C8763 - LABORATORY OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY

Learning outcomes

At the end of the course, the student has the basic knowledge for developing and using tools for sequence and structure analysis in relation to the annotation problems in the genomic era. In particular, the student will be able to: discuss the theoretical basics of some machine learning tools (Neural Networks, Hidden Markov Models); selecting programs for problem solving; writing programs.

Course contents

Prerequisites

Basic knowledge of molecular biology, genetics, biochemistry, and bioinformatics is recommended. Familiarity with protein sequence and structure, fundamental concepts of computational biology, and basic principles of data analysis is also suggested.

Course topics

The course is organised into two modules (7 and 2 ECTS credits) covering theoretical and practical aspects of bioinformatics, computational biology, sequence analysis, and protein structure and function prediction.

Module 1 (7 ECTS credits)

The module is devoted to the study and application of bioinformatics approaches for the analysis of biological sequences, protein structures, and functional relationships, with particular emphasis on computational methods, their theoretical foundations, practical applications, and critical evaluation.

The topics covered include:

• the role of bioinformatics in modern molecular biology and computational biology;
• biological databases and analysis of Next Generation Sequencing (NGS) experiments;
• sequence annotation and the relationship between sequence, structure, and function;
• protein sequence analysis and protein structure comparison;
• generation of rules for sequence comparison based on structural information;
• statistical evaluation of sequence similarity and extreme value distributions;
• exploration of protein sequence space and the UniProt KnowledgeBase (UniProtKB);
• evolutionary insights derived from large-scale protein structure comparisons and analysis of the Protein Data Bank (PDB);
• theoretical foundations and practical applications of homology modelling;
• from protein sequence to structure and function;
• protein geometrical properties and principles of protein three-dimensional, secondary, and covalent structure;
• protein domains and structural classifications, including SCOP and CATH;
• functional domains, Gene Ontology (GO) terms, protein families, and their evolutionary relationships;
• historical development and perspectives of biosequence analysis;
• mapping structures onto sequences and sequences onto structures;
• propensity scales, propensity profiles, and sliding-window approaches;
• conditional probability approaches for secondary structure prediction;
• fundamentals of feed-forward neural networks and their applications in bioinformatics;
• training, testing, and evaluation of neural network-based prediction methods;
• critical evaluation of machine learning approaches, including Hidden Markov Models (HMMs) and neural networks;
• prediction and analysis of proteins with low sequence identity.
• Practical activities will focus on best practices in bioinformatics analysis, including:
• management and comparison of different sequence alignment approaches;
• protein structure modelling and validation of predicted three-dimensional models;
• comparison of protein modelling approaches, including SwissModeler;
• modelling of protein domains using Hidden Markov Models (HMMs);
• use of HMMER and statistical validation of predicted protein domains;
• comparison with curated resources such as Pfam.

Module 2 (2 ECTS credits)

The module focuses on sequence comparison methods and profile-based approaches for biological sequence analysis.

The topics covered include:

• sequence comparison and substitution matrices;
• exact algorithms for local and global sequence alignment;
• heuristic algorithms for database searching, including BLAST;
• profile-based search methods, including PSI-BLAST;
• profile modelling using Hidden Markov Models (HMMs).

The module emphasizes the theoretical principles underlying sequence comparison algorithms, the interpretation of alignment results, and the application of profile-based methods for the identification of evolutionary and functional relationships among biological sequences.

Learning activities

Students will perform practical analyses using bioinformatics databases and computational tools for sequence, structure, and domain analysis.

Particular attention will be devoted to understanding the theoretical principles underlying the methods, selecting appropriate computational approaches, evaluating prediction reliability, and interpreting biological results.

Students will also analyse and discuss research articles and review papers related to bioinformatics methods and applications. Activities may include guided exercises, discussion of scientific literature, and the development of a research project involving the application of bioinformatics tools to biological questions.

Readings/Bibliography

Study materials

The study material mainly consists of research articles and review papers selected by the instructor, covering methodologies in bioinformatics, computational biology, sequence analysis, structural bioinformatics, and protein modelling.

Additional in-depth readings may be suggested during the course in relation to the topics addressed.

Teaching materials

Lecture slides, scientific articles, practical protocols, and any supplementary materials will be made available through the Virtuale platform.

Teaching methods

The course combines lectures, practical exercises, and the development of a research project.

Teaching activities include:

• lectures by the instructor on theoretical principles and computational methods in bioinformatics and computational biology;
• practical sessions focused on the application of bioinformatics tools and databases for sequence, structure, and domain analysis;
• guided analysis and discussion of scientific articles, with particular attention to methodological aspects, computational approaches, validation procedures, and interpretation of results.

The teaching approach aims to develop students’ ability to apply bioinformatics methods, critically evaluate computational predictions, and understand the relationship between biological questions and computational solutions.

Assessment methods

Assessment is based on written examinations, a research project, and a final oral examination.

Written examinations aim to assess the student’s understanding of the theoretical principles and computational methods presented during the course.

The research project aims to evaluate the student’s ability to apply bioinformatics tools, retrieve and analyse biological information from databases, interpret computational results, and critically discuss the reliability of the obtained predictions.

The oral examination assesses:

• knowledge of the course topics;
• understanding of the theoretical principles underlying bioinformatics methods;
• ability to analyse and interpret sequence, structural, and functional information;
• ability to critically evaluate computational approaches and results;
• appropriate use of scientific terminology.

The final grade takes into account the results of the written examinations, the quality of the research project, and the performance in the oral examination.

Assessment criteria

• 18–21: basic knowledge of the topics and understanding of fundamental concepts.
• 22–25: good knowledge of the contents and ability to apply concepts and methods.
• 26–29: in-depth knowledge, autonomy in analysis, and correct application of bioinformatics approaches.
• 30–30 cum laude: complete mastery of the topics, critical thinking skills, ability to integrate different concepts, and excellent command of scientific language.

Use of Generative Artificial Intelligence

The research project may include a declared use of AI, accompanied by a mandatory critical analysis of the results obtained.

Teaching tools

• Virtuale platform for the distribution of teaching materials;
• lecture slides;
• scientific articles, online seminars, and review papers selected by the instructor;
• links to biological databases, software repositories, and other bioinformatics resources used during the course;
• datasets and additional in-depth materials.

The use of generative Artificial Intelligence tools is allowed as support for individual study (e.g., summarization, explanations, self-assessment, or further exploration), in accordance with the guidelines established for course assessment.

Students with Specific Learning Disorders (SLD) or temporary or permanent disabilities are encouraged to contact the University's dedicated office (https://site.unibo.it/studenti-con-disabilita-e-dsa/en). The office will propose any necessary accommodations, which must be submitted to the instructor for approval at least 15 days before the examination. The instructor will evaluate the appropriateness of such accommodations in relation to the intended learning outcomes.

Students who have been granted working student status should consult the dedicated University web page (https://www.unibo.it/it/studiare/guida-alla-scelta-del-corso/conciliare-studio-e-lavoro) to apply for this status and learn about the available support measures.

Office hours

See the website of Emidio Capriotti

See the website of Pier Luigi Martelli