BIOTECHNOLOGY FOR THE GENETIC IMPROVEMENT OF TREE SPECIES

Module BIOINFORMATICS TOOLS FOR THE ANALYSIS OF AGRONOMIC TRAITS

Provide an in-depth knowledge of the main bioinformatic approaches for the analysis of genomic and transcriptomic data in the fruit production sector. In particular, through lectures and practical exercises, the course will cover the main methods for population genetics analysis, the identification of regions of interest within a genome (e.g., candidate genes), the alignment of reads to a reference genome, and the in silico identification of molecular markers.
In detail, with reference to the so-called “Dublin Descriptors,” the course aims to provide the following knowledge and skills.

Knowledge and understanding abilities:
The student will acquire specific knowledge of bioinformatics applied to the genetic improvement of fruit species.

Applying knowledge and understanding abilities:
The student will be able to analyze high-throughput genomic data using bioinformatic software and/or pipelines, with practical examples related to Mediterranean fruit species.

Making judgements:
The knowledge and skills acquired during the course, particularly through independent study of specific topics and critical analysis of the literature, will enable the student to develop autonomy of judgement and the ability to study and work independently.

Communication skills:
Lectures, additional activities, and individual study of selected teaching materials will allow the student to acquire the foundations of technical language, including in English. The student will be encouraged to use appropriate technical terminology in class discussions as well as during other activities. Mastery of technical language will contribute to the final assessment.

Learning skills:
During the course, activities involving the collection and analysis of scientific literature and the preparation of scientific reports are intended to prepare the student for independent work and critical analysis of the literature. These skills are useful for writing the final thesis, for preparing a strong research proposal for admission to a PhD program, and for the continuous updating of knowledge and abilities required in the profession.

The course (3 ECTS) includes 7 hours of lectures and 14 hours of other activities. For lectures, the instructor will make use of PowerPoint presentations, also in English. As part of the other activities, computer-based exercises will be carried out in the form of a mini-course on some main topics (e.g., population genetics, identification of polymorphisms in genomic data), as well as seminars (including online).

In order to guarantee equal opportunities and in compliance with current legislation, interested students may request a personal meeting to plan any compensatory and/or dispensatory measures, based on the learning objectives and specific needs. It is also possible to contact the CInAP (Center for Active and Participatory Inclusion – Services for Disabilities and/or Specific Learning Disorders) representative of our Department.

Knowledge of plant genetics principles and biomolecular methodologies is recommended

Attendance is not compulsory but strongly recommended

Introduction to Bioinformatics

Population Genetics

• PCA

• Structure

• Admixture analysis

• Phylogenetic trees

Sequencing Techniques

Genome Assembly

• Evaluation of assembly quality: metrics, coverage analysis, and BUSCO

Transcriptomics

• RNA-Seq analysis

In silico Identification of Polymorphisms

• SNPs

• Structural Variants

• INDELs

• SSRs

Leraning material provided by the teacher [2]

In particular, the relevance of the answers to the questions posed, the quality of the content, the ability to connect with other topics covered in the program, the ability to provide examples, the use of correct technical language, and the student’s overall expressive ability will be evaluated.

Learning assessment takes place through an oral examination. The evaluation of the student’s preparation will be based on the following criteria: learning ability and level of depth of the topics covered, ability to synthesize and present, and reasoning skills.

The grading follows the scheme below:

Fail (Not eligible)

• Knowledge and understanding of the subject: Major gaps. Significant inaccuracies.

• Analysis and synthesis skills: Negligible. Frequent generalizations. Inability to synthesize.

• Use of references: Completely inappropriate.

18–20

• Knowledge and understanding of the subject: At threshold level. Clear imperfections.

• Analysis and synthesis skills: Barely sufficient abilities.

• Use of references: Barely appropriate.

21–23

• Knowledge and understanding of the subject: Routine knowledge.

• Analysis and synthesis skills: Able to analyze and synthesize correctly. Arguments are logical and coherent.

• Use of references: Uses standard references.

24–26

• Knowledge and understanding of the subject: Good knowledge.

• Analysis and synthesis skills: Good ability to analyze and synthesize. Arguments are expressed coherently.

• Use of references: Uses standard references.

27–29

• Knowledge and understanding of the subject: More than good knowledge.

• Analysis and synthesis skills: Strong ability to analyze and synthesize.

• Use of references: Topics studied in greater depth.

30–30L (cum laude)

• Knowledge and understanding of the subject: Excellent knowledge.

• Analysis and synthesis skills: Strong ability to analyze and synthesize.

• Use of references: Significant in-depth study.

The candidate should illustrate the logical steps for genome assembly.

The candidate should illustrate the main techniques for studying genetic variability within a germplasm collection.

The candidate should describe the characteristics of a Variant Calling Format (VCF) file.