About this course

Introduction to sequences and databases. Sequence alignments - theory and practice. Database search - BLAST and similar tools. Models of biological sequences. Phylogeny. Gene expression analysis. The topics will be introduced approximately in this order, through weekly lectures and practical computer exercises. Knowledge of bioinformatics increases our understanding of the world we live in and helps the development of sustainable societies.

Learning outcome

KNOWLEDGE: Students will get a basic knowledge of sequence comparions, i.e. alignments, sequence models and phylogeny. They wil also get a basic knowledge of the analysis of data from quantification of gene expression and microbial communities. They will be able to explain the principles on which the methods rely.

SKILLS: Emphasis will be put on teaching students to use general data analysis tools, including basic programming in R, and understand the output of the different programs that will be used.

GENERAL COMPETENCE: Be able to use basic bioinformatic tools, both those we have used in this course as well as other similar tools.

  • Learning activities

    Lectures and practical computer exercises.

  • Teaching support

    Lecture notes will be available in Canvas on the Internet.

  • Syllabus

    Lecture notes, videos, exercises and handouts.

  • Prerequisites

    Basic knowledge of molecular biology and statistics (BIO120/STAT100).

    In statistics the students are required to:

    • Be able to perform descriptive statistics and draw conclusions from this.
    • Know basic concepts and principles in probability theory with emphasis on stochastic variables and their properties.
    • Be familiar with some common probability distributions, including the normal distribution, binomial distribution,

    In coding the students are required to

    • Have basic skills in coding, i.e. know basic data types (numbers, text, logicals), basic data structures (vectors/arrays, tables) and control structures (conditions and loops)

  • Recommended prerequisites

    Knowledge of genetics and statistics and coding beyond the introductory level.

  • Assessment method

    Written final exam (counts 100%).

    Written exam Karakterregel: Letter grades Hjelpemiddel: A1 No calculator, no other aids

  • About use of AI

    K3: We encourage the use of AI as a tool for improving your own learning, like a discussion partner, improving written language or suggesting R code. However, handed in material must be a product of your own efforts. Direct copying of AI generated material without considerable personal contribution, like editing, commenting or discussion, is not allowed. If you use AI you must declare exactly what type of AI and for what it was used.

    Descriptions of AI-category codes.

  • Examiner scheme

    Intern sensur.

    Ekstern sensor godkjenner opplegg for vurdering.

  • Mandatory activity

    There will be compulsory assignments for the students to hand in and have approved before the exam.

  • Teaching hours

    Lectures: 2 hours plus video per week. Exercises: 2-6 hours per week.

  • Preferential right

    B-BIOTEK, M-BIOTEK, M-KB, M-BIAS.

  • Admission requirements

    Special requirements in Science