Bioinformatics | University of Stavanger

This course provides a comprehensive overview of applied bioinformatics and programming, encompassing various key topics. These include access to bioinformatic databases; data analysis with help of sequence alignments and phylogenetic analysis; primer design for PCR, omics data analysis. Furthermore, students will learn using Python and R programming for bioinformaitcs. Machine learning and AI are included in the course.

Content

NB! This is an elective course and may be cancelled if fewer than 10 students are enrolled by January 20th for the spring semester.

The course covers the following subjects in applied bioinformatics: gene/protein/metabolite databases, local and global multiple sequence alignments, construction of phylogenetic trees, sequence analysis tools, primer construction for PCR/RT-PCR (QPCR), omics data analysis, and introduction to Python and R programming for bioinformatics. Data analysis, simulations, visualization techniques, and artificial intelligence (AI) applications are included.

Learning outcome

Learning objectives:

To get a good overview of important bioinformatics tools (sequence alignments, construction of phylogenetic trees, sequence analysis tools, protein structure, and omics data analysis)
To be able to use major bioinformatics software and databases
To learn primer construction for PCR
To learn basic programming for bioinformatic purposes.
To get an introduction to machine learning
To learn about Artificial intelligence (AI) applications.

Required prerequisite knowledge

None

Recommended prerequisites

Cell Biology (BIO100), Biochemistry (BIO200)

Basic knowedge in molecular biology and biochemistry and computational skills are beneficial for completion of this course.

Exam

Written exam

Weight 1/1

Duration 4 Hours

Marks Letter grades

Aid None permitted

Exam system WISEflow

Written digital exam.

Coursework requirements

3 home assignments

The course includes the following compulsory elements:

Three home assignments. Approved assignments are required to qualify for the final assignment.
80% attendance at scheduled teaching hours is required to qualify for the final assignment.

Method of work

2 hours lectures and 2 hours practical computer exercises per week.

Using AI to support learning and study activities

AI tools may be used in this course as a learning aid, but must never substitute for the understanding, reasoning and skills the course is designed to build — you must be able to produce and explain your own work. Productive uses include asking AI to explain concepts you find difficult, to give feedback on or help debug and structure work you have written yourself, and to generate practice questions for self-testing before assessments. AI should not produce the answers, code, calculations or text that you submit as your own. Any use of AI in assessed work must follow the course's guidelines and, where you submit work, be disclosed as specified in the syllabus — and you remain responsible for critically evaluating whatever the AI produces.

Overlapping courses

Course	Reduction (SP)
Bioinformatics (BIO510_1) , Bioinformatics (MOT290_1)	10

Open for

If the number of participants needs to be limited, priority will be given to Master students in Biological Chemistry.

Admission requirements

A completed bachelor’s degree

Course assessment

The faculty decides whether early dialogue will be held in all courses or in selected groups of courses. The aim is to collect student feedback for improvements during the semester. In addition, a digital course evaluation must be conducted at least every three years to gather students’ experiences.