Bioinformatics

Recommended Prerequisites

- Programming - 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, application algorithms in proteomics and in the area of systems biology, and especially in genomic regulatory networks.

Work Placement(s)

No

Syllabus

1. Introduction and Key Concepts

    a. Computational Challenges in Computational Biology

    b. Databases and Bioinformatic libraries

    c. Knowledge of linux command line environment and bash scripting 

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.

Assessment Methods

Assessment
Exam: 40.0%
Project: 60.0%

Bibliography

Ramsundar, Bharath, et al. Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More. " O'Reilly Media, Inc.", 2019. ISBN: 978-1492039839

 

Moses, Alan. Statistical Modeling and Machine Learning for Molecular Biology. Chapman and Hall/CRC, 2017.

 

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.