. Search Algorithms - Exhaustive Search: restriction mapping, searching themes.
3. Search Algorithms - Greedy algorithm: analysis of genetic changes with reversion and sorting method Breakpoints.
4. Dynamic programming (general description of the method, a global alignment, local alignment, optimal path, applications).
5. Cluster analysis of genomic data for analysis (basic principles, applications).
6. Hidden Markov models for analyzing genomic data (basic principles, applications).
Exercise in computer lab
52 hod., compulsory
Teacher / Lecturer
Syllabus
1. Laboratory introduction and organization of laboratory works.
2. Fundamentals of Algorithmics jobs.
3. Recursive and iterative algorithms, complexity of algorithms.
4. Exhaustive Search: restriction mapping.
5. Exhaustive Search: the search for motives.
6. Greedy algorithm: analysis of genetic changes with reversion
7. Analysis of genetic changes using Breakpoints.
8. Dynamic programming: a general description of the method, the global alignment.
9. Dynamic programming: local alignment, the optimal path.
10. Cluster analysis of genomic data for analysis (basic principles, applications).
11. Hidden Markov models for analyzing genomic data (basic principles, applications).
12. A complementary exercise.
13. Final test.
2. Fundamentals of Algorithmics jobs.
3. Recursive and iterative algorithms, complexity of algorithms.
4. Exhaustive Search: restriction mapping.
5. Exhaustive Search: the search for motives.
6. Greedy algorithm: analysis of genetic changes with reversion
7. Analysis of genetic changes using Breakpoints.
8. Dynamic programming: a general description of the method, the global alignment.
9. Dynamic programming: local alignment, the optimal path.
10. Cluster analysis of genomic data for analysis (basic principles, applications).
11. Hidden Markov models for analyzing genomic data (basic principles, applications).
12. A complementary exercise.
13. Final test.