C8335 - BASIC IN BIOINFORMATICS AND ARTIFICIAL INTELLIGENCE TOOLS IN BIOMEDICINE

Learning outcomes

By the end of the course, the student has a solid understanding of the main computational methods required for the analysis of biological omics data and the use of the most widely adopted public biological databases. The student knows how to write simple scripts in R and Python, design scalable approaches for handling large datasets, use specialized software for omics data analysis, and retrieve and extract information from specific databases. The student is also familiar with the most up-to-date approaches and emerging research areas in Artificial Intelligence.

Course contents

SPECIFIC PREREQUISITES FOR THIS COURSE

To better understand the topics covered in this course, students are advised to have basic knowledge of molecular and cellular biology, genetics, and the central dogma of molecular biology. Basic knowledge of statistics and familiarity with the use of a computer are also recommended. Previous experience in bioinformatics or computer programming is not required.

SPECIFIC CONTENTS OF THE COURSE

The syllabus is structured as follows:

Lectures (16 hours)

• The molecular biology paradigm and the role of bioinformatics: DNA, RNA and proteins; bioinformatics as a multidisciplinary field connecting biology, computer science, mathematics and statistics

• DNA and protein sequence alignment: principles of sequence comparison; pairwise, global and local alignments; sequence similarity searches

• Next-Generation Sequencing and short-read alignment: basic principles of NGS technologies; reference-based and de novo approaches; introduction to commonly used tools

• Sequence annotation and variant detection: principles and strategies for the functional annotation of biological sequences and the identification of single nucleotide polymorphisms and other sequence variants

• Comparative genomics and phylogenetics: principles of molecular evolution; construction and interpretation of phylogenetic trees

• Gene expression analysis: analysis of microarray and RNA-seq data; differential gene expression; functional interpretation of results and pathway enrichment analysis

• Metabarcoding and shotgun metagenomics: analysis of microbial communities in complex matrices

• applications of whole-genome sequencing to pathogen characterization and outbreak investigation

• Machine learning in bioinformatics: introduction to the main supervised and unsupervised learning approaches and their applications to biological classification and prediction problems

The main public biological databases and data repositories relevant to each topic will be presented throughout the course, together with strategies for retrieving, extracting and interpreting biological information.

Laboratory/Practical Sessions (36 hours)

• The bioinformatician’s computational environment: installation and use of an Ubuntu environment; use of the RekuLab computational workspace; introduction to R, Python and elementary Bash scripting; responsible use of generative Artificial Intelligence tools to support programming, code interpretation and debugging

• Sequence analysis workflows: practical exercises on BLAST sequence alignment and phylogenetic tree construction

• NGS short-read quality control, trimming and alignment; introductory analysis of metataxonomic datasets

• Development of a supervised predictive model: construction, training and evaluation of a machine-learning model, such as a support vector machine or random forest, applied to a biological classification problem

• Bioinformatics project: starting from a set of scientific questions and input datasets, students will design and complete an end-to-end bioinformatics project. The activity will include data preparation, selection and application of appropriate computational methods, interpretation of the results, and preparation of a written report to be presented and discussed during the examination

Readings/Bibliography

The teaching materials for this course are available on the Virtuale Learning Environment (https://virtuale.unibo.it/?lang=en ).

Supplementary reading:

• Introduction to Bioinformatics, Fifth Edition, Arthur Lesk

Teaching methods

The course includes theoretical lectures, guided computer-based practical sessions and project-based learning activities.

Lectures introduce the biological questions, computational principles and main bioinformatics methods covered in the course, supported by examples and demonstrations of relevant software and public databases.

During the practical sessions, students will work with biological datasets and apply the methods introduced in class using command-line tools, R, Python, Bash and dedicated bioinformatics software. Generative Artificial Intelligence tools will also be used to support programming and debugging, with particular attention to the critical evaluation of the generated outputs.

In the final part of the course, students will develop a complete bioinformatics project under supervision, from data analysis and interpretation to the preparation of a written report and oral presentation.

Assessment methods

The examination is designed to assess the achievement of the course learning outcomes, with reference to the topics covered during lectures and practical sessions.

Students are required to submit a final written report describing the bioinformatics project developed during the course. The report must present the scientific question, the dataset, the analytical methods applied, the results obtained and their biological interpretation.

The project will be presented and discussed orally. During the presentation, the instructor will ask questions concerning the project and the entire course syllabus in order to assess the student’s theoretical knowledge, practical skills, ability to interpret bioinformatics results and command of discipline-specific terminology.

The final grade will take into account the quality and completeness of the written report, the clarity of the oral presentation, the appropriateness of the analytical approach and the accuracy of the answers provided during the discussion.

The examination is considered passed only if both the written report and the oral discussion are satisfactory. The minimum passing grade is 18/30. The final result will be communicated at the end of the oral examination.

The following grading criteria will be applied:

• 18–22: basic knowledge of the course topics and limited ability to analyse and interpret the results;

• 23–26: adequate knowledge and correct application of the main bioinformatics methods;

• 27–29: broad knowledge, good analytical autonomy and appropriate use of discipline-specific terminology;

• 30–30L: comprehensive knowledge, excellent analytical and critical skills, and a clear and rigorous presentation of the project.

Students can register for exams through the AlmaEsami platform (http://almaesami.unibo.it/ ). Exams are scheduled during the designated periods in the academic calendar. Additional sessions are available for students beyond the standard program duration.

Students with learning disorders and/or temporary or permanent disabilities: please, contact the office responsible (https://site.unibo.it/studenti-con-disabilita-e-dsa/en/for-students ) as soon as possible so that they can propose acceptable adjustments. The request for adaptation must be submitted in advance (15 days before the exam date) to the lecturer, who will assess the appropriateness of the adjustments, taking into account the teaching objectives.

With regard to the learning assessment, limited, declared, and non-substantive use of AI is permitted for support activities, such as summarization and rephrasing. Substantive use of AI to complete parts of the assessment task is not permitted.

Teaching tools

Slide presentations will be used during lectures. Practical activities will be carried out through group work using students’ personal computers or the computers available in the computer room. Dedicated bioinformatics software, public databases and online resources will also be used during the practical sessions.

In case of difficulty understanding the course content, the instructor is available for clarification meetings, which must be scheduled via email.

Office hours

See the website of Valentina Indio