Course detail

Programming in Bioinformatics

FEKT-FPRGAcad. year: 2012/2013

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

Practical knowledge of Perl programming focused on work witk DNA and protein databases. Knowledge of basic DNA and protein sequence analysis and result interpetation.

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

Requirements for completion of a course are specified by a regulation issued by the lecturer responsible for the course and updated for every year.

Course curriculum

Fundamentals of Algorithms tasks (recursive and iterative algorithms, complexity of algorithms, different types of algorithms and their use in boinformatice). Search algorithms (Exhaustive Search - restriction mapping, searching themes Greedyho algorithm - Analysis of genetic changes with reversion and sorting method Breakpoints). Dynamic programming (general description of the method, a global alignment, local alignment, optimal path, applications). Cluster analysis and Hidden Markov Models for genomic data analysis (basic principles, applications).

Work placements

Not applicable.

Aims

To acquire the basic programming skills in Perl. Selected solved problems for analysis of DNA and protein sequences will be studied. Introduction to types of data, how to operate and how to present results. Knowledge of proposal for program.

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

Limmitations of controlled teaching and its procedures are specified by a regulation issued by the lecturer responsible for the course and updated for every year.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Moorhouse M, Barry P: Bioinformatics Biocomputing and Perl: An Introduction to Bioinformatics Computing Skills and Practice. Wiley; 1 edition, 2004. (EN)

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme BTBIO-F Master's

    branch F-BTB , 2 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

doc. Ing. Jana Kolářová, 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

39 hod., compulsory

Teacher / Lecturer

Ing. Denisa Maděránková, Ph.D.
Ing. Lucie Krátká

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.