Universitat Internacional de Catalunya
Core Bioinformatics: from Molecular Structure to Omics Data in Biomedicine
Other languages of instruction: Catalan, Spanish
Teaching staff
Questions will be resolved in person or by videoconference with the corresponding academic tutor of each block.
If necessary, please make an appointment with the work experience coordinator: srnajle@uic.es
Introduction
This course provides a foundational understanding of how bioinformatics tools allow us to extract knowledge from molecular and omics data. It is structured in two complementary blocks:
The first block focuses on Structural Bioinformatics, a foundational field that aims to understand biological function through molecular structure. Its relevance has been recently recognized with the 2024 Nobel Prize in Chemistry, awarded for crucial advances in protein structure prediction through the use of AI (AlphaFold software). Students will learn how molecular structure underlies biological function, how to retrieve and interpret structural information, and how to predict or assess the effect of genetic variants.
The second block introduces the principles of Omics Bioinformatics, where bioinformatics enables the handling of large-scale molecular data from high-throughput experiments. This includes gene expression, single-cell biology, and epigenomics, all of which are key to modern biomedical research.
The course blends conceptual understanding with hands-on exploration of bioinformatics resources, promoting critical thinking about the quality, interpretation, and biomedical relevance of molecular data.
This course aligns with several United Nations Sustainable Development Goals (SDGs) by fostering a data-driven and ethically responsible approach to biomedical science. Through the exploration of molecular structures, omics data, and computational tools, it supports SDG 3 (Good Health and Well-being) by advancing understanding of the molecular mechanisms underlying human disease and enabling precision medicine approaches. By offering rigorous, hands-on training in bioinformatics and data interpretation, it contributes to SDG 4 (Quality Education), empowering students with advanced skills essential for modern biomedical research. Its emphasis on innovative computational methods—such as structure prediction with AlphaFold, genomics interpretation, and multi-omics data integration—directly promotes SDG 9 (Industry, Innovation and Infrastructure), strengthening the technological foundations of biomedical research and biotechnology. Finally, through the use of open-access databases, collaborative projects, and community-driven research practices, the course advances SDG 17 (Partnerships for the Goals), encouraging international cooperation and open science for global health impact.
Pre-course requirements
It is recommended that students have previously completed the courses Introduction to Bioinformatics and Biomolecular Interactions.
Objectives
- Introduce the principles of structural bioinformatics to help students understand how molecular organization underpins biological function and disease mechanisms.
- Develop practical skills in the use of databases and visualization tools for exploring, interpreting, and critically evaluating structural and omics data.
- Familiarize students with modern approaches to structure prediction (e.g., homology modeling, AlphaFold) and their application in biomedical contexts.
- Train students to critically analyze and interpret omics information and datasets, fostering an integrated understanding of complex biological systems.
- Promote translational thinking by highlighting how bioinformatics tools and multi-omics integration contribute to biomedical research and clinical decision-making.
Competences/Learning outcomes of the degree programme
- CN14 - Identify the principles of biomedical sciences related to health, as well as the basic concepts and tools that have an impact on Biomedical Sciences and allow them to work in any of its fields (biomedical companies, bioinformatics labs, research laboratories, clinical analysis companies, etc.).
- CP05 - Apply biological foundations in the search for practical solutions to health problems, following ethical standards and scientific rigour and respecting fundamental equal rights between men and women, and the promotion of human rights and the values inherent in a peaceful society of democratic values that includes inclusive, non-discriminatory language without stereotypes.
Learning outcomes of the subject
By the end of the course, students will be able to:
- Explain the relationship between molecular structure and function by analyzing structural organization, functional domains, and structural alterations linked to disease.
- Access, visualize, and interpret structural data using key databases (e.g., PDB, UniProt) and molecular graphics software (e.g., PyMol), applying these skills to real biomedical cases.
- Evaluate and apply structure prediction methods (e.g., homology modelling, AlphaFold) by understanding their principles, strengths, limitations, and relevance in biomedical research.
- Assess the structural and functional impact of genetic variants through mapping, prediction tools, and case-based interpretation in clinical and research contexts.
- Interpret and analyse omics datasets (genomic, transcriptomic, single-cell, and epigenomic) by applying bioinformatics approaches to identify meaningful biological patterns.
- Integrate multi-omics data (variants, expression, epigenetic information) to derive biomedical insights and discuss the role of bioinformatics in advancing clinical decision-making.
Syllabus
Block I: Structural Bioinformatics – Understanding Function at the Molecular Level (15 hours)
1. From Sequence to Structure: The Molecular Basis of Protein Function
- From sequence to structure: levels of organization (1D to quaternary).
- Sequence-structure-function relationships
- Structural disruption in disease: stability, interactions, moonlighting proteins.
- Structural basis of molecular recognition, catalysis and regulation.
- Why protein structure matters in health and disease.
2. Exploring Structural Data: Databases and Visualization
- Experimental methods for determining protein structures.
- Key resources: UniProt, PDB, EMDB, Pfam.
- Reading and interpreting structures: representations, quality, caveats.
- Visual exploration with PyMol (guided session).
- Practical: visualizing a pathogenic variant in 3D.
3. Structure, Stability and Function
- Molecular interactions that stabilize protein structures.
- Buried and exposed residues, binding sites and interfaces.
- Structural basis of molecular recognition and activity.
- Effects of structural disruption on protein function
4. Filling the Gaps: Predicting Structure When Experiments Fall Short
- Why structure prediction is a complex challenge.
- Sequence vs. structure conservation: what really matters.
- Introduction to multiple sequence alignment (MSA).
- Homology modelling and remote homology: principles and main tools.
- Sequence alignment, homology and structure conservation.
- Homology modelling and AlphaFold.
- Model confidence, quality and limitations.
- Practical interpretation of predicted structures.
5. Interpreting Genetic Variants through Structure
- Types of variants and their structural consequences.
- Mapping variants onto experimental and predicted structures.
- Computational prediction of functional impact.
- Biomedical case studies in variant interpretation.
- Practical cases: assessing variant pathogenicity in real biomedical scenarios.
Block II: Omics Bioinformatics – Navigating Biological Complexity through Data (15 hours)
1. What Omics Tell Us: From Genes to Systems
- The concept of omics in biomedicine.
- Why large-scale data matters in diagnosis and research.
- Core omics layers: genome, transcriptome, epigenome.
2. NGS in Practice: Interpreting Genome and Transcriptome Data
- Principles of Next-Generation Sequencing (NGS). Where does my data come from?
- One genome or many? How many? The importance of comprehensive datasets in medical genomics.
- RNA-Seq: quantifying gene expression.
- Quality control and common pitfalls.
- Practical: genome browser navigation and interpretation of simplified RNA-Seq outputs.
3. Single-Cell Omics: Cellular Heterogeneity Revealed
- What is single-cell RNA-seq and why does it matter?
- Main techniques and data characteristics.
- Analytical challenges: variability, dimensionality.
- Applications in immunology, cancer, and development.
4. Epigenomic Data: Seeing Beyond the Sequence
- Role of epigenetics in gene regulation.
- Key methods: ChIP-seq, ATAC-seq (conceptual overview)
- Practical interpretation: identifying meaningful epigenetic patterns.
5. The Big Picture: Integrating Data for Biomedical Insight
- Why integrate omics?
- Combining variant, expression, and epigenetic data.
- From data to phenotype: case-based reasoning.
- Closing discussion: the role of bioinformatics in clinical decision-making.
Teaching and learning activities
In person
Fully in-person classroom modality
Lectures – 8 hours: the instructor conveys knowledge in a classroom setting to the entire group of students.
Case Method (CM) – 20 hours: students, working individually or in groups, perform guided database navigation and data interpretation, or solve clinical cases provided by the instructor on that day. In case of the latter, during the class, students present their conclusions with the active participation of the instructor, who may introduce new concepts whenever necessary.
It is mandatory that every student bring its own personal computer to the CM lessons.
Evaluation systems and criteria
In person
Fully In-Person Classroom Modality
Students in the first call:
Midterm multiple-choice exam: 35%
Final multiple-choice exam: 35%
Case method: 20%
Presentation of a scientific article: 10%
Students in the second or subsequent calls: The grade obtained in the case method and presentation of a scientific article will be retained, and the final exam will account for 70% of the final grade.
Students repeating the course who wish to retake the midterm exam in the 3rd or 5th session may do so, provided they notify the course coordinator in advance.
General points to consider regarding the evaluation system:
1) To be eligible for grade averaging, a minimum score of 5 must be obtained in both the mid-term and the final exams. The minimum overall grade required to pass the course is 5.
2) The exams will be multiple-choice tests with four possible answers. One point will be awarded for each correct answer, and 0.33 points will be deducted for each incorrect answer.
3) Due to the continuous assessment nature of this course, it will not be possible to be evaluated if the student has not attended at least 75% of the total class hours.
4) Attendance to lectures is not mandatory, but attendees must follow the rules indicated by the lecturers. If arriving late, enter quietly without disturbing the class. If attendance is below 65%, class participation will be graded very low.
5) Improper use of electronic devices (including recording or sharing images, audio, or video of students or instructors during sessions, as well as using such devices for recreational rather than educational purposes) may lead to expulsion from the class.
Bibliography and resources
Block I:
Kessel, A., & Ben-Tal, N. (2018). Introducción a las proteínas: estructura, función y movimiento. Chapman y Hall/CRC.
Xiong, J. (2006). Bioinformática esencial. Cambridge University Press
Creighton, Thomas E. La química biofísica de los ácidos nucleicos y proteínas. Editorial Helvética, 2010
Bourne, P. E., & Weissig, H. (Eds.). (2010). Bioinformática estructural (2ª ed.). Wiley-Liss.
Block II:
Dandekar T. & Kunz M. (2023). Bioinformática. Un libro de texto introductorio.
Korpelainen E., Tuimala J., Somervuo P., Huss M. & Wong G. (eds.) (2013). Análisis de datos de RNA-seq. Un enfoque práctico.
Aizat, W. M., Baharun, S. N. & Goh, H-H. (Eds.). (2018). Aplicaciones ómicas para biología de sistemas.
Forero, D. A. (ed.). (2022). Bioinformática y investigación en genómica humana.