DSIB01 Introduction to Bioinformatics

Faculty of Medicine
autumn 2022
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)
Ing. Katarína Grešová (lecturer)
Mgr. Václav Hejret (assistant)
Guaranteed by
prof. RNDr. Ondřej Slabý, Ph.D.
Department of Biology – Theoretical Departments – Faculty of Medicine
Contact Person: Mgr. Lenka Vivas
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: 9/15, only registered: 1/15, only registered with preference (fields directly associated with the programme): 0/15
fields of study / plans the course is directly associated with
there are 236 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)
The course is taught annually.
The course is taught: in blocks.
Note related to how often the course is taught: Teaching is conducted in five blocks:(1 block=2 hours);: Mon 7.11. 15:00-17:00, Thu 10.11. 14:00-18:00, Mon 14.11. 15:00-17:00, Fri 18.11. 15:00-17:00.
General note: E35/211.
Teacher's information
Panagiotis Alexiou, mail: panagiotis.alexiou@ceitec.muni.cz
The course is also listed under the following terms autumn 2019, autumn 2020, autumn 2021.
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