Algorithms in Bioinformatics

Course programme (Note the program is updated regularly)

Course description in spanish

In the summer 2011, CBS is organizing an 2 weeks off-site course on MSc/PhD level in Algorithms in Bioinformatics at the Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires

The first lecture in course is given Monday February 28th, 10.00-17.00, 2011 in auditorium 5, Departamento de Computacion

Scope and form:
The scope of the course is to give students a detailed introduction to bioinformatics and the algorithms used to construct high quality bioinformatics prediction methods. The course consists of lectures, exercises and project. The course is evaluated in terms of exercises and one written report to be handed in by the end of the course and an oral examination.

Duration of Course:
2 weeks

Type of assessment:
Oral examination and written report. Written report (50%) and oral examination (50%)

General course objectives:
To provide the student with an overview and in-depth understanding of Bioinformatics machine-learning algorithms. Enable the student to first evaluate which algorithm(s) are best suited for answering a given biological question and next implement and develop prediction tools based on such algorithms to describe complex biological problems such as immune system reactions, vaccine discovery, protein structure and function, post-translational modifications etc..

Learning objectives:
A student who has met the objectives of the course will be able to:

• Understand the details of the algorithms commonly used in bioinformatics.
• Develop computer programs implementing these algorithms.
• Identify which type of algorithm is best suited to describe a given biological problem.
• Understand the concepts of data redundancy and homology reduction.
• Develop bioinformatics prediction algorithms describing a given biological problem.
• Implement and develop prediction tools on a detailed level using the following algorithms: Dynamic programming, Sequence clustering, Weight matrices, Artificial neural networks, Hidden Markov models.
• Design a project where a biological problem is analyzed using one or more machine learning algorithms.
• Implement, document and present the course project.

Content:
The course will cover the most commonly used algorithms in bioinformatics. Emphasis will be on the precise mathematical implementation of the algorithms in terms of functional computer programs. During the course, biological problems of immunological relevance will be introduced and analyzed with the purpose of highlighting the strengths and weaknesses of the different algorithms. The following topics will be covered:

• Weight matrices: Sequence weighting, pseudo count correction for low counts, Gibbs sampling, and Psi-Blast
• Dynamic programming: Needleman-Wunsch, Smith-Waterman, and alignment heuristics
• Data redundancy and homology reduction: Hobohm and other clustering algorithms
• Hidden Markov Models: Model construction, Viterbi decoding, and posterior decoding, and Baum Welsh HMM learning
• Artificial neural networks: Architectures and sequence encoding, feed-forward algorithm, and back propagation

Prerequisites:
Students should have basic programming knowledge and some knowledge of Bioinformatics is recommended. The programming language of the course is C, but no prior knowledge of C is required. The course is a combination of lectures and exercises, and will be conducted in English.

Sign up:

For further information about the course contact Morten Nielsen

Participants restrictions:
Minimum 10, Maximum: 30

Course curriculum: Compendium of review papers and selected chapters from Immunological Bioinformatics, Lund et al., MIT Press, 2005. The compendium will be handed out during the first week of the course. Other material will be made available online during the course.

For more information, please contact course organizer Morten Nielsen or click on the links above.

Course programme (Note the program is updated regularly)
Programa del curso en espanol. (PDF file no links included)

Use your own portable computer for exercises