Bioinformatics
Academic Year 2023/2024 - Teacher: GIOVANNI MICALEExpected Learning Outcomes
General teaching training objectives in terms of expected learning outcomes.
- Knowledge and understanding: The course aims to give the knowledge and basic and advanced skills to the analysis of bioinformatics data.
- Applying knowledge and understanding: the student will acquire knowledge about the models and algorithms for analyzing bioinformatics data.
- Making judgments: Through concrete examples and case studies, the student will be able to independently develop solutions to specific problems related to bioinformatics data analysis.
- Communication skills: the student will acquire the necessary communication skills and expressive appropriateness in the use of technical language in the general area of bioinformatics and computational biology.
- Learning skills: The course aims to provide students with the necessary theoretical and practical methods to deal independently and solve new problems that may arise during a work activity. For this purpose, different topics will be covered in class by involving students in the search for possible solutions to real problems, using benchmarks available in the literature.
Course Structure
Lectures.
Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.
Required Prerequisites
Attendance of Lessons
To better follow lectures, slides are provided by the professor.
Slides are not a means for studying, but help to learn the topics explained during lectures.
Detailed Course Content
- Prerequisites of Biology
- Cells, genomes and evolution
- The genome and the genes
- Transcription
- Translation
- Coding and non-coding RNA
- Prerequisites of probability and statistics for bioinformatics
- R Programming language
- Pairwise and Multiple Alignment
- Biological databases
- Transcriptome analysis tools (Microarrays, Next-generation Sequencing) and biomarkers
- DNA sequencing and RNAseq pipeline
- R package Bioconductor and differential analysis in R
- Tools for mining of biological networks (Graph Matching, Network Biomarkers, Pathway Analysis, Network Alignment)
Textbook Information
We recommend the use of the text "Fondamenti di bioinformatica".
Authors: Manuela Helmer Citterich, Fabrizio Ferrè, Giulio Pavesi, Graziano Pesole, Chiara Romualdi.
Zanichelli Publisher (2018).
Other updated resources will be indicated by the lecturer in the slides used in class.
Course Planning
| Subjects | Text References | |
|---|---|---|
| 1 | Introduction: bioinformatics and the new medicine. | Zanichelli Chapters 2 and 16 |
| 2 | Biological pre-requisites: cells, genomes and evolution. | Zanichelli Chapter 1 |
| 3 | Biological pre-requisites: genes, transcription and translation. | Zanichelli Chapter 1 |
| 4 | Biological pre-requisites: coding and non-coding RNAs. | Zanichelli Chapter 1 |
| 5 | Pre-requisites of probability and statistics for bioinformatics. | Zanichelli Chapter 3 |
| 6 | R programming language | Materials provided by the lecturer |
| 7 | Pairwise and multiple alignment. | Zanichelli Chapters 5 and 6 |
| 8 | General biological databases. | Materials provided by the lecturer |
| 9 | Biological databases for medicine. | Materials provided by the lecturer |
| 10 | Tools for transcriptome analysis (Microarray and NGS). | Zanichelli Chapters 7 and 10 |
| 11 | Tools for searching and evaluating biomarkers. | Materials provided by the lecturer |
| 12 | DNA sequencing. | Zanichelli Chapter 8 |
| 13 | R package Bioconductor and differential analysis in R language. | Materials provided by the lecturer |
| 14 | Introduction to biological networks. | Materials provided by the lecturer |
| 15 | Network biomarkers and pathway analysis. | Materials provided by the lecturer |
| 16 | Biological network alignment. | Materials provided by the lecturer |
Learning Assessment
Learning Assessment Procedures
Written exam includes theoretical questions on the topics covered by the course.
The minimum grade to successfully pass the written exam is 16. Students that do not pass the written exam cannot attend the oral examination. Written exam can be examined together with the professor before the oral examination.
The project must be completed within 6 months since passing the written exam.
Unless otherwise communicated, written exam is held at 9 AM.
Notes:
- Usage of any hardware instrument (calculators, tablets, smartphones, cell phones, BT earphones, etc.), books or personal documents during the written exam is forbidden;
- To attend the exam, the student must reserve for the exam by using the proper module on the CEA student portal;
- Late reservations by email are not admitted. If reservation is missing, the final exam cannot be verbalized;
- Learning assessment may also be carried out on line, should the conditions require it.