| 課程與核心能力關聯配比(%) |
課程目標之教學方法與評量方法 |
| 課程目標 |
核心能力 |
配比(%) |
教學方法 |
評量方法 |
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 |
| 1.具備農作物整合性健康管理專業知能 |
| 2.具備循環經濟與永續管理專業知能 |
| 3.具備科學研究、團隊合作與資訊推廣能力 |
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| 授課內容(單元名稱與內容、習作/每週授課、考試進度-共16週加自主學習) |
| 週次 |
授課內容 |
| 第1週 |
Introduction and course brief |
| 第2週 |
R program: Introduction and basic function |
| 第3週 |
R program: Data analysis and graphics |
| 第4週 |
Web tools: Galaxy bioinformatic |
| 第5週 |
Transcriptome analysis: RNAseq raw data, cleaning, and transcriptome assemble |
| 第6週 |
Transcriptome analysis: differentially expressed genes (DEGs) |
| 第7週 |
Transcriptome analysis: weighted gene correlation network analysis |
| 第8週 |
Transcriptome analysis: GO and KEGG pathway analysis |
| 第9週 |
Midterm |
| 第10週 |
Image analysis: ImageJ introduction, counting colonies |
| 第11週 |
Image analysis: Disease symptom analysis, machine learning concept |
| 第12週 |
Web tools: NCBI, ENSEMBL, TAIR |
| 第13週 |
Web tools: PlantGDB, Phytozome |
| 第14週 |
Web tools: benchling |
| 第15週 |
Genome analysis: Genome annotation and genome comparisons |
| 第16週 |
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. |
自主學習
內容 |
03.製作專題報告
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| 學習評量方式 |
Group discussion participation (30%)
Presentation (20%)
Assignment of practices (50%) |
| 教科書＆參考書目(書名、作者、書局、代理商、說明) |
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). |
| 課程教材（教師個人網址請列在本校內之網址） |
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. |
| 課程輔導時間 |
| Friday from 13:00 to 15:00 |