First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
MOLECULAR BIOLOGY
Course unit
OMIC DATASETS: EXPERIMENTAL DESIGN AND ANALYSIS
SCP9088298, A.A. 2019/20

Information concerning the students who enrolled in A.Y. 2017/18

Information on the course unit
Degree course First cycle degree in
MOLECULAR BIOLOGY
SC1166, Degree course structure A.Y. 2015/16, A.Y. 2019/20
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Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination OMIC DATASETS: EXPERIMENTAL DESIGN AND ANALYSIS
Website of the academic structure http://biologiamolecolare.scienze.unipd.it/2019/laurea
Department of reference Department of Biology
Mandatory attendance
Language of instruction Italian
Branch PADOVA
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Lecturers
Teacher in charge CHIARA ROMUALDI BIO/11
Other lecturers GABRIELE SALES BIO/18

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Educational activities in elective or integrative disciplines BIO/11 Molecular Biology 3.0
Educational activities in elective or integrative disciplines BIO/18 Genetics 3.0

Course unit organization
Period Second semester
Year 3rd Year
Teaching method frontal

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Practice 2.0 32 18.0 No turn
Lecture 4.0 32 68.0 No turn

Calendar
Start of activities 02/03/2020
End of activities 12/06/2020
Show course schedule 2019/20 Reg.2015 course timetable

Syllabus
Prerequisites: Bioinformatics fundamentals (courses “Informatica e Bioinformatica” and “Bioinformatica II). Solid knowledge of molecular biology, genetics and biochemistry.
Target skills and knowledge: The course introduces theoretical and practical methods for the analysis of sequencing data deriving from last generation technologies. It offers an overview of the most popular techniques used for managing and analyzing genomic and transcriptomic datasets. Students will develop critical skills and scientific independence in applying bioinformatical tools for these tasks.
Examination methods: The evaluation of the acquired knowledge will be based on a written exam based on open questions. The final evaluation will also keep into account the activity of the student in the analysis of a case study and the preparation of a report on his findings. This will gauge the establishment of the proper knowledge, the scientific lexicon, the ability to discuss critically and to summarize the topics discussed in the lectures.
Assessment criteria: The following points will be evaluated:
1) Comprehension of the topics described in the lectures
2) Ability to apply and generalize analysis methods to case studies
3) Ability to summarize the topics and to use the appropriate scientific lexicon
4) Critical skills in the interpretation of analysis results
Course unit contents: 1. The statistical programming system R
- Basic commands and the interface to the operating system
- Package installation; using BioConductor
- The graphical environment RStudio
- Using notebooks for analysis and reporting
2. Sequence alignment for NGS
- Burrows–Weeler transform, genome indexing
- Quality control
- Expression quantification (RSEM, Salmon)
3. Gene expression analysis
- Data normalization
- Differential expression tests
- Functional analysis for the interpretation of results
4. DNA variant analysis
- Quality filters
- Statistical tests
5. Biological networks
- Pathway databases
- Graphical visualization
- Tools for the statistical analysis
Planned learning activities and teaching methods: The course is subdivided into frontal lectures and practical classes in computer laboratories. Teaching is interactive, favoring QA sessions to present case studies with the objective to promote open discussion and critical thinking. Practical classes, in particular, will cover real life examples of bioinformatic analyses and will offer to students the opportunity to present and compare their results.
Additional notes about suggested reading: Slides for frontal lectures and details about practical laboratories will be available on the teacher websites and on the e learning platform.
Textbooks (and optional supplementary readings)

Innovative teaching methods: Teaching and learning strategies
  • Case study
  • Interactive lecturing
  • Working in group
  • Active quizzes for Concept Verification Tests and class discussions
  • Use of online videos
  • Students peer review

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)

Sustainable Development Goals (SDGs)
Good Health and Well-Being Life on Land