| Relevance of Course Objectives and Core Learning Outcomes(%) |
Teaching and Assessment Methods for Course Objectives |
| Course Objectives |
Competency Indicators |
Ratio(%) |
Teaching Methods |
Assessment Methods |
1. Introduction of the concepts of bioinformatics
2. Understanding the program we use in class (Online resource and R program)
3. Cases studies in bioinformatics |
|
|
| topic Discussion/Production |
| Exercises |
|
|
| Course Content and Homework/Schedule/Tests Schedule |
| Week |
Course Content |
| Week 1 |
Introduction and course brief |
| Week 2 |
R program: Introduction and basic function |
| Week 3 |
R program: Data analysis and graphics |
| Week 4 |
Web tools: Galaxy bioinformatic |
| Week 5 |
Transcriptome analysis: RNAseq raw data, cleaning, and transcriptome assemble |
| Week 6 |
Transcriptome analysis: differentially expressed genes (DEGs) |
| Week 7 |
Transcriptome analysis: weighted gene correlation network analysis |
| Week 8 |
Transcriptome analysis: GO and KEGG pathway analysis |
| Week 9 |
Midterm |
| Week 10 |
Image analysis: ImageJ introduction, counting colonies |
| Week 11 |
Image analysis: Disease symptom analysis, machine learning concept |
| Week 12 |
Web tools: NCBI, ENSEMBL, TAIR |
| Week 13 |
Web tools: PlantGDB, Phytozome |
| Week 14 |
Web tools: benchling |
| Week 15 |
Genome analysis: Genome annotation and genome comparisons |
| Week 16 |
Web tools: Protein data bank
Protein visulisation: PyMOL
Final remarks
This course will ask students to finish a project related to the plant-microbe interaction transcriptome project. |
self-directed
learning |
03.Preparing presentations or reports related to industry and academia.
|
|
| Evaluation |
Group discussion participation (30%)
Presentation (20%)
Assignment of practices (50%) |
| Textbook & other References |
Khan, A., Singh, S., and Singh, V. K. (2021). “Bioinformatics in Plant Pathology,” in Emerging Trends in Plant Pathology, eds. K. P. Singh, S. Jahagirdar, and B. K. Sarma (Singapore: Springer), 725–844. doi: 10.1007/978-981-15-6275-4_32.
Kabacoff, R.I. (2015). R in action: data analysis and graphics 2nd Ed. (USA: Manning publication). |
| Teaching Aids & Teacher's Website |
1. A comprehensive flowchart showing the step-by-step process from raw sequencing data to analysed results helps students visualise the entire bioinformatics workflow.
2. A pre-loaded virtual machine image with all required bioinformatics tools and sample datasets allows students to start practising immediately without software installation issues.
3. A collection of real plant pathology case studies with accompanying datasets gives students hands-on experience with research problems and their solutions.
4. An interactive command-line cheat sheet listing essential bioinformatics commands and their usage, serving as a quick reference during practical sessions. |
| Office Hours |
| Friday from 13:00 to 15:00 |
| Sustainable Development Goals, SDGs(Link URL) |
| 04.Quality Education 09.Industry, Innovation and Infrastructure | include experience courses：N |
|