Current Topics in Bioinformatics
Code: 105065Credits: 3
| Degree programme | Type | Course |
|---|---|---|
| Genetics | OP | 4 |
Contact lecturer
- Name :
- Marta Coronado Zamora
- Email :
- marta.coronado@uab.cat
Teaching staff
- Marta Coronado Zamora
Group languages
You can consult this information at the end of the document.
Prerequisites
- It is recommended to have completed the courses Bioinformatics (3rd year), Genomics, Proteomics and Interactomics (3rd year) and the Programming module within the Instrumental Techniques course (2nd year) of the Degree in Genetics.
- Basic knowledge of a programming language (such as Python or R) and prior experience with the Linux environment are essential to follow the practical sessions and complete the continuous assessment activities.
- A level B1.2 of English or equivalent is recommended.
Objectives
The purpose of this course is to introduce students to current applications of bioinformatics in genomics and other omics disciplines through entirely practical sessions and lectures delivered by experts in the field. Rather than being a cumulative course, it is designed as a transversal subject aimed at providing a broad overview of the concepts, methodologies, and applications that shape bioinformatics.
The main objective is to provide students with the knowledge and skills required to understand and apply bioinformatics tools in different areas of genomic research and omics sciences. The content taught and the activities carried out throughout the course offer a global perspective on the potential of bioinformatics in both basic and applied research.
Learning outcomes
- CM28 (Evaluate the biological and biomedical relevance of the findings derived from the analysis of genomic, omics and variability data.) Evaluate the biological and biomedical relevance of the findings derived from the analysis of genomic, omics and variability data.
- KM21 (Describe the organisation of nuclear and organelle genomes, integrating bioethical implications and the gender perspective in genomics.) Describe the organisation of nuclear and organelle genomes, integrating bioethical implications and the gender perspective in genomics.
- KM22 (Describe experimental technologies and methods for structural and functional analysis of genomes and other omics.) Describe experimental technologies and methods for structural and functional analysis of genomes and other omics.
- SM25 (Use bioinformatics tools and databases for the management and analysis of genomic and sequencing data.) Use bioinformatics tools and databases for the management and analysis of genomic and sequencing data.
- SM26 (Interpret results of intraspecific association and variation studies to understand the basis of biological traits, integrating the impact of sex and gender.) Interpret results of intraspecific association and variation studies to understand the basis of biological traits, integrating the impact of sex and gender.
- SM27 (Apply functional omics analysis strategies for the investigation of gene expression and protein function.) Apply functional omics analysis strategies for the investigation of gene expression and protein function.
Contents
The course consists of practical sessions and lectures delivered by renowned specialists in different areas and fields of bioinformatics.
Practical sessions
The practical sessions will take place in the computer laboratory using the faculty's computers. Students will work in groups (3-4 students), promoting active learning and fostering analytical and synthesis skills, critical thinking, and problem-solving abilities.
The practical sessions are divided into four main activities that will introduce the basic bioinformatics workflows: from data management and processing using Linux, to data visualization and report generation with R, and subsequent functional analyses to address biological questions using R packages and other bioinformatics tools. The practicals are divided into two major sections: Part I: Basic Concepts of Bioinformatics Workflows and Part II: Solving Real Genomics Case Studies.
These practical sessions also aim to develop additional skills that are highly valuable in research but are rarely experienced during undergraduate studies, such as learning how to transform data into effective visualizations for communication, conducting reproducible research using GitHub, and writing scientific reports using LaTeX.
Conferences by invited guests
Attendance at the guest lectures (1-2 hours per lecture) is mandatory and in person. These lectures will be delivered by invited experts in the field of bioinformatics and will be taught in English (although some may be delivered in Catalan or Spanish, with prior notice). They will cover a variety of topics related to the practical applications of bioinformatics and genetics in different settings, including hospitals, private companies, and academic institutions. They will also address more specific and timely topics, such as cancer research. The final lecture dates will be continuously updated in the course calendar and announced through the communication tools available on Moodle. All lectures will be held in person.
Learning activities and methodology
| Title | Hours | ECTS | Learning outcomes |
|---|---|---|---|
| Theoretical-practical sessions | 12 | 0.48 | CM28, KM21, KM22, SM25, SM26, SM27 |
| Study/Problem solving | 25 | 1 | CM28, KM21, KM22, SM25, SM26, SM27 |
| Lectures | 17 | 0.68 | |
| Portfolio | 20 | 0.8 | CM28, KM21, KM22, SM25, SM26, SM27 |
In-person learning activities and autonomous learning
- A cooperative learning experience will be implemented. Each group will be responsible for managing and solving practical case studies independently.
- The four practical sessions will be interconnected, as the results or methodology from one practical session will serve as the basis for the next.
- Active participation, work management, and discussion of the knowledge acquired will constitute an essential part of each student's role.
- At the end of the course, each group will deliver a seminar on a topic of their choice within the field of bioinformatics, which will be evaluated by both the other students and the teaching staff.
Lectures
Several lectures will be delivered by experts in their respective research or professional fields, providing a realistic view of bioinformatics as a key tool for addressing questions in both basic and applied biological research. Particular emphasis will be placed on the importance of data analysis and management in the current era of big data. Other lectures will address topics of practical interest, such as how to survive a PhD, how to communicate science through a real-life example, and how the scientific world works. The different career opportunities available in bioinformatics, both in academia and in the private sector, will also be explored.
Assessment
Continuous assessment activities
| Title | Weight | Hours | ECTS | Learning outcomes |
|---|---|---|---|---|
| Seminar lecture | 20% | 1 | 0.04 | CM28, KM22, SM26, SM27 |
| Soft skills | 10% | 0 | 0 | CM28, KM21, KM22, SM25, SM26, SM27 |
| Portfolio | 70% | 0 | 0 | SM25, SM26, SM27 |
Assessment will be based on the submission of a portfolio and a group presentation on a bioinformatics topic chosen by the students.
Portfolio (70%). The portfolio will be submitted as a group assignment, although each student will have an individual folder in which they will collect and organize their own material. The portfolio will include the learning evidence generated throughout the course, such as the submissions of the four group practical assignments, individual reflections or essays, and other complementary materials.
Presentation (20%). Each group will deliver a 15–20-minute oral presentation on a freely chosen topic in bioinformatics.
Attendance and participation (10%). Active participation will be assessed, particularly through asking questions and interacting with the invited speakers during the lectures.
Guidelines
- Only students whose names appear on the group submissions will be assessed.
- The seminar and group assignments will be evaluated through peer assessment and self-assessment.
- The course will be passed when the average mark of the assessment activities is equal to or higher than 5. Given the continuous and transversal nature of the assessment, students must attend at least 80% of the scheduled sessions. Otherwise, they will receive a grade of "Not assessed".
Single assessment
This course does not provide a single-assessment system for the 2026/2027 academic year.
Use of Artificial Intelligence
The use of Artificial Intelligence (AI) technologies is permitted exclusively for support tasks such as literature searches, information retrieval, reviewing R code, proofreading texts, or translations. Students must clearly identify any content generated using AI, specify the tools employed, and include a critical reflection on how these tools influenced both the process and the final outcome. Failure to disclose the use of AI will be considered a breach of academic integrity and may result in partial or total penalties, as well as additional sanctions in serious cases.
Protocol in cases of academic misconduct
Any irregularity during an assessment activity (academic fraud, plagiarism, or inappropriate use of AI, except where explicitly authorized) that may significantly affect the grade will result in a mark of 0 for that assessment.
If the course syllabus establishes a minimum grade requirement for that assessment in order to pass the course, or if multiple irregularities occur in the same course, the final course grade will be 0. Furthermore, disciplinary proceedings may be initiated against any student involved in such misconduct.
Bibliography
Books available in the Biociències library:
- Claverie, Jean-Michel & Notredame, Cedric. (2007). Bioinformatics for dummies. (2nd ed.) Clav Wiley Pub. (https://bibcercador.uab.cat/permalink/34CSUC_UAB/1eqfv2p/alma991010022259706709)
- Lesk, Arthur M. (2019). Introduction to bioinformatics. (5th ed.) Oxford University Press Lesk (https://bibcercador.uab.cat/permalink/34CSUC_UAB/1eqfv2p/alma991010637336206709)
- Pezzullo, John C. (2013). Biostatistics for dummies. John Wiley & Sons Pezz (https://bibcercador.uab.cat/permalink/34CSUC_UAB/1eqfv2p/alma991010890868306709)
- Samuelsson, Tore. (2012). Genomics and bioinformatics : an introduction to programming Sam tools for life scientists. Cambridge University Press (https://bibcercador.uab.cat/permalink/34CSUC_UAB/1eqfv2p/alma991003255899706709)
- Samuelsson, Tore. (2012). Genomics and bioinformatics : an introduction to programming Sam tools for life scientists. (1st ed.) Cambridge University Press (https://bibcercador.uab.cat/permalink/34CSUC_UAB/1eqfv2p/alma991010751701806709)
- Shamir, Ron & Pevzner, Pavel. (2011). Bioinformatics for biologists. Cambridge University Sha Press (https://bibcercador.uab.cat/permalink/34CSUC_UAB/1eqfv2p/alma991003200439706709)
- Shamir, Ron & Pevzner, Pavel. (2011). Bioinformatics for biologists. Cambridge University Sha Press (https://bibcercador.uab.cat/permalink/34CSUC_UAB/1eqfv2p/alma991010751999106709)
- Wickham, Hadley. (2009). Ggplot2 : elegant graphics for data analysis. Springer Wick (https://bibcercador.uab.cat/permalink/34CSUC_UAB/1eqfv2p/alma991001327759706709)
Articles
- Himelblau, E. (2021). A cartoon guide to bioinformatics by a novice coder. Nature. https://doi.org/10.1038/d41586-021-01485-y
- Markowetz, F. (2017). All biology is computational biology. PLoS Biology, 15(3), e2002050. https://doi.org/10.1371/journal.pbio.2002050
- Webb, S. (2018). Deep learning for biology. Nature, 554(7693), 555–557. https://doi.org/10.1038/d41586-018-02174-z
- Mardis, E.R. (2010). The $1,000 genome, the $100,000 analysis? Genome Medicine, 2, 84. https://doi.org/10.1186/gm205
- Tyler-Smith, C., et al. (2015). Where next for genetics and genomics? PLoS Biology, 13(7), e1002216. https://doi.org/10.1371/journal.pbio.1002216
- Tippmann, S. (2015). Programming tools: Adventures with R. Nature, 517(7532), 109–110. https://doi.org/10.1038/517109a
- Piccolo, S.R., et al. (2023). Evaluating a large language model's ability to solve programming exercises from an introductory bioinformatics course. PLoS Computational Biology, 19(9), e1011511. https://doi.org/10.1371/journal.pcbi.1011511
- Kovalevskiy, O., et al. (2024). AlphaFold two years on: Validation and impact. Proceedings of the National Academy of Sciences, 121(34), e2315002121. https://doi.org/10.1073/pnas.2315002121
- Carey, M.A., & Papin, J.A. (2018). Ten simple rules for biologists learning to program. PLoS Computational Biology, 14(1), e1005871. https://doi.org/10.1371/journal.pcbi.1005871
Software
- Operating system: Linux
- Programming languages: bash and R
- Software: RStudio and Jupyter Notebook
- R packages: ggplot2, shiny, rmarkdown, knitr, BiocManager, DESeq2, clusterProfiler, dplyr, biomaRt, ggrepel, pheatmap, org.Hs.eg.db, pathview, Seurat
All the software is installed on the computers at the faculty. The usage of the faculty computers is recommended.
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
| Type of teaching | Group | Language | Semester | Shift |
|---|---|---|---|---|
| (TE) Theory | 64 | English | first semester | morning-mixed |
| (PLAB) Practical laboratories | 641 | English | first semester | morning-mixed |
| (SEM) Seminars | 641 | English | first semester | morning-mixed |