Course syllabus for Introduction to bioinformatics

The course syllabus contains changes
See changes

Course syllabus adopted 2017-02-20 by Head of Programme (or corresponding).

Overview

  • Swedish nameIntroduktion till bioinformatik
  • CodeMVE510
  • Credits7.5 Credits
  • OwnerMPBIO
  • Education cycleSecond-cycle
  • Main field of studyBioengineering, Software Engineering, Mathematics
  • DepartmentMATHEMATICAL SCIENCES
  • GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail

Course round 1

  • Teaching language English
  • Application code 08123
  • Maximum participants60
  • Open for exchange studentsYes

Credit distribution

0117 Examination 5 c
Grading: TH		 5 c
  • 14 Jan 2021 am J
  • 09 Apr 2021 pm J
  • 25 Aug 2021 am J
0217 Laboratory 2.5 c
Grading: UG		 2.5 c

In programmes

Examiner

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Eligibility

General entry requirements for Master's level (second cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Specific entry requirements

English 6 (or by other approved means with the equivalent proficiency level)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Course specific prerequisites

Basic courses in molecular biology and statistics.

Aim

The course provides an introduction to bioinformatics with focus on large-scale molecular data and how it can be used to address problems within the life sciences. The course will cover techniques to generate omics data as well as basic concepts and tools for analysis and visualization. Challenges related to the interpretation of omics data will also be discussed.

Learning outcomes (after completion of the course the student should be able to)

  • Explain how biological questions can be addressed using bioinformatics and high-throughput biological data.
  • Explain the advantages and disadvantages of different technologies for high-throughput sequencing and their applications in genomics.
  • Describe and apply methods for analysis of DNA and protein sequence data.
  • Describe methods to align sequence data with the purpose to identify mutations and to quantify mRNA, protein and gene abundances.
  • Describe and apply statistics and machine learning algorithms for exploration and visualization of high-dimensional data.
  • Describe and apply methods to identify biological effects in high-dimensional data.
  • Describe and apply methods for assessing statistical significance in high-dimensional data.
  • Explain and critically discuss i) quantitativitiy, ii) 'the curse of dimensionality' and iii) correlation and causation in relation to high-throughput biological data.

Content

The course covers methods for high-throughput sequencing, algorithms for analysis of sequencing data and their applications in genomics, tools for visualization of high-dimensional data and statistical methods for identifying biological effects and assessing significance. The course participants will also critically discuss the many challenges associated with the analysis of big data within the life sciences.

Organisation

Lectures and computer-based exercises.

Literature

To be decided. Will be specified in the course PM.

Examination including compulsory elements

Obligatory computer-based exercises and a written exam.

The course syllabus contains changes

  • Changes to course rounds:
    • 2020-05-29: Block Block B added by lina haglund
      [Course round 1]
    • 2020-05-29: Block Block B removed by lina haglund
      [Course round 1]
  • Changes to examination:
    • 2020-09-30: Grade raising No longer grade raising by GRULG