Bioinformatic applications for high-throughput phenomics and genomics data analysis
| Title | ΒΙΟΠΛΗΡΟΦΟΡΙΚΗ ΕΠΕΞΕΡΓΑΣΙΑ ΔΕΔΟΜΕΝΩΝ ΜΕΓΑΛΗΣ ΚΛΙΜΑΚΑΣ ΑΠΟ ΦΑΙΝΟΤΥΠΙΚΗ ΚΑΙ ΓΕΝΟΤΥΠΙΚΗ ΑΝΑΛΥΣΗ / Bioinformatic applications for high-throughput phenomics and genomics data analysis |
| Code | GPP110 |
| Faculty | Agriculture, Forestry and Natural Environment |
| School | Agriculture |
| Cycle / Level | 2nd / Postgraduate, 3rd / Doctorate |
| Teaching Period | Winter |
| Coordinator | Alexios Polidoros |
| Common | Yes |
| Status | Active |
| Course ID | 600017391 |
Programme of Study: Prógramma metaptychiakṓn Spoudṓn Genetikī, Veltíōsī fytṓn kai Paragōgī Pollaplasiastikoý Ylikoý
Registered students: 8
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS - Ypochreōtiká & Epilogīs | Elective Courses | 3 | 2 | 7 |
| Academic Year | 2023 – 2024 |
| Class Period | Winter |
| Faculty Instructors |
|
| Instructors from Other Categories |
|
| Weekly Hours | 4 |
| Total Hours | 52 |
| Class ID | 600239613
|
Type Of Offer
- Disciplinary Course
Course Type 2021
Specific Foundation
Course Type 2016-2020
- Scientific Area
Course Type 2011-2015
Knowledge Deepening / Consolidation
Mode of Delivery
- Face to face
- Distance learning
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600239613
- eLearning: https://elearning.auth.gr/course/view.php?id=12521
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
- Greek (Instruction, Examination)
- English (Examination)
Learning Outcomes
Upon successful completion of the course, students will:
1) know the largest databases of genetic information (genome, proteome, biochemical pathways, microbiome, etc.) and ways to access and extract information.
2) are familiar with the use of genome-wide, microbiome- and transcriptome-level sequencing software;
3) they have learned what imaging sensors for phenotypic data acquisition and their ground and aerial transportation platforms are and how they work.
4) they will know imaging and analysis techniques of plant phenotypic parameters
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Adapt to new situations
- Make decisions
- Work autonomously
- Work in teams
- Work in an international context
- Work in an interdisciplinary team
- Generate new research ideas
- Design and manage projects
- Appreciate diversity and multiculturality
- Respect natural environment
- Demonstrate social, professional and ethical commitment and sensitivity to gender issues
- Be critical and self-critical
- Advance free, creative and causative thinking
Course Content (Syllabus)
Collect and analyze data from sensor systems to automatically detect and map threats (weeds, fungi, viruses and insects) to crops, as well as detect, identify and map crop stresses. Measurement of production in orchards and large crops using new technologies (RTK-GPS, zigbee, ambient computing). Product and work traceability systems in the field using new technologies (RFID, barcode, GPS, zigbee, wearable computers, etc.). Imaging sensors for receiving phenotypic data (visible, infrared, hyperspectral, fluorescent, thermal, LIDAR). Ground and aerial platforms to transport the sensors (phenotypic mapping platforms, UAVs). Techniques for imaging plant phenotypic parameters (vegetation indices, leaf area index, plant cover, plant height, stress from biotic and abiotic factors, biomass, production). Image processing for parameter estimation. Collection and analysis of genome sequencing data. Access and acquisition of data from sequence databases. Sequencing data evaluation, sequencing software and platforms. Annotation, gene ontology (Gene Ontology), connection to databases of metabolic pathways in sequences obtained by massively parallel next generation sequencing (Next Generation Sequencing). Analysis of environmental microbiomes. Transcriptome, proteome, metabolome analysis software.
Keywords
automations, sensors, imaging methods, gene ontology, sequencing, genome, proteome, metabolome
Educational Material Types
- Notes
- Slide presentations
- Multimedia
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Laboratory Teaching
- Use of ICT in Communication with Students
- Use of ICT in Student Assessment
Description
Teaching is done using appropriate database access and analysis software, sequence analysis, image and spectral data analysis
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 70 | ✓ | ✓ | ||
| Laboratory Work | 30 | ✓ | ✓ | ||
| Tutorial | 26 | ✓ | ✓ | ||
| Interactive Teaching in Information Center | 52 | ✓ | ✓ | ||
| Exams | 2 | ✓ | |||
| Total | 180 |
Student Assessment
Description
Written exams, use of software for data analysis
Student Assessment methods
- Written Exam with Multiple Choice Questions (Formative, Summative)
- Written Exam with Short Answer Questions (Formative, Summative)
- Written Exam with Extended Answer Questions (Formative, Summative)
- Written Exam with Problem Solving (Formative)
Bibliography
Additional bibliography for study
DOI: 10.25165/j.ijabe.20181102.2696
Sensors 2014, 14, 20078-20111; doi:10.3390/s141120078
Computers and Electronics in Agriculture, http://dx.doi.org/10.1016/j.compag.2015.10.011
Last Update
28-02-2024
