Guide in PDF format

Bioinformatics

Code: 104415
Credits: 6

2026/2027

Degree programme	Type	Course
Computational Mathematics and Data Analytics	OP	4

Contact lecturer

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

Teaching staff

: Juan Ramón Gonzalez Ruiz
: Gianluigi Caltabiano
: Angel Gonzalez Wong
: Carolina Soriano Tarraga

Group languages

You can consult this information at the end of the 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

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:

Computational Methodologies Applied to Drug Discovery
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

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

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.

Learning activities and methodology

Title	Hours	ECTS
Practical sessions	21	0.84
Preparation of Research Project	20	0.8
Tutoring	10	0.4
Study	70	2.8
Presentation of Research Project	3	0.12
Theory classes	21	0.84

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.

In this course, the use of Artificial Intelligence (AI) technologies is allowed as an integral part of the development of the work, provided that the final result reflects a significant contribution from the student in terms of analysis and personal reflection. The student must clearly identify which parts have been generated using this technology, specify the tools used, and include a critical reflection on how these have influenced both the process and the final outcome of the activity. Lack of transparency in the use of AI will be considered a breach of academic integrity and may result in a penalty in the grade for the activity, or more serious sanctions in severe cases.

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

Continuous assessment activities

Title	Weight	Hours	ECTS	Learning outcomes
Presentation Research Project	20	2	0.08	CM35, KM29, KM30, SM36, SM38
Presentation of practicum reports	30	0.5	0.02	CM34, CM35, KM29, SM36, SM37, SM38
Presentation class exercises	30	0.5	0.02	KM29, KM30, KM31, SM37, 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.

The commission of any irregularity in an assessment activity (academic fraud, plagiarism, or improper use of AI, unless such use is expressly authorized in the course guide) that may lead to a significant change in the grade will result in that activity being graded with a 0. If the course guide establishes that passing the subject requires obtaining a minimum mark in that assessment activity, or if multiple irregularities occur in the assessment activities of the same subject, the final grade for the subject will be 0. In addition, disciplinary proceedings may be initiated against any student who incurs any of these irregularities.

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/

Course groups and languages

The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE