790303 Computing skills for biotechnology (in Eng.)

Type
Lecture and exercise
Semester hours
2
Lecturer (assistant)
De Ruiter, Anita , Dohm, Juliane , Oostenbrink, Chris , Poliak, Peter , Himmelbauer, Heinz
Organisation
Offered in
Wintersemester 2024/25
Languages of instruction
Englisch
Content

This course provides an introduction to 1) online tools, databases, and database searches with application to the analysis of nucleotide and protein sequences and protein structures, 2) Linux command line and Python.

Part 2) is organised in two parallel groups. Students will choose one of two groups at the beginning of the course (see BOKUlearn).

Group 1: The Institute of Computational Biology (ICB) is responsible for this group.
Content: Students will learn programming in Python and how to work on Linux systems using the command line (bash) for file management and data analysis. Why should you care about Linux? Linux tools are efficient and powerful. They can be used for many tasks without the need to write code from scratch in a programming language. When it comes to large data sets like in sequencing projects a wealth of Linux-based analysis software is available, and high-performance computing systems are necessary to handle the data. All top supercomputers in the world use Linux, and it is the operating system of choice in bioinformatics and other data-related sciences and industry. Being fluent and creative on the Linux command line is a clear competitive advantage in the context of data analysis skills no matter if it is about small tables or GB-sized compressed files. Another core component of the course in this group will be basics of Python programming. It is widely known that Python skills are asked by many employers, and understanding of programming principles is a general benefit for any researcher. We will introduce Python in manageable bits along with supporting exercises to fully understand what each line of the code is doing. The Python part will introduce data types, variables, control structures, functions and methods, and useful libraries.
Organisation: The sessions will be fully virtual, i.e. consisting of pre-recorded teaching videos and recommended exercises on BOKUlearn, no attendance is necessary at any specific dates. Homework exercises will be voluntary except for two assignments that will be graded.

Group 2: In this group, the emphasis will be on programming with python for data analysis, the automation of tasks and visualization. With Python's flexibility and widespread use in scientific research, students will gain practical experience that can be directly applied to challenges in modern biotechnology. Students will acquire transferable skills in valuable computational tools to support their research and professional growth. The sessions will be offered in a hybrid setting, in the lecture room and via Zoom. This facilitates an interactive learning experience with direct interactions with the lecturers. Recordings of the session will be made available afterwards. Students will have the opportunity to hand in weekly exercises on a voluntary basis. The evaluation of this part of the course will be based on two compulsory exercises.
Previous knowledge expected

None
Objective (expected results of study and acquired competences)

Students know public databases and online tools for analyzing biological data. They are able to work on the Linux command line and have basic programming skills. Students have competences in terms of "computational thinking".
You can find more details like the schedule or information about exams on the course-page in BOKUonline.