Introduction

• What is Bioinformatics?

  Bioinformatics is a multi-disciplinary subject covering the development and application of computational solutions to problems in the biological sciences.

  Rapid advances in the biosciences are producing vast amounts of new data. The need to use computational approaches in making sense of these data has placed bioinformatics activities at the centre of many bioscience disciplines. The IT Programme's Specialisation in Bioinformatics gives you the opportunity to move into this exciting, important and challenging multi-disciplinary field.

• Which areas of IT are important in Bioinformatics?

  The biosciences are data-rich areas and databases are needed to manage various kinds of data including genome sequences, protein structures, molecular interactions and the complex pathways of biochemical reactions that are vital to life. Algorithms are developed to analyse these data, for example string matching techniques are used to detect similarities between human genes and those of different species, and clustering techniques are used in detecting evolutionary relationships and predicting three-dimensional protein structures. Good choices of data structures are important in implementing these algorithms efficiently. Machine learning methods can be used to try to find complex patterns in large biological data sets. Interactive computer graphics are used in visualising scientific data including three-dimensional molecular structures. Image processing techniques are widely used in analysing biological images, including images of microarrays and two-dimensional electrophoresis gels from which gene and protein expression levels can be measured. Software engineering skills are needed to build systems and software packages that can be used by scientists, and designing effective user interfaces for these requires human-computer interaction principles to be applied. Web technologies are often used to make bioinformatics data sets and tools available to the scientific community. Many other areas of IT are also used in bioinformatics.

• I don't know anything about biology; does that prevent me from taking the Bioinformatics Specialisation?

  No. The course "Basics in Biology" is aimed at students with no previous knowledge in this area. People working in bioinformatics have varied backgrounds; some have their initial education in IT, others in the biosciences, others in other disciplines. What is important is that you should be interested to acquire knowledge in new areas.

  Several of the careers articles listed below highlight the demand for bioinformaticians with an IT background.

• What is the relationship between the IT Programme's Bioinformatics Specialisation and the Chalmers International Masters Programme in Bioinformatics?

  These programmes share the same core bioinformatics courses. However, students on the IT Programme's Bioinformatics Specialisation have more flexibility to take other computing science courses alongside the core bioinformatics courses.

  The students on the International Masters Programme in Bioinformatics have varied backgrounds, including biochemistry, molecular biology, computing science, mathematics, statistics, physics, and medicine. Working alongside students from different disciplines is an important part of the bioinformatics education at Chalmers, and presents an opportunity to develop communication skills across discipline boundaries.

Aims

The aim of the Bioinformatics specialisation of the IT Programme is to give students knowledge of the various types of problems addressed in bioinformatics, skills in the computational techniques used, and insight into their theoretical basis.

Also, proficiency in communicating across different disciplines is an important effect of the education. An interdisciplinary area such as bioinformatics may easily suffer from communication problems; there is a need for a common language. By bringing students with different background together, the training in understanding and communicating with people of other disciplines is assured.

Career Prospects

On completing this specialisation, the student should be a competent bioinformatician; ready to start PhD studies in bioinformatics, or able to take up a position in a bioinformatics group in industry or academia.

• "Capitalizing on New Needs and New Opportunities: Government - Industry Partnerships in Biotechnology and Information Technologies (2001)" and in particular see the chapter on "Bioinformatics: Emerging Opportunities and Emerging Gaps"

• "Hot Property: Biologists Who Compute" and "Demand Outstrips Supply" (Science, 1996)
• "Focus on Careers: Bioinformatics Science" (Science Careers)
• "Focus on Careers: Bioinformatics" (Science Careers, June 1999)
• "Bioinformatics: Low Supply, High Demand" (Science Careers, June 2001)
• "Hot careers: Beyond Biology's Bounds" (Science Careers, November 2001)
• "Careers in Bioinformatics", (Chemical and Engineering News, January 2002)
• "Genomics: Bridging Research Areas" (Science Careers, May 2003)
• "Choose a Career: Bioinformatics", (New Scientist Jobs, 2003)

Objectives

On completing the Bioinformatics specialisation of the IT Programme the student should:

• understand basic biological principles and systems;
• be familiar with biological databases, and available tools to analyse the information in such databases;
• have skills in design and implementation of bioinformatics systems including databases, algorithms, and visualisation;
• be able to model biological data and systems, and understand the logical structure, conceptual basis and presumptions of such models;
• have insights into, and skills in using, a number of different computational and statistical techniques;
• be able to perform statistical analysis on genetic information;
• have insights into the ethical aspects of bioinformatics and biotechnology research.

Requirements for Passing

Students must take all of the obligatory courses and undertake project work ("Exjobb") in the area of Bioinformatics.

Obligatory Courses

Introduction to Bioinformatics (5 credits)
Basics in Biology (5 credits)
Sequence Information (5 credits)
Structural Bioinformatics (5 credits)
Gene Expression and Cell Models (5 credits)

Recommended Courses

Algorithms for Machine Learning and Inference (5 credits)
Statistical Image Processing (5 credits)
Population Genetics (5 credits)
Statistics in Genetics (5 credits)
Algorithms (4 credits)
Ethical and Social Issues in Genetics and Biotechnology (3 credits)

Prerequisites

Schedule