Big Data Analysis in Bioinformatics
Code: 104886 ECTS Credits: 6| 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
Learning Outcomes
- CM14 (Competence) Propose the statistical model needed to analyse data sets belonging to real studies.
- KM17 (Knowledge) Recognise the statistical models for the analysis of data with different structures and complexities that frequently appear in different fields of application.
- 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.
- SM16 (Skill) Select appropriate sources of information for the statistical work.
- SM17 (Skill) Discuss scientific articles in which the analysis of a study of the different areas of application is considered.
- SM18 (Skill) Refine the information available for subsequent statistical processing.
- 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 |