Course detail

Programming in Bioinformatics

FEKT-FPRGAcad. year: 2016/2017

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

doc. Ing. Jana Kolářová, Ph.D.

Department

Department of Biomedical Engineering (UBMI)

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

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: basic principles of algorithms (min 15 points, max 30 points)
2. test: implementation of algorithms (min 15 points, max 30 points)
3. final test (min 20 points, max 40 points).
Individual activities check students' ability to implement algorithms in Matlab for the analysis of genomic data.

Course curriculum

1-2. Fundamentals of algorithmization (recursive and iterative algorithms, complexity of algorithms, different types of algorithms and their use in bioinformatics).
3-5. Search algorithms (Exhaustive Search - restriction mapping, motive searching, Greedy algorithm - analysis of genetic changes with reversion and sorting method Breakpoints).
6-8. Dynamic programming (general description of the methods, global alignment, local alignment, optimal path, applications).
9-12. Cluster analysis and Hidden Markov Models for genomic data analysis (basic principles, applications).

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 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)

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme BTBIO-F Master's

    branch F-BTB , 1 year of study, winter semester, compulsory

  • Programme EEKR-CZV lifelong learning

    branch EE-FLE , 1 year of study, winter semester, compulsory

Type of course unit

 

Lecture

13 hod., optionally

Teacher / Lecturer

Ing. Denisa Maděránková, Ph.D.

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).

Exercise in computer lab

52 hod., compulsory

Teacher / Lecturer

doc. Mgr. Ing. Karel Sedlář, Ph.D.
Ing. Denisa Maděránková, Ph.D.

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.