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.

The evaluation is based on the realization of a written exam that will evaluate the knowledge obtained in class (40% of final grade) and the evaluation of their performance during the Pratical classes (60% of final grade).

Learning Outcomes

Upon completing this course, the student will be able to understand and apply the main algorithms and tools used in Computational Biology; analyse and annotate biological sequences; apply proteomics algorithms; model and interpret gene regulatory networks within a systems biology framework; select and integrate computational tools appropriate for different biological problems; and develop autonomy in searching for and critically evaluating specialised scientific literature.

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

Head Lecturer(s)

Joel Perdiz Arrais

Assessment Methods

Assessment
Exam: 40.0%
Project: 60.0%

Bibliography

Deep Learning Applications in Translational Bioinformatics, Reza, K, Elsevier, 2024, ISBN: 9780443222993

Practical Bioinformatics, Agostino, M., 2023, ISBN: 978-1134063918

Introduction to Bioinformatics. Lesk, A, Oxford University Press, 2019, ISBN:0198794142

Discovering genomics, proteomics, and bioinformatics, 2nd Edition, A. Malcolm Campbell, Laurie J. Heyer, Benjamin Cummings; 2006