Logo UAB

Big Data Analysis in Bioinformatics

Code: 104886 ECTS Credits: 6
2025/2026
Degree Type Year
Applied Statistics 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. CM14 (Competence) Propose the statistical model needed to analyse data sets belonging to real studies.
  2. KM17 (Knowledge) Recognise the statistical models for the analysis of data with different structures and complexities that frequently appear in different fields of application.
  3. KM18 (Knowledge) Recognise the language of applications of economics and finances, biomedical science and engineering, provided by research and innovation in the field of statistics.
  4. SM16 (Skill) Select appropriate sources of information for the statistical work.
  5. SM17 (Skill) Discuss scientific articles in which the analysis of a study of the different areas of application is considered.
  6. SM18 (Skill) Refine the information available for subsequent statistical processing.
  7. SM19 (Skill) Analyse complex data, whether this is due to their characteristics or their size.

Content

MODULE 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.

MODULE 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
Practicum Reports Preparation 30 0.5 0.02 CM14, KM17, KM18, SM16, SM18, SM19
Presentation class exercises 30 0.5 0.02 CM14, KM17, KM18, SM18, SM19
Presentation Research Project 20 2 0.08 KM18, SM16, SM17, SM18, SM19
Theoretical-Practical Exam 20 2 0.08 CM14, KM17, KM18, SM19

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

  • 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
  • Buffalo, V. Bioinformatics Data Skills. O’Reilly Media, 2015.
  • Lesk, A. M. Introduction to Bioinformatics. Oxford University Press, 2019.
  • González, J. R., 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