LF:DSIB01 Introduction to Bioinformatics - Course Information
DSIB01 Introduction to Bioinformatics
Faculty of Medicineautumn 2020
- Extent and Intensity
- 2/0/2. 4 credit(s) (plus 1 for the colloquium). Type of Completion: k (colloquium).
- Teacher(s)
- Panagiotis Alexiou, PhD (lecturer)
Mgr. Vojtěch Bystrý, Ph.D. (lecturer)
Mgr. Václav Hejret (assistant) - Guaranteed by
- prof. RNDr. Ondřej Slabý, Ph.D.
Department of Biology – Theoretical Departments – Faculty of Medicine
Contact Person: Olga Křížová
Supplier department: Central European Institute of Technology - Prerequisites
- SOUHLAS
The course is offered to doctoral and master students of any study field. Knowledge of biology, basic statistics, and basic programming concepts is expected. - Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 15 student(s).
Current registration and enrolment status: enrolled: 2/15, only registered: 0/15, only registered with preference (fields directly associated with the programme): 0/15 - fields of study / plans the course is directly associated with
- there are 232 fields of study the course is directly associated with, display
- Course objectives
- Students will understand bioinformatics methods, explore available resources, and be introduced to programming for bioinformatics. For 2020 autumn semester, the course will be ONLINE only.
- Learning outcomes
- Upon completion of the course, students will be able to: • understand and use methods in bioinformatics such as sequence alignment, phylogenetic analysis and pattern recognition • access bioinformatic resources, retrieve information, apply to biological problems • use and design simple analysis • scripts in bash, python, and R
- Syllabus
- Sequence analysis introduction/common file formats Sequence Alignment / Sequence pattern recognition 1/3 Sequence Alignment / Sequence pattern recognition 2/3 Sequence Alignment / Sequence pattern recognition 3/3 NGS / galaxy Introduction to Data Analysis Principles of Data visualisation Clustering / PCA Basic Statistics Bayesian Inference/Bayesian classifier Current Developments in Machine Learning
- Teaching methods
- The course will combine theoretical lectures and practical exercises. For completion of the course, students will need to prepare and present a project.
- Assessment methods
- For successful completion of the course, students must complete and present a project.
- Language of instruction
- English
- Further comments (probably available only in Czech)
- Study Materials
The course is taught annually.
The course is taught: every week.
Note related to how often the course is taught: For 2020 autumn semester, the course will be ONLINE only.
- Enrolment Statistics (autumn 2020, recent)
- Permalink: https://is.muni.cz/course/med/autumn2020/DSIB01