Universitat Internacional de Catalunya

Introduction to Bioinformatics

Introduction to Bioinformatics
6
13489
2
Second semester
OB
BASIC HEALTH INFORMATICS TOOLS
Main language of instruction: Spanish

Other languages of instruction: Catalan, English

Teaching staff


Questions will be answered in person before or after class, or via e-mail.

Teachers:
  • Dr. CHOROSTECKI, Uciel Pablo (upchorostecki@uic.es)

 
  • Dra. OZKAN, Selen (sozkan@uic.es)

  • Dr. FERNÁNDEZ TORRAS, Adrià (afernandezto@uic.es)

  • Dra. MANCINI, Estefania

  • AIRA, Nicolas (naira@uic.es)

Introduction

Large amounts of biomedical data are generated daily, whether from research laboratories, clinical laboratories, or private companies. It is necessary to improve our ability to understand and analyze this type of data to fully leverage its capacity to generate new scientific advances and improve patient care. For those who are not bioinformaticians, handling large amounts of data remains a daunting task.

This course introduces students to the fundamental concepts and tools of bioinformatics, focusing on its applications to understand and solve biological problems. Students will explore structural bioinformatics, omics, databases, and the role of artificial intelligence in biomedical research.

Pre-course requirements

To follow this class, previous knowledge in genetics, biomolecules and molecular biology is needed. 

Additional experience with the use of computers, web browsers and the internet and use of spreadsheets software is highly recommended.

Objectives

  • Explain the principles and applications of bioinformatics in biomedicine. 
  • Foster the development of skills in the use of bioinformatics tools for sequence analysis, protein structure modeling, and database management. 
  • Introduce next-generation sequencing (NGS) and omics techniques in clinical and research settings. 
  • Teach students the integration of artificial intelligence in biomedical data analysis.

Competences/Learning outcomes of the degree programme

  • CB01 - Students must demonstrate that they have and understand knowledge in an area of study that is based on general secondary education, and it tends to be found at a level that, although it is based on advanced textbooks, also includes some aspects that involve knowledge from the cutting-edge of their field of study.
  • CB03 - Students must have the ability to bring together and interpret significant data (normally within their area of study) to issue judgements that include a reflection on significant issues of a social, scientific and ethical nature.
  • CB04 - That students can transmit information, ideas, problems and solutions to specialist and non-specialist audiences.
  • CE17 - To identify and know how to use basic tools from the field of bioinformatics and how to analyse the structure and interaction of the main biomolecules.
  • CG04 - To use the bioinformatics tools specific to the field of biomedical research.
  • CT01 - To develop the organisational and planning skills that are suitable in each moment.
  • CT02 - To develop the ability to resolve problems.
  • CT03 - To develop analytical and summarising skills.
  • CT04 - To interpret experimental results and identify consistent and inconsistent elements.
  • CT05 - To use the internet as a means of communication and a source of information.
  • CT06 - To know how to communicate, give presentations and write up scientific reports.
  • CT07 - To be capable of working in a team.
  • CT08 - To reason and evaluate situations and results from a critical and constructive point of view.
  • CT09 - To have the ability to develop interpersonal skills.
  • CT10 - To be capable of autonomous learning.
  • CT11 - To apply theoretical knowledge to practice.
  • CT12 - To apply scientific method.
  • CT13 - To be aware of the general and specific aspects related to the field of nutrition and ageing.
  • CT14 - To respect the fundamental rights of equality between men and women, and the promotion of human rights and the values that are specific to a culture of peace and democratic values.

Learning outcomes of the subject

At the end of the course, students should be able to:

  • Describe the transition from early computational tools to modern bioinformatics technologies, as well as identify the equipment and software requirements necessary for this discipline.
  • Apply key tools and techniques for the analysis of biological data in the area of human health and biomedicine through the exploration, management, and interpretation of bioinformatic data and algorithms, and of sequence and protein databases.
  • Analyze and apply sequencing and omics technologies in the clinical and research setting, implementing analysis strategies such as NGS and RNA-Seq.
  • Use structural visualization tools and databases to analyze protein structures and their implications in the design of biomedical therapies.
  • Identify and explain machine learning models, the AI workflow, and their applications in biological data analysis, drug discovery, and innovative tools such as AlphaFold and ChatGPT.

Syllabus

  1. Introduction to Bioinformatics

    1. History of Computing in Biology: From Early Tools to Modern Bioinformatics.

    2. Equipment and Software Requirements in Bioinformatics.

    3. Data Mining in Bioinformatics

    4. Key Algorithms in Bioinformatics.

    5. Main Applications of Bioinformatics in Human Health.

    6. History and Evolution of Sequencing Technologies.

    7. Sequencing Strategies.

    8. Sequence Databases.

    9. Applications of High-Throughput Sequencing in Human Genetics.

  2. Structural Bioinformatics

    1. Introduction: Why Protein Structure is Important.

    2. Protein Sequence-Structure-Function Relationship.

    3. Techniques for Visualizing Protein Structures.

    4. Role in Biomedicine.

    5. Future of Structural Bioinformatics in Biomedicine.

    6. Exploring Protein Sequences and Functional Relationships with UniProt.

    7. Structural Databases and Protein Visualization.

  3. Omics

    1. Next-Generation Sequencing (NGS).

    2. Historical Description and Techniques.

    3. Clinical Applications: From Gene Panels to Whole-Genome Sequencing.

    4. RNA-Seq: Fundamentals of Gene Expression and its Implications for Health.

    5. RNA-Seq Workflows and Data Interpretation.

    6. Single-Cell Experiments, Epigenomics, and Multi-Omics.

    7. Techniques and Applications in Biomedical Research.

  4. Databases

    1. Overview of Essential Databases in Bioinformatics.

    2. Practical Sessions: Data Navigation, Retrieval, and Analysis.

  5. Introduction to Artificial Intelligence

    1. Exploring Advanced Biological Data Modalities.

    2. Expanding the Scope of Biomedicine.

    3. A Friendly Introduction to Artificial Intelligence.

    4. Overview of Machine Learning Models.

    5. The Machine Learning Pipeline: From Data Preparation to Model Evaluation.

    6. Applications of AI in Biomedicine and Drug Discovery (AlphaFold, ADMETlab, and ChatGPT).

 

Practical Sessions:

Teaching and learning activities

In person



Fully in-person modality in the classroom

1. Lectures - 20 hours: presentation of a theoretical topic by the teaching staff.

2. Case Methods (CM) - 28 hours: presentation of a real or hypothetical situation in small groups. Students work together with the teaching staff to solve practical questions. The teaching staff intervenes actively and, if necessary, provides new knowledge.

3. Practical Classes - 12 hours: experimental demonstration in the laboratory on concepts studied in theoretical classes under the supervision of the teaching staff.

4. Virtual Education (VE): online material that students can consult from any computer, at any time, and that will contribute to the learning of concepts related to the subject.

Evaluation systems and criteria

In person



1) Student in first call:

  • Final exam: 40%
  • Case method solution: 30%
  • Practical solution: 30%

2) Students in the second call or later: the same criteria as in the first call. The grade of any of the approved parts will be saved.

  General points to consider about the evaluation system:
  • A minimum grade of 5 on the final exam is required to calculate the overall grade.
  • In addition, to pass the course, a general average of 5 or higher in all evaluations is necessary.
  • Due to the continuous nature of the assessment, it is not possible to pass the course without attending at least 75% of the scheduled sessions.
  • Inappropriate use of electronic devices (such as recording and sharing student or teaching staff content during sessions, or using devices for non-educational purposes) may result in expulsion from the class.
  • The teaching staff reserves up to 10% of the total grade to be awarded based on subjective criteria, such as commitment, participation, compliance with basic rules, etc.

Bibliography and resources

  1. Applied Bioinformatics, 2nd Edition. Springer (2018). ISBN: 978-3-319-68299-0
  2. Biomedical Informatics, 4th Edition. Springer (2014). ISBN:  978-1-4471-4473-1
  3. Fundations of Programming Languages. 2nd Edition. Springer (2017). ISBN: 978-3-319-70789-1
  4. Bioinformatics with Python cookbook, 2nd Edition ISBN-10: 1789344697
  5. H. Wickham. R packages. O'Reailly, Sebastopol, 2015.

Evaluation period

E: exam date | R: revision date | 1: first session | 2: second session:
  • E1 27/05/2025 A09 16:00h
  • E1 27/05/2025 A08 16:00h
  • E2 01/07/2025 A04 11:00h