SubjectsSubjects(version: 950)
Course, academic year 2019/2020
Laboratory Project II - N143007
Title: Laboratorní projekt II
Guaranteed by: Department of Informatics and Chemistry (143)
Faculty: Faculty of Chemical Technology
Actual: from 2019 to 2020
Semester: summer
Points: summer s.:2
E-Credits: summer s.:2
Examination process: summer s.:
Hours per week, examination: summer s.:0/3, MC [HT]
Capacity: unknown / unknown (unknown)
Min. number of students: unlimited
Language: Czech
Teaching methods: full-time
Teaching methods: full-time
Is provided by: M143011
For type:  
Guarantor: Znamenáček Jiří Ing.
Class: Specializační laboratoř
Examination dates   Schedule   
Annotation -
Last update: Znamenáček Jiří Ing. (01.04.2016)
The goal of the course is to get practical knowledge of bioinformatics algorithms using Python programming language.
Aim of the course -
Last update: Znamenáček Jiří Ing. (01.04.2016)

Students will be able to:

Understand the principles behind the most important bioinformatics algorithms.

Recreate known algorithms in Python and develop new ones.

Use existing bioinformatics tools for their own work.

Literature -
Last update: Znamenáček Jiří Ing. (01.04.2016)

R: Compeau, Phillip; Pevzner, Pavel: "Bioinformatics Algorithms: An Active Learning Approach"

R: Jones, Neil C.; Pevzner, Pavel A.: "An Introduction to Bioinformatics Algorithms"

A: Antao, Tiago: "Bioinformatics with Python Cookbook"

A: Stevens, Tim J.; Boucher, Wayne: "Python Programming for Biology: Bioinformatics and Beyond"

A: Haddock, Steven H.D.; Dunn, Casey W.: "Practical Computing for Biologists"

A: Bassi, Sebastian: "Python for Bioinformatics"

Learning resources -
Last update: Znamenáček Jiří Ing. (01.04.2016)

The course dedicated web pages.

Requirements to the exam - Czech
Last update: Znamenáček Jiří Ing. (01.04.2016)

Podmínkou udělení klasifikovaného zápočtu je úspěšné naprogramování vybraných variant některých bioinformatických algoritmů.

Syllabus -
Last update: Znamenáček Jiří Ing. (01.04.2016)

Solving NP-complete problems.

Markov chains.

Monte Carlo method.

Protein folding prediction.

Machine learning.

Gene regulatory networks.

Registration requirements -
Last update: Znamenáček Jiří Ing. (01.04.2016)

Successfully finished course "Laboratory Project I".

Teaching methods
Activity Credits Hours
Účast v laboratořích (na exkurzi nebo praxi) 6 168
6 / 2 168 / 56
Coursework assessment
Form Significance
Regular attendance 30
Defense of an individual project 70