BIN210 Introduction to Bioinformatics

Credits (ECTS):10

Course responsible:Torgeir Rhodén Hvidsten, Lars Gustav Snipen

Campus / Online:Taught campus Ås

Teaching language:Norsk

Limits of class size:1000

Course frequency:Annually

Nominal workload:Lectures: 28 hours. Exercises: 56 hours. Individual study: 166 hours.

Teaching and exam period:This course starts in Spring parallel. This course has teaching/evaluation in Spring parallel.

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 gene expression data. 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.

  • Lectures and practical computer exercises.
  • Lecture notes will be available in Canvas on the Internet.
  • 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, Student’s t-distribution,
    • F-distribution and the chi-square distribution.
    • Understand basic estimation theory, including what is meant by confidence intervals, point estimates, expectation accuracy and standard error for an estimator.
    • Reformulate simple situation descriptions and problem(s) to a relevant statistical model, and interpret the parameters in this.This applies to situations that can be covered by simple linear regression models, one-way analysis of variance or bivariate analyses.
    • Test relevant cases using formal hypotheses, including setting up relevant hypotheses, testing these and interpreting the result. Basic skills in R/RStudio.

    This is covered by STAT100 or a similar course.

  • Written final exam (counts 100%).

  • An external examiner approves the examination questions and marks 25 selected examination papers.
  • There will be compulsory assignments for the students to hand in and have approved before the exam.
  • Lectures: 2 hours per week. Exercises: 2 hours per week.
  • Special requirements in Science