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Bioinformatics

Code: 104415 ECTS Credits: 6
2025/2026
Degree Type Year
Computational Mathematics and Data Analytics OP 4

Contact

Name:
Angel Gonzalez Wong
Email:
angel.gonzalez@uab.cat

Teachers

Gianluigi Caltabiano
Angel Gonzalez Wong
Juan Ramon Gonzalez Ruiz
Carolina Soriano Tarraga

Teaching groups languages

You can view this information at the end of this document.


Prerequisites

Basic knowledge of the English language, as a large part of the articles, tutorials, and software packages are written in English.

It is recommended to have taken the Bioinformatics course or have equivalent knowledge of:

  • Basics of Molecular Biology and Genomics.
  • Basic programming with R.




Objectives and Contextualisation

The course aims to provide an overview of the possibilities of Big Data analysis focused on Biomedicine and Bioinformatics.

The course consists of two thematic blocks:
  1. Computational Methodologies Applied to Drug Discovery
  2. Omics Data Analysis
At the end of the course, the student will be able to:
  • Characterize and manage large-scale data from biomedical research.
  • Apply statistical, bioinformatics, and machine learning algorithms to biological and biomedical data.
  • Integrate and visualize multiple layers of data to interpret biological hypotheses.
  • Critically evaluate results and research in the field, integrate knowledge, and communicate information clearly and appropriately within the disciplinary context.
The course is part of the Statistics Mention for Health Sciences.



Learning Outcomes

  1. CM34 (Competence) Propose suitable statistical models for epidemiological studies.
  2. CM35 (Competence) Write technical reports that clearly express the results and conclusions of a bioscience study using vocabulary specific to the field of application.
  3. KM29 (Knowledge) Recognise the most used statistical inference methods in bioinformatics.
  4. KM30 (Knowledge) Identify the use of statistical knowledge in bioinformatics and in health science.
  5. KM31 (Knowledge) Identify the most used statistical inference methods in epidemiology studies.
  6. SM36 (Skill) Analyse data corresponding to epidemiological studies or clinical trials.
  7. SM37 (Skill) Apply statistical methods to the analysis of gene expression data.
  8. SM38 (Skill) Use the most common databases in the field of health science.

Content

BLOCK 1. Big Data in Drug Discovery

  • Introduction to Big Data in Biosciences, Bioconductor, and the R ecosystem
  • Databases and representation of biological components and chemical compounds.
  • Analysis, clustering, and visualization of chemical and pharmacological substances.
  • Virtual Screening in Drug Discovery.

BLOCK 2. Big Data in Omics Data Analysis

  • Introduction to Bioconductor and bioinformatics tools for omics data analysis.
  • Genetic association studies and GWAS (Genome-Wide Association Studies).
  • Multivariate Methods for the Integration of Omics Data and Big Data.




Activities and Methodology

Title Hours ECTS Learning Outcomes
Type: Directed      
Practical sessions 21 0.84
Presentation of Research Project 3 0.12
Theory classes 21 0.84
Type: Supervised      
Tutoring 10 0.4
Type: Autonomous      
Preparation of Research Project 20 0.8
Study 70 2.8

The course is organized in sessions of 3 hours. Each session consists of a theoretical part (theory classroom) that will introduce the new concepts followed by a practical part (computer room) where the students will work on the implementation of concepts explained in the theoretical part. In each session the teacher will indicate the students some tasks to do autonomously, such as reading articles, resolution of class exercises or sending reports. The material used by the teachers will be available on the Virtual Campus of the course.

 

Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.


Assessment

Continous Assessment Activities

Title Weighting Hours ECTS Learning Outcomes
Presentation class exercises 30 0.5 0.02 KM29, KM30, KM31, SM37, SM38
Presentation of practicum reports 30 0.5 0.02 CM34, CM35, KM29, SM36, SM37, SM38
Presentation Research Project 20 2 0.08 CM35, KM29, KM30, SM36, SM38
Theoretical-practical exams 20 2 0.08 KM29, KM30, KM31, SM37, SM38

BLOCK 1. Big Data in Drug Design (50%):

  • Class exercises presentation (15%)
  • Preparation of Practice Reports (15%)
  • Bioinformatics Project Presentation before a committee (20%)

BLOCK 2. Big Data in Omics Data Analysis (50%):

  • Class exercise presentation (15%)
  • Preparation of Practice Reports (15%)
  • Theoretical-Practical Test (20%)

The minimum overall grade required to pass the course will be 5 points. To calculate the average, the minimum grade for each of the assessable activities must be equal to or greater than 3,5 points.

In order to be eligible for the resit, students must have previously been assessed in a set of activities whose weight is equivalent to at least two-thirds of the total grade for the course. Students who have failed or not submitted one or more of the assessments may take the resit exam corresponding to the failed block. If the established threshold is not reached in any of the blocks during the resit, the final course grade will be the minimum of the block grades.

This course does not allow for the single assessment system.


Bibliography

  • Lesk A.M. Introduction to Bioinformatics. Oxford University Press 2005.
  • Attwood, T.K., Parry-Smith, D.J., Introducción a la Bioinformática. Pearson Education, 2002.
  • Foulkes A.S. Applied Statistical Genetics with R. For Population-based Association Studies.Springer Dordrecht Heidelberg London New York. ISBN 978-0-387-89553-6
  • Gonzalez JR, Cáceres A. Omic association studies with R and Bioconductor. Chapman and Hall/CRC, ISBN 9781138340565, 2019.
  • Specialized readings and articles available on the course's virtual campus
  • https://www.bioconductor.org/

Software

R: https://www.r-project.org/

Rstudio: https://www.rstudio.com/


Groups and Languages

Please note that this information is provisional until 30 November 2025. You can check it through this link. To consult the language you will need to enter the CODE of the subject.

Name Group Language Semester Turn
(PLAB) Practical laboratories 1 Catalan second semester afternoon
(TE) Theory 1 Catalan second semester afternoon