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
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topic Discussion/Production |
Exercises |
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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 |
Week 17 |
Protein visulisation: PyMOL |
Week 18 |
Final remarks |
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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 |
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Office Hours |
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Sustainable Development Goals, SDGs |
  | include experience courses:N |
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