Bioinformatics

Recommended Prerequisites

- Molecular Biology

- Biostatistics and computational modeling.

Teaching Methods

The course is divided into expository and laboratory classes. The first is dedicated to present the content in a more theoretical approach, without failing to include the active participation of students. The aim is to develop their’s reasoning ability and integration of knowledge and stimulate their critical thinking. Practical classes will enable the student to explore the acquired concepts. Those will follow a problem oriented approach by launching challenges that require knowledge integration, and wherever possible, the use of working groups and discussion.

Learning Outcomes

Systematic comprehension of the main algorithms and tools used in Computational Biology. In particular, it is aim to focus on methods of analysis and annotation of sequences, Phylogenetic trees, application algorithms in proteomics and in the area of systems biology.

Work Placement(s)

No

Syllabus

1. Introduction and Key Concepts

    a. Computational Challenges in Computational Biology

    b. Databases and Bioinformatic libraries 

2. Methods for sequence analysis

    a. Global and local sequence aligment

    b. Penalty functions and Heuristic methods

    c. Multiple Sequence Alignments

    d. Molecular evolution and Phylogenetic Tree Reconstruction 

    e. Annotation of genomes

3. Prediction of RNA secondary structure

    a. Base-pairs maximisation methods

    b. Energy minimisation methods

    b. Clustering and classification

4. Genomic basis of Human diseases

    a. Human Population genomics

    b. DNA sequencing and Assembly

    c. Genetic variations and diseases

    d. Gene expression analysis. Clustering and classification.

5. Biological Networks

    a. Theoretical properties of Biological Networks

    b. Forecasting and simulation of biological networks

    c. Time series reconstruction.

Head Lecturer(s)

Joel Perdiz Arrais

Assessment Methods

Assessment
Exam: 40.0%
Project: 60.0%

Bibliography

Gusfield, Dan. Algorithms on Strings, Trees and Sequences: Computer Science and Computational Biology. Cambridge, UK: Cambridge University Press, 1997. ISBN: 0521585198.

Waterman, Michael. Introduction to Computational Biology: Maps, Sequences, and Genomes. Boca Raton, FL: CRC Press, 1995. ISBN: 0412993910.

Durbin, Richard, Graeme Mitchison, S. Eddy, A. Krogh, and G. Mitchison. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge, UK: Cambridge University Press, 1997. ISBN: 0521629713.

Jones, Neil, and Pavel Pevzner. An Introduction to Bioinformatics Algorithms. Cambridge, MA: MIT Press , 2004. ISBN: 0262101068.