BIOINFORMATICS

The Bioinformatics course lies at the intersection of life sciences, computer science, and quantitative methods, with the aim of providing students with the tools necessary to analyze biological data through computational approaches.
Considering the background of students in Physical Engineering, the course is designed starting from an essential introduction to the fundamental concepts of molecular biology (DNA, RNA, proteins, and the flow of genetic information), treated in a way that supports quantitative analysis rather than a descriptive approach. The emphasis is therefore not on memorizing biological content, but on understanding biological data as complex systems to be modeled, analyzed, and interpreted.

By the end of the course, the student will be able to:
• Understand the main computational methods applied to biology
• Analyze biological sequences (DNA, RNA, and proteins)
• Use biological databases and bioinformatics tools
• Interpret and analyze “omics” data (genomics, metagenomics, transcriptomics, and translatomics/proteomics)
• Develop a systems-oriented mindset to understand the difficulty of interpreting and simplifying biological systems

No prerequisites are required for this course, but students are recommended to have a basic knowledge of computer science and biology.

The course introduces the fundamentals of bioinformatics, starting from an essential presentation of the molecular biology concepts necessary for understanding biological data, including the structure and function of DNA, RNA, and proteins, genome organization, and the flow of genetic information. The main sequencing technologies and the characteristics of the data they produce will also be described.
Subsequently, the course addresses the representation and analysis of biological sequences, with particular attention to standard formats (FASTA, FASTQ, GenBank), local and global alignment algorithms, and similarity search tools such as BLAST. The concepts of homology and substitution matrices will be introduced.
Part of the course is dedicated to the use of biological databases (such as GenBank, ENA, UniProt, and the Protein Data Bank) and bioinformatics tools for data access, integration, and annotation, including major genome browsing platforms.
Significant emphasis is placed on the analysis of “omics” data, including elements of genomics (sequencing, assembly, and annotation), transcriptomics (RNA-seq and gene expression analysis), metagenomics (computational study of microbial communities and their taxonomic and functional classification), and proteomics/translatomics. Principles of data visualization will also be presented, with particular attention to the effective representation of complex biological data (e.g., heatmaps, PCA, clustering, biological network graphs), as a fundamental tool for exploring and interpreting results.
The learning path is further enriched by case studies, group work, and journal club activities, aimed at integrating theoretical knowledge with the analysis of real bioinformatics problems. Case studies will allow the exploration of concrete applications, from problem formulation to result interpretation, while group work develops transversal skills such as collaboration, project management, and scientific communication. Journal club activities will foster critical reading of the scientific literature, with attention to computational methodologies, evaluation of results, their visualization, and the limitations of studies, promoting independent judgment and the ability to connect theory and practice.

Holmes, S., & Huber, W. (2019). Modern Statistics for Modern Biology. Cambridge University Press.
Buffalo, V. (2015). Bioinformatics Data Skills: Reproducible and Robust Research with Open Source Tools. O’Reilly Media.
Ulteriore materiale didattico sarà fornito dal docente e includerà articoli scientifici peer-reviewed, review di settore e risorse online (link a database, software e tutorial), utili per l’approfondimento autonomo e per le attività di journal club.

Regular and active participation in lectures will be considered a positive element in the evaluation. The oral exam is divided into two parts. The first part consists of a group presentation of a research article in class on topics related to omics data analysis, accounting for 30% of the total course grade. The second part consists of at least three open-ended questions covering all topics addressed during the course, accounting for 70%.

A fully successful exam (27-30/30) will be deemed when a solid and broad mastery of the concepts discussed during the classes and journal clubs is demonstrated. An average grade (22-26/30) will be the result of fairly complete understanding of individual themes but with limited interconnection among subjects. A pass level (18-21/30) will correspond to a minimum knowledge of individual notions.

oral

The lecturer has a duty to ensure that the rules regarding the authenticity and originality of exam tests and papers are respected. Therefore, if there is suspicion of irregular conduct, an additional assessment may be conducted, which could differ from the original exam description.

30 cum laude: Excellent with distinction. The student demonstrates exceptional mastery of the subject, original critical analysis, and creative application of concepts beyond expectations.
30: Excellent. The student demonstrates complete and in-depth understanding of concepts, applying knowledge rigorously and communicating with precision.
28-29: Outstanding. The student demonstrates very solid knowledge of the subject with well-developed critical analysis skills.
26-27: Very good. The student possesses good mastery of content with ability to make connections and analysis.
24-25: Good. The student demonstrates adequate understanding of the main concepts with correct application ability.
22-23: Satisfactory. The student shows a decent knowledge of fundamental content, with some inaccuracies.
20-21: More than sufficient. The student possesses a basic but complete knowledge of essential topics.
18-19: Sufficient. The student demonstrates minimal but acceptable knowledge of fundamental content.
< 18: Insufficient. The student presents serious gaps in understanding and application of fundamental concepts.

The teaching methods integrate theoretical instruction with active learning approaches to bridge concepts and real-world bioinformatics applications. Case studies are used to explore concrete problems, guiding students from problem formulation to the interpretation of results. Group work fosters the development of transversal skills such as collaboration, project management, and scientific communication. In addition, journal club activities encourage critical reading of the scientific literature, with a focus on computational methodologies, evaluation and visualization of results, and awareness of study limitations, thereby promoting independent judgment and the ability to connect theory with practice.

This subject deals with topics related to the macro-area "Human capital, health, education" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development