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Master in

Plant genetics, genomics and breeding

Next edition: 1st part: September 2023 – June 2024 / 2nd part: September 2024 – June 2025

Master in

Plant genetics, genomics and breeding

General information on the Unit

Contact hours: 30 (18 lectures, 12 practicals)
Personal work hours: 45
Character: Compulsory
Venue: Mediterranean Agronomic Institute of Zaragoza (CIHEAM Zaragoza)
- Developed during the first academic year of the Master, in the middle of the first semester.
- The assessment of this Unit consists in a written exam and the assessment of problem and exercise solving during the first semester.
Requisites and permanence
Knowledge on basic genetics, statistics and molecular biology is required.
Learning methods
Combination of theoretical and practical sessions consisting of lectures, computer work, and individual study and work.
Lecturers deliver the topics in English. The documents supplied by the lecturers is also written in English.


Presentation of the Unit and context within the syllabus

The Unit complements the previous one, deepening the study of genomic structure and function using current and cutting-edge methodologies.

The Unit first presents the bases and technologies of plant genome sequencing and assembly that allow the characterization and location of genes in the genome. The advantages of using pangenomes to improve the identification of variants from sequence data and genes associated with key traits are then presented. Subsequently, the Unit deals with how transcriptomics helps to measure gene expression and to discover relativeness among genotypes. Finally, the use of other omic technologies is introduced.



Specific competences

  • SC1 Understanding the structure and functions of genomes and applying the technologies involved in their determination.
  • SC2 Awareness of the utility of using omics technology to determine gene functions and products.

General competences

  • GC1 Integrating scientific and technical knowledge and applying it discerningly.
  • GC2 Performing scientific and/or technical information searches and processing them selectively.
  • GC3 Analysing results or strategies and elaborating conclusions, which contribute to clarify the problems and to find possible solutions.
  • GC4 Learning and working autonomously, responding to unforeseen situations and re-aiming a strategy if necessary.
  • GC5 Writing presentations and synthesis, preparing and presenting oral communications, and defending them in public.


Learning outcomes

The student, at the end of the learning of this Unit:

  • Is familiar with the methods and software tools to sequence and assemble genomes.
  • Understands the bases of structural genomics to know the chromosome structure and sequence, and of functional genomics to know gene functions and expression.
  • Is aware of the usefulness of using reference genomes for genome assembly.
  • Knows the advantages of utilize pangenomics to better identify the diversity and composition of the global gene range, especially when dealing with high intraspecific variability.
  • Masters the applications of transcriptomics to find patters in expression data.
  • Has a view of the applications of other omic technologies to determine the biological functions of gene products.



  • Plant genome sequencing
  • Genome structure
  • Reference genomes
  • Pangenomes
  • Transcriptomics and functional genomics

Learning activities

Learning activity 1: Lectures combined with applied examples
Hours: 51
Percentage of contact: 35%

Learning activity 2: Tutored individual work
(a) Understanding basic Linux commands
(b) Performing a de novo genome assembly
(c) Performing a reference-guided genome assembly
(d) Comparing genome assemblies of two rice cultivars. After downloading Nipponbare and IR64 genome assemblies, students have to run a whole genome alignment, plot the alignment, extract PLA1 sequences from the genomes and align them
(e) Barley genebank genomics portal
(f) RNA-seq data analysis. Students have to download determined RNA-seq data and the wheat gene sequences, then align RNAseq data to genes and run differential gene expression analysis with 3D-RNAseq
Data and necessary software were installed in a virtual machine. Students, guided by the corresponding tutor, work individually on the exercises proposed. Thereafter, students have to solve a similar exercise as homework and present the results to the tutors.
Hours: 12 (a, b and c); 12 (d, e and f)
Percentage of contact: 50%


Assessment methods

Assessment system 1: Written exams, composed of questions provided by different lecturers of the Unit.
The questions are multiple choice. The exam assesses the content of lectures.
Weighting: 66% of the final score of the Unit

Assessment system 2: Assessment by the tutors of the practical work related to learning activities 2 of the solved exercises submitted by each student. These exercises are similar to those performed during the tutored individual work. Understanding of the methodology and quality of the results will be assessed.
Weighting: 34% of the final score of the Unit



Jordi GARCIA-MAS, CRAG, Barcelona (Spain)
Martin MASCHER, IPK Gatersleben, Seeland (Germany)
Adreu PAYTUVÍ, Sequentia Biotech, Barcelona (Spain)