Bioinformatics for Horticultural Sciences
A.Y. 2023/2024
Learning objectives
The course will provide knowledge on the application of complex data analysis techniques in all plant sectors of agricultural interest.
In particular, it will cover aspects foreseen by the Plant Science Research Network (PSRN) which has formulated a ten-year strategic plan on the information infrastructure, big data challenges and training necessary to advance plant systems science.
In particular, it will cover aspects foreseen by the Plant Science Research Network (PSRN) which has formulated a ten-year strategic plan on the information infrastructure, big data challenges and training necessary to advance plant systems science.
Expected learning outcomes
The student will be able to set up an experiment and apply complex data analysis techniques in experimentation and research applied to tree species and agricultural species.
Lesson period: First semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
First semester
Course syllabus
- Review of research methods and technologies and use of molecular markers.
- Practical examples of application of the tools described above in species of agricultural interest.
- The main software for the analysis of gene expression on a large scale.
- Methods of bioinformatic analysis of genomes.
- Principles and methods of metagenomic analysis.
- Introduction to the "R" scripting language and to the main packages of interest for bioinformatics applications.
- Overview of the Python programming language with practical examples.
- Fundamentals of database structure and management and analysis of some cases of agricultural interest.
- Methods of analysis of non-coding RNAs (search and analysis of expression).
- The main software for creating genetic maps and integrating with genomic projects.
- High-performance computing infrastructures (HPC) and examples of use of the INDACO university platform.
- Practical examples of application of the tools described above in species of agricultural interest.
- The main software for the analysis of gene expression on a large scale.
- Methods of bioinformatic analysis of genomes.
- Principles and methods of metagenomic analysis.
- Introduction to the "R" scripting language and to the main packages of interest for bioinformatics applications.
- Overview of the Python programming language with practical examples.
- Fundamentals of database structure and management and analysis of some cases of agricultural interest.
- Methods of analysis of non-coding RNAs (search and analysis of expression).
- The main software for creating genetic maps and integrating with genomic projects.
- High-performance computing infrastructures (HPC) and examples of use of the INDACO university platform.
Prerequisites for admission
- Students should already have knowledge of genetics, biochemistry, and molecular biology.
Teaching methods
- The course includes lectures and computer exercises.
Teaching Resources
- During the course, consultation texts, original articles, multimedia material and the PDF files containing the material illustrated in class and the subject of the practical exercises updated every year are suggested, which the student can download from the teacher's personal websites:
https://fgeunammmg.ariel.ctu.unimi.it/
https://fgeunammmg.ariel.ctu.unimi.it/
Assessment methods and Criteria
- The exam consists of a written test which may include the solution of applicative exercises, similar to those addressed in the classroom and the discussion of concepts covered in the course.
AGR/03 - ARBORICULTURE AND FRUITCULTURE - University credits: 6
Practicals: 32 hours
Lessons: 32 hours
Lessons: 32 hours
Professor:
Geuna Filippo
Professor(s)
Reception:
Free upon request by telephone or email
Hover the mouse pointer on "DISAA Sez. Coltivazioni Arboree" at this page: https://www.unimi.it/sites/default/files/2019-01/SAAA_mappa_facolta.pdf