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master degree course in COMPUTER SCIENCE

Course year 1
Teaching units Unit Bioinformatica
Related or Additional Studies (lesson)
  • TAF: Supplementary compulsory subjects SSD: ING-IND/34 CFU: 6
Teachers: Silvio BICCIATO
Moodle portal
Exam type oral
Evaluation final vote
Teaching language Italiano
Contents download pdf download




The course aims to provide the knowledge and skills necessary for the computational analysis and of data related to biological macromolecules. These activities are preparatory to deepen in more specialized and professionalizing biological disciplinary sectors. The class also introduces students to the application of algorithms and IT tools essential to analyze and understand the quantitative aspects related to genomic sequences.
For a more complete understanding of the training objectives, please refer to the learning outcomes expected following the completion of this training course.

Admission requirements

The class requires basic knowledge of Mathematics, Computer Science and Statistics applied to biology, organic chemistry, cell biology, biochemistry, molecular biology, genetics. Students should be able to understand the principles for describing and solving a problem through mathematical formalism.

Course contents

Contents and CFUs are purely indicative. Both might indeed change during teaching in light of the feedback from students.

1 CFU (8 hours):
- Introduction to Bioinformatics: the use of bioinformatics in genomic research
- Biological databases
- Structure of biological databases: analysis of EMBL and GenBank flat files
- Browsers for bioinformatics and automatic database query systems

1.5 CFU (12 hours):
- Concepts and principles for the analysis of genomic sequences: strings and algorithms
- Methods for searching for motifs in genomic sequences: prediction of Open Reading Frames
- Methods for searching for motifs in genomic sequences: prediction of binding motifs
- Sequence pattern display: sequence logo representation

1.5 CFU (12 hours):
- The alignment of sequences: concepts and principles
- Algorithms for optimal global alignment
- Algorithms for excellent local alignment
- Definition of scoring schemes for alignments: substitution matrices

1.5 CFU (12 hours):
- Concepts and principles of heuristic methods of alignment
- Alignment heuristic methods: FASTA
- Alignment heuristic methods: BLAST

0.5 CFU (4 hours):
- The USCS Genome Browser

Teaching methods

Teaching is given through face-to-face lectures (theory and application examples) which are carried out with the aid of audiovisual media (Power Point presentations) and practical exercises in the use of computational tools. Attendance to face-to-face lectures (theory, application examples and exercises) is not compulsory. The course language is Italian.

Assessment methods

The exam will take place at the end of the course according to the official exam schedule. The test is oral. The duration of the interview is about 15 minutes during which the student is asked to illustrate two topics related to sequence alignment and genome analysis. The interview is aimed at evaluating: - knowledge and understanding skills; - the application of knowledge and understanding; - autonomy of judgment. The evaluation parameters consist of: - the application of knowledge and understanding (80%); - autonomy of judgment (15%); - communication skills (5%). The final grade of the exam will be communicated at the end of the interview; the verbalization will take place through Esse3.

Learning outcomes

1) Knowledge and understanding
at the end of the course, it is hoped that the student will be able to:
a) apply mathematical, statistical and computer methods to the analysis of the sequences of biological macromolecules;
b) use computational methods of genomic and post-genomic analysis.
2) Ability to apply
a) logical-mathematical, statistical, computer and physical tools;
b) bioinformatics knowledge;
c) methods for consulting national and international documents on bioethical aspects, bibliographic material, databases.
3) Autonomy of judgment
a) plan and interpret data for laboratory testing and safety;
b) reorganize the knowledge learned and implement one's own capacity for critical and autonomous evaluation of what has been learned.
4) Communication skills
a) express their knowledge correctly and logically, recognizing the required topic and responding in a timely and complete manner to the exam questions.
b) communicate in Italian and foreign (English) written and / or oral with appropriate scientific language.
5) Learning skills
a) develop autonomy in study and laboratory work;
b) know how to consult bibliographic material and databases and other resources of the network;
c) to learn basic cognitive tools for the continuous updating of knowledge.


Un testo a scelta fra:
- J. Pevsner, Bioinformatics and functional genomics, Wiley-Blackwell, 2009
- J. Setubal, J. Meidanis, Introduction to computational molecular biology, PWS Publishing Company, 1997
- M. Helmer Citterich, F. Ferre’, G. Pavesi, G. Pesole, C. Romualdi Fondamenti di bioinformatica, Zanichelli, 2018

Sulla pagina del Portale MOODLE relativa all’insegnamento di Bioinformatica sono disponibili già all'inizio del corso (e nel rispetto dei diritti d’autore).
Le dispense utilizzate dal docente nel corso delle lezioni frontali.